Audio Classification.html

<!DOCTYPE html>
<html>
<head><meta charset="utf-8" />
<title>Audio Classification</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>

<style type="text/css">
    /*!
*
* Twitter Bootstrap
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 * Bootstrap v3.3.7 (http://getbootstrap.com)
 * Copyright 2011-2016 Twitter, Inc.
 * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
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/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */
html {
  font-family: sans-serif;
  -ms-text-size-adjust: 100%;
  -webkit-text-size-adjust: 100%;
}
body {
  margin: 0;
}
article,
aside,
details,
figcaption,
figure,
footer,
header,
hgroup,
main,
menu,
nav,
section,
summary {
  display: block;
}
audio,
canvas,
progress,
video {
  display: inline-block;
  vertical-align: baseline;
}
audio:not([controls]) {
  display: none;
  height: 0;
}
[hidden],
template {
  display: none;
}
a {
  background-color: transparent;
}
a:active,
a:hover {
  outline: 0;
}
abbr[title] {
  border-bottom: 1px dotted;
}
b,
strong {
  font-weight: bold;
}
dfn {
  font-style: italic;
}
h1 {
  font-size: 2em;
  margin: 0.67em 0;
}
mark {
  background: #ff0;
  color: #000;
}
small {
  font-size: 80%;
}
sub,
sup {
  font-size: 75%;
  line-height: 0;
  position: relative;
  vertical-align: baseline;
}
sup {
  top: -0.5em;
}
sub {
  bottom: -0.25em;
}
img {
  border: 0;
}
svg:not(:root) {
  overflow: hidden;
}
figure {
  margin: 1em 40px;
}
hr {
  box-sizing: content-box;
  height: 0;
}
pre {
  overflow: auto;
}
code,
kbd,
pre,
samp {
  font-family: monospace, monospace;
  font-size: 1em;
}
button,
input,
optgroup,
select,
textarea {
  color: inherit;
  font: inherit;
  margin: 0;
}
button {
  overflow: visible;
}
button,
select {
  text-transform: none;
}
button,
html input[type="button"],
input[type="reset"],
input[type="submit"] {
  -webkit-appearance: button;
  cursor: pointer;
}
button[disabled],
html input[disabled] {
  cursor: default;
}
button::-moz-focus-inner,
input::-moz-focus-inner {
  border: 0;
  padding: 0;
}
input {
  line-height: normal;
}
input[type="checkbox"],
input[type="radio"] {
  box-sizing: border-box;
  padding: 0;
}
input[type="number"]::-webkit-inner-spin-button,
input[type="number"]::-webkit-outer-spin-button {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: textfield;
  box-sizing: content-box;
}
input[type="search"]::-webkit-search-cancel-button,
input[type="search"]::-webkit-search-decoration {
  -webkit-appearance: none;
}
fieldset {
  border: 1px solid #c0c0c0;
  margin: 0 2px;
  padding: 0.35em 0.625em 0.75em;
}
legend {
  border: 0;
  padding: 0;
}
textarea {
  overflow: auto;
}
optgroup {
  font-weight: bold;
}
table {
  border-collapse: collapse;
  border-spacing: 0;
}
td,
th {
  padding: 0;
}
/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */
@media print {
  *,
  *:before,
  *:after {
    background: transparent !important;
    color: #000 !important;
    box-shadow: none !important;
    text-shadow: none !important;
  }
  a,
  a:visited {
    text-decoration: underline;
  }
  a[href]:after {
    content: " (" attr(href) ")";
  }
  abbr[title]:after {
    content: " (" attr(title) ")";
  }
  a[href^="#"]:after,
  a[href^="javascript:"]:after {
    content: "";
  }
  pre,
  blockquote {
    border: 1px solid #999;
    page-break-inside: avoid;
  }
  thead {
    display: table-header-group;
  }
  tr,
  img {
    page-break-inside: avoid;
  }
  img {
    max-width: 100% !important;
  }
  p,
  h2,
  h3 {
    orphans: 3;
    widows: 3;
  }
  h2,
  h3 {
    page-break-after: avoid;
  }
  .navbar {
    display: none;
  }
  .btn > .caret,
  .dropup > .btn > .caret {
    border-top-color: #000 !important;
  }
  .label {
    border: 1px solid #000;
  }
  .table {
    border-collapse: collapse !important;
  }
  .table td,
  .table th {
    background-color: #fff !important;
  }
  .table-bordered th,
  .table-bordered td {
    border: 1px solid #ddd !important;
  }
}
@font-face {
  font-family: 'Glyphicons Halflings';
  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot');
  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff2') format('woff2'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff') format('woff'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular') format('svg');
}
.glyphicon {
  position: relative;
  top: 1px;
  display: inline-block;
  font-family: 'Glyphicons Halflings';
  font-style: normal;
  font-weight: normal;
  line-height: 1;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
.glyphicon-asterisk:before {
  content: "\002a";
}
.glyphicon-plus:before {
  content: "\002b";
}
.glyphicon-euro:before,
.glyphicon-eur:before {
  content: "\20ac";
}
.glyphicon-minus:before {
  content: "\2212";
}
.glyphicon-cloud:before {
  content: "\2601";
}
.glyphicon-envelope:before {
  content: "\2709";
}
.glyphicon-pencil:before {
  content: "\270f";
}
.glyphicon-glass:before {
  content: "\e001";
}
.glyphicon-music:before {
  content: "\e002";
}
.glyphicon-search:before {
  content: "\e003";
}
.glyphicon-heart:before {
  content: "\e005";
}
.glyphicon-star:before {
  content: "\e006";
}
.glyphicon-star-empty:before {
  content: "\e007";
}
.glyphicon-user:before {
  content: "\e008";
}
.glyphicon-film:before {
  content: "\e009";
}
.glyphicon-th-large:before {
  content: "\e010";
}
.glyphicon-th:before {
  content: "\e011";
}
.glyphicon-th-list:before {
  content: "\e012";
}
.glyphicon-ok:before {
  content: "\e013";
}
.glyphicon-remove:before {
  content: "\e014";
}
.glyphicon-zoom-in:before {
  content: "\e015";
}
.glyphicon-zoom-out:before {
  content: "\e016";
}
.glyphicon-off:before {
  content: "\e017";
}
.glyphicon-signal:before {
  content: "\e018";
}
.glyphicon-cog:before {
  content: "\e019";
}
.glyphicon-trash:before {
  content: "\e020";
}
.glyphicon-home:before {
  content: "\e021";
}
.glyphicon-file:before {
  content: "\e022";
}
.glyphicon-time:before {
  content: "\e023";
}
.glyphicon-road:before {
  content: "\e024";
}
.glyphicon-download-alt:before {
  content: "\e025";
}
.glyphicon-download:before {
  content: "\e026";
}
.glyphicon-upload:before {
  content: "\e027";
}
.glyphicon-inbox:before {
  content: "\e028";
}
.glyphicon-play-circle:before {
  content: "\e029";
}
.glyphicon-repeat:before {
  content: "\e030";
}
.glyphicon-refresh:before {
  content: "\e031";
}
.glyphicon-list-alt:before {
  content: "\e032";
}
.glyphicon-lock:before {
  content: "\e033";
}
.glyphicon-flag:before {
  content: "\e034";
}
.glyphicon-headphones:before {
  content: "\e035";
}
.glyphicon-volume-off:before {
  content: "\e036";
}
.glyphicon-volume-down:before {
  content: "\e037";
}
.glyphicon-volume-up:before {
  content: "\e038";
}
.glyphicon-qrcode:before {
  content: "\e039";
}
.glyphicon-barcode:before {
  content: "\e040";
}
.glyphicon-tag:before {
  content: "\e041";
}
.glyphicon-tags:before {
  content: "\e042";
}
.glyphicon-book:before {
  content: "\e043";
}
.glyphicon-bookmark:before {
  content: "\e044";
}
.glyphicon-print:before {
  content: "\e045";
}
.glyphicon-camera:before {
  content: "\e046";
}
.glyphicon-font:before {
  content: "\e047";
}
.glyphicon-bold:before {
  content: "\e048";
}
.glyphicon-italic:before {
  content: "\e049";
}
.glyphicon-text-height:before {
  content: "\e050";
}
.glyphicon-text-width:before {
  content: "\e051";
}
.glyphicon-align-left:before {
  content: "\e052";
}
.glyphicon-align-center:before {
  content: "\e053";
}
.glyphicon-align-right:before {
  content: "\e054";
}
.glyphicon-align-justify:before {
  content: "\e055";
}
.glyphicon-list:before {
  content: "\e056";
}
.glyphicon-indent-left:before {
  content: "\e057";
}
.glyphicon-indent-right:before {
  content: "\e058";
}
.glyphicon-facetime-video:before {
  content: "\e059";
}
.glyphicon-picture:before {
  content: "\e060";
}
.glyphicon-map-marker:before {
  content: "\e062";
}
.glyphicon-adjust:before {
  content: "\e063";
}
.glyphicon-tint:before {
  content: "\e064";
}
.glyphicon-edit:before {
  content: "\e065";
}
.glyphicon-share:before {
  content: "\e066";
}
.glyphicon-check:before {
  content: "\e067";
}
.glyphicon-move:before {
  content: "\e068";
}
.glyphicon-step-backward:before {
  content: "\e069";
}
.glyphicon-fast-backward:before {
  content: "\e070";
}
.glyphicon-backward:before {
  content: "\e071";
}
.glyphicon-play:before {
  content: "\e072";
}
.glyphicon-pause:before {
  content: "\e073";
}
.glyphicon-stop:before {
  content: "\e074";
}
.glyphicon-forward:before {
  content: "\e075";
}
.glyphicon-fast-forward:before {
  content: "\e076";
}
.glyphicon-step-forward:before {
  content: "\e077";
}
.glyphicon-eject:before {
  content: "\e078";
}
.glyphicon-chevron-left:before {
  content: "\e079";
}
.glyphicon-chevron-right:before {
  content: "\e080";
}
.glyphicon-plus-sign:before {
  content: "\e081";
}
.glyphicon-minus-sign:before {
  content: "\e082";
}
.glyphicon-remove-sign:before {
  content: "\e083";
}
.glyphicon-ok-sign:before {
  content: "\e084";
}
.glyphicon-question-sign:before {
  content: "\e085";
}
.glyphicon-info-sign:before {
  content: "\e086";
}
.glyphicon-screenshot:before {
  content: "\e087";
}
.glyphicon-remove-circle:before {
  content: "\e088";
}
.glyphicon-ok-circle:before {
  content: "\e089";
}
.glyphicon-ban-circle:before {
  content: "\e090";
}
.glyphicon-arrow-left:before {
  content: "\e091";
}
.glyphicon-arrow-right:before {
  content: "\e092";
}
.glyphicon-arrow-up:before {
  content: "\e093";
}
.glyphicon-arrow-down:before {
  content: "\e094";
}
.glyphicon-share-alt:before {
  content: "\e095";
}
.glyphicon-resize-full:before {
  content: "\e096";
}
.glyphicon-resize-small:before {
  content: "\e097";
}
.glyphicon-exclamation-sign:before {
  content: "\e101";
}
.glyphicon-gift:before {
  content: "\e102";
}
.glyphicon-leaf:before {
  content: "\e103";
}
.glyphicon-fire:before {
  content: "\e104";
}
.glyphicon-eye-open:before {
  content: "\e105";
}
.glyphicon-eye-close:before {
  content: "\e106";
}
.glyphicon-warning-sign:before {
  content: "\e107";
}
.glyphicon-plane:before {
  content: "\e108";
}
.glyphicon-calendar:before {
  content: "\e109";
}
.glyphicon-random:before {
  content: "\e110";
}
.glyphicon-comment:before {
  content: "\e111";
}
.glyphicon-magnet:before {
  content: "\e112";
}
.glyphicon-chevron-up:before {
  content: "\e113";
}
.glyphicon-chevron-down:before {
  content: "\e114";
}
.glyphicon-retweet:before {
  content: "\e115";
}
.glyphicon-shopping-cart:before {
  content: "\e116";
}
.glyphicon-folder-close:before {
  content: "\e117";
}
.glyphicon-folder-open:before {
  content: "\e118";
}
.glyphicon-resize-vertical:before {
  content: "\e119";
}
.glyphicon-resize-horizontal:before {
  content: "\e120";
}
.glyphicon-hdd:before {
  content: "\e121";
}
.glyphicon-bullhorn:before {
  content: "\e122";
}
.glyphicon-bell:before {
  content: "\e123";
}
.glyphicon-certificate:before {
  content: "\e124";
}
.glyphicon-thumbs-up:before {
  content: "\e125";
}
.glyphicon-thumbs-down:before {
  content: "\e126";
}
.glyphicon-hand-right:before {
  content: "\e127";
}
.glyphicon-hand-left:before {
  content: "\e128";
}
.glyphicon-hand-up:before {
  content: "\e129";
}
.glyphicon-hand-down:before {
  content: "\e130";
}
.glyphicon-circle-arrow-right:before {
  content: "\e131";
}
.glyphicon-circle-arrow-left:before {
  content: "\e132";
}
.glyphicon-circle-arrow-up:before {
  content: "\e133";
}
.glyphicon-circle-arrow-down:before {
  content: "\e134";
}
.glyphicon-globe:before {
  content: "\e135";
}
.glyphicon-wrench:before {
  content: "\e136";
}
.glyphicon-tasks:before {
  content: "\e137";
}
.glyphicon-filter:before {
  content: "\e138";
}
.glyphicon-briefcase:before {
  content: "\e139";
}
.glyphicon-fullscreen:before {
  content: "\e140";
}
.glyphicon-dashboard:before {
  content: "\e141";
}
.glyphicon-paperclip:before {
  content: "\e142";
}
.glyphicon-heart-empty:before {
  content: "\e143";
}
.glyphicon-link:before {
  content: "\e144";
}
.glyphicon-phone:before {
  content: "\e145";
}
.glyphicon-pushpin:before {
  content: "\e146";
}
.glyphicon-usd:before {
  content: "\e148";
}
.glyphicon-gbp:before {
  content: "\e149";
}
.glyphicon-sort:before {
  content: "\e150";
}
.glyphicon-sort-by-alphabet:before {
  content: "\e151";
}
.glyphicon-sort-by-alphabet-alt:before {
  content: "\e152";
}
.glyphicon-sort-by-order:before {
  content: "\e153";
}
.glyphicon-sort-by-order-alt:before {
  content: "\e154";
}
.glyphicon-sort-by-attributes:before {
  content: "\e155";
}
.glyphicon-sort-by-attributes-alt:before {
  content: "\e156";
}
.glyphicon-unchecked:before {
  content: "\e157";
}
.glyphicon-expand:before {
  content: "\e158";
}
.glyphicon-collapse-down:before {
  content: "\e159";
}
.glyphicon-collapse-up:before {
  content: "\e160";
}
.glyphicon-log-in:before {
  content: "\e161";
}
.glyphicon-flash:before {
  content: "\e162";
}
.glyphicon-log-out:before {
  content: "\e163";
}
.glyphicon-new-window:before {
  content: "\e164";
}
.glyphicon-record:before {
  content: "\e165";
}
.glyphicon-save:before {
  content: "\e166";
}
.glyphicon-open:before {
  content: "\e167";
}
.glyphicon-saved:before {
  content: "\e168";
}
.glyphicon-import:before {
  content: "\e169";
}
.glyphicon-export:before {
  content: "\e170";
}
.glyphicon-send:before {
  content: "\e171";
}
.glyphicon-floppy-disk:before {
  content: "\e172";
}
.glyphicon-floppy-saved:before {
  content: "\e173";
}
.glyphicon-floppy-remove:before {
  content: "\e174";
}
.glyphicon-floppy-save:before {
  content: "\e175";
}
.glyphicon-floppy-open:before {
  content: "\e176";
}
.glyphicon-credit-card:before {
  content: "\e177";
}
.glyphicon-transfer:before {
  content: "\e178";
}
.glyphicon-cutlery:before {
  content: "\e179";
}
.glyphicon-header:before {
  content: "\e180";
}
.glyphicon-compressed:before {
  content: "\e181";
}
.glyphicon-earphone:before {
  content: "\e182";
}
.glyphicon-phone-alt:before {
  content: "\e183";
}
.glyphicon-tower:before {
  content: "\e184";
}
.glyphicon-stats:before {
  content: "\e185";
}
.glyphicon-sd-video:before {
  content: "\e186";
}
.glyphicon-hd-video:before {
  content: "\e187";
}
.glyphicon-subtitles:before {
  content: "\e188";
}
.glyphicon-sound-stereo:before {
  content: "\e189";
}
.glyphicon-sound-dolby:before {
  content: "\e190";
}
.glyphicon-sound-5-1:before {
  content: "\e191";
}
.glyphicon-sound-6-1:before {
  content: "\e192";
}
.glyphicon-sound-7-1:before {
  content: "\e193";
}
.glyphicon-copyright-mark:before {
  content: "\e194";
}
.glyphicon-registration-mark:before {
  content: "\e195";
}
.glyphicon-cloud-download:before {
  content: "\e197";
}
.glyphicon-cloud-upload:before {
  content: "\e198";
}
.glyphicon-tree-conifer:before {
  content: "\e199";
}
.glyphicon-tree-deciduous:before {
  content: "\e200";
}
.glyphicon-cd:before {
  content: "\e201";
}
.glyphicon-save-file:before {
  content: "\e202";
}
.glyphicon-open-file:before {
  content: "\e203";
}
.glyphicon-level-up:before {
  content: "\e204";
}
.glyphicon-copy:before {
  content: "\e205";
}
.glyphicon-paste:before {
  content: "\e206";
}
.glyphicon-alert:before {
  content: "\e209";
}
.glyphicon-equalizer:before {
  content: "\e210";
}
.glyphicon-king:before {
  content: "\e211";
}
.glyphicon-queen:before {
  content: "\e212";
}
.glyphicon-pawn:before {
  content: "\e213";
}
.glyphicon-bishop:before {
  content: "\e214";
}
.glyphicon-knight:before {
  content: "\e215";
}
.glyphicon-baby-formula:before {
  content: "\e216";
}
.glyphicon-tent:before {
  content: "\26fa";
}
.glyphicon-blackboard:before {
  content: "\e218";
}
.glyphicon-bed:before {
  content: "\e219";
}
.glyphicon-apple:before {
  content: "\f8ff";
}
.glyphicon-erase:before {
  content: "\e221";
}
.glyphicon-hourglass:before {
  content: "\231b";
}
.glyphicon-lamp:before {
  content: "\e223";
}
.glyphicon-duplicate:before {
  content: "\e224";
}
.glyphicon-piggy-bank:before {
  content: "\e225";
}
.glyphicon-scissors:before {
  content: "\e226";
}
.glyphicon-bitcoin:before {
  content: "\e227";
}
.glyphicon-btc:before {
  content: "\e227";
}
.glyphicon-xbt:before {
  content: "\e227";
}
.glyphicon-yen:before {
  content: "\00a5";
}
.glyphicon-jpy:before {
  content: "\00a5";
}
.glyphicon-ruble:before {
  content: "\20bd";
}
.glyphicon-rub:before {
  content: "\20bd";
}
.glyphicon-scale:before {
  content: "\e230";
}
.glyphicon-ice-lolly:before {
  content: "\e231";
}
.glyphicon-ice-lolly-tasted:before {
  content: "\e232";
}
.glyphicon-education:before {
  content: "\e233";
}
.glyphicon-option-horizontal:before {
  content: "\e234";
}
.glyphicon-option-vertical:before {
  content: "\e235";
}
.glyphicon-menu-hamburger:before {
  content: "\e236";
}
.glyphicon-modal-window:before {
  content: "\e237";
}
.glyphicon-oil:before {
  content: "\e238";
}
.glyphicon-grain:before {
  content: "\e239";
}
.glyphicon-sunglasses:before {
  content: "\e240";
}
.glyphicon-text-size:before {
  content: "\e241";
}
.glyphicon-text-color:before {
  content: "\e242";
}
.glyphicon-text-background:before {
  content: "\e243";
}
.glyphicon-object-align-top:before {
  content: "\e244";
}
.glyphicon-object-align-bottom:before {
  content: "\e245";
}
.glyphicon-object-align-horizontal:before {
  content: "\e246";
}
.glyphicon-object-align-left:before {
  content: "\e247";
}
.glyphicon-object-align-vertical:before {
  content: "\e248";
}
.glyphicon-object-align-right:before {
  content: "\e249";
}
.glyphicon-triangle-right:before {
  content: "\e250";
}
.glyphicon-triangle-left:before {
  content: "\e251";
}
.glyphicon-triangle-bottom:before {
  content: "\e252";
}
.glyphicon-triangle-top:before {
  content: "\e253";
}
.glyphicon-console:before {
  content: "\e254";
}
.glyphicon-superscript:before {
  content: "\e255";
}
.glyphicon-subscript:before {
  content: "\e256";
}
.glyphicon-menu-left:before {
  content: "\e257";
}
.glyphicon-menu-right:before {
  content: "\e258";
}
.glyphicon-menu-down:before {
  content: "\e259";
}
.glyphicon-menu-up:before {
  content: "\e260";
}
* {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
*:before,
*:after {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
html {
  font-size: 10px;
  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
}
body {
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-size: 13px;
  line-height: 1.42857143;
  color: #000;
  background-color: #fff;
}
input,
button,
select,
textarea {
  font-family: inherit;
  font-size: inherit;
  line-height: inherit;
}
a {
  color: #337ab7;
  text-decoration: none;
}
a:hover,
a:focus {
  color: #23527c;
  text-decoration: underline;
}
a:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
figure {
  margin: 0;
}
img {
  vertical-align: middle;
}
.img-responsive,
.thumbnail > img,
.thumbnail a > img,
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  display: block;
  max-width: 100%;
  height: auto;
}
.img-rounded {
  border-radius: 3px;
}
.img-thumbnail {
  padding: 4px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: all 0.2s ease-in-out;
  -o-transition: all 0.2s ease-in-out;
  transition: all 0.2s ease-in-out;
  display: inline-block;
  max-width: 100%;
  height: auto;
}
.img-circle {
  border-radius: 50%;
}
hr {
  margin-top: 18px;
  margin-bottom: 18px;
  border: 0;
  border-top: 1px solid #eeeeee;
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  margin: -1px;
  padding: 0;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
[role="button"] {
  cursor: pointer;
}
h1,
h2,
h3,
h4,
h5,
h6,
.h1,
.h2,
.h3,
.h4,
.h5,
.h6 {
  font-family: inherit;
  font-weight: 500;
  line-height: 1.1;
  color: inherit;
}
h1 small,
h2 small,
h3 small,
h4 small,
h5 small,
h6 small,
.h1 small,
.h2 small,
.h3 small,
.h4 small,
.h5 small,
.h6 small,
h1 .small,
h2 .small,
h3 .small,
h4 .small,
h5 .small,
h6 .small,
.h1 .small,
.h2 .small,
.h3 .small,
.h4 .small,
.h5 .small,
.h6 .small {
  font-weight: normal;
  line-height: 1;
  color: #777777;
}
h1,
.h1,
h2,
.h2,
h3,
.h3 {
  margin-top: 18px;
  margin-bottom: 9px;
}
h1 small,
.h1 small,
h2 small,
.h2 small,
h3 small,
.h3 small,
h1 .small,
.h1 .small,
h2 .small,
.h2 .small,
h3 .small,
.h3 .small {
  font-size: 65%;
}
h4,
.h4,
h5,
.h5,
h6,
.h6 {
  margin-top: 9px;
  margin-bottom: 9px;
}
h4 small,
.h4 small,
h5 small,
.h5 small,
h6 small,
.h6 small,
h4 .small,
.h4 .small,
h5 .small,
.h5 .small,
h6 .small,
.h6 .small {
  font-size: 75%;
}
h1,
.h1 {
  font-size: 33px;
}
h2,
.h2 {
  font-size: 27px;
}
h3,
.h3 {
  font-size: 23px;
}
h4,
.h4 {
  font-size: 17px;
}
h5,
.h5 {
  font-size: 13px;
}
h6,
.h6 {
  font-size: 12px;
}
p {
  margin: 0 0 9px;
}
.lead {
  margin-bottom: 18px;
  font-size: 14px;
  font-weight: 300;
  line-height: 1.4;
}
@media (min-width: 768px) {
  .lead {
    font-size: 19.5px;
  }
}
small,
.small {
  font-size: 92%;
}
mark,
.mark {
  background-color: #fcf8e3;
  padding: .2em;
}
.text-left {
  text-align: left;
}
.text-right {
  text-align: right;
}
.text-center {
  text-align: center;
}
.text-justify {
  text-align: justify;
}
.text-nowrap {
  white-space: nowrap;
}
.text-lowercase {
  text-transform: lowercase;
}
.text-uppercase {
  text-transform: uppercase;
}
.text-capitalize {
  text-transform: capitalize;
}
.text-muted {
  color: #777777;
}
.text-primary {
  color: #337ab7;
}
a.text-primary:hover,
a.text-primary:focus {
  color: #286090;
}
.text-success {
  color: #3c763d;
}
a.text-success:hover,
a.text-success:focus {
  color: #2b542c;
}
.text-info {
  color: #31708f;
}
a.text-info:hover,
a.text-info:focus {
  color: #245269;
}
.text-warning {
  color: #8a6d3b;
}
a.text-warning:hover,
a.text-warning:focus {
  color: #66512c;
}
.text-danger {
  color: #a94442;
}
a.text-danger:hover,
a.text-danger:focus {
  color: #843534;
}
.bg-primary {
  color: #fff;
  background-color: #337ab7;
}
a.bg-primary:hover,
a.bg-primary:focus {
  background-color: #286090;
}
.bg-success {
  background-color: #dff0d8;
}
a.bg-success:hover,
a.bg-success:focus {
  background-color: #c1e2b3;
}
.bg-info {
  background-color: #d9edf7;
}
a.bg-info:hover,
a.bg-info:focus {
  background-color: #afd9ee;
}
.bg-warning {
  background-color: #fcf8e3;
}
a.bg-warning:hover,
a.bg-warning:focus {
  background-color: #f7ecb5;
}
.bg-danger {
  background-color: #f2dede;
}
a.bg-danger:hover,
a.bg-danger:focus {
  background-color: #e4b9b9;
}
.page-header {
  padding-bottom: 8px;
  margin: 36px 0 18px;
  border-bottom: 1px solid #eeeeee;
}
ul,
ol {
  margin-top: 0;
  margin-bottom: 9px;
}
ul ul,
ol ul,
ul ol,
ol ol {
  margin-bottom: 0;
}
.list-unstyled {
  padding-left: 0;
  list-style: none;
}
.list-inline {
  padding-left: 0;
  list-style: none;
  margin-left: -5px;
}
.list-inline > li {
  display: inline-block;
  padding-left: 5px;
  padding-right: 5px;
}
dl {
  margin-top: 0;
  margin-bottom: 18px;
}
dt,
dd {
  line-height: 1.42857143;
}
dt {
  font-weight: bold;
}
dd {
  margin-left: 0;
}
@media (min-width: 541px) {
  .dl-horizontal dt {
    float: left;
    width: 160px;
    clear: left;
    text-align: right;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
  }
  .dl-horizontal dd {
    margin-left: 180px;
  }
}
abbr[title],
abbr[data-original-title] {
  cursor: help;
  border-bottom: 1px dotted #777777;
}
.initialism {
  font-size: 90%;
  text-transform: uppercase;
}
blockquote {
  padding: 9px 18px;
  margin: 0 0 18px;
  font-size: inherit;
  border-left: 5px solid #eeeeee;
}
blockquote p:last-child,
blockquote ul:last-child,
blockquote ol:last-child {
  margin-bottom: 0;
}
blockquote footer,
blockquote small,
blockquote .small {
  display: block;
  font-size: 80%;
  line-height: 1.42857143;
  color: #777777;
}
blockquote footer:before,
blockquote small:before,
blockquote .small:before {
  content: '\2014 \00A0';
}
.blockquote-reverse,
blockquote.pull-right {
  padding-right: 15px;
  padding-left: 0;
  border-right: 5px solid #eeeeee;
  border-left: 0;
  text-align: right;
}
.blockquote-reverse footer:before,
blockquote.pull-right footer:before,
.blockquote-reverse small:before,
blockquote.pull-right small:before,
.blockquote-reverse .small:before,
blockquote.pull-right .small:before {
  content: '';
}
.blockquote-reverse footer:after,
blockquote.pull-right footer:after,
.blockquote-reverse small:after,
blockquote.pull-right small:after,
.blockquote-reverse .small:after,
blockquote.pull-right .small:after {
  content: '\00A0 \2014';
}
address {
  margin-bottom: 18px;
  font-style: normal;
  line-height: 1.42857143;
}
code,
kbd,
pre,
samp {
  font-family: monospace;
}
code {
  padding: 2px 4px;
  font-size: 90%;
  color: #c7254e;
  background-color: #f9f2f4;
  border-radius: 2px;
}
kbd {
  padding: 2px 4px;
  font-size: 90%;
  color: #888;
  background-color: transparent;
  border-radius: 1px;
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
}
kbd kbd {
  padding: 0;
  font-size: 100%;
  font-weight: bold;
  box-shadow: none;
}
pre {
  display: block;
  padding: 8.5px;
  margin: 0 0 9px;
  font-size: 12px;
  line-height: 1.42857143;
  word-break: break-all;
  word-wrap: break-word;
  color: #333333;
  background-color: #f5f5f5;
  border: 1px solid #ccc;
  border-radius: 2px;
}
pre code {
  padding: 0;
  font-size: inherit;
  color: inherit;
  white-space: pre-wrap;
  background-color: transparent;
  border-radius: 0;
}
.pre-scrollable {
  max-height: 340px;
  overflow-y: scroll;
}
.container {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
@media (min-width: 768px) {
  .container {
    width: 768px;
  }
}
@media (min-width: 992px) {
  .container {
    width: 940px;
  }
}
@media (min-width: 1200px) {
  .container {
    width: 1140px;
  }
}
.container-fluid {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
.row {
  margin-left: 0px;
  margin-right: 0px;
}
.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
  position: relative;
  min-height: 1px;
  padding-left: 0px;
  padding-right: 0px;
}
.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
  float: left;
}
.col-xs-12 {
  width: 100%;
}
.col-xs-11 {
  width: 91.66666667%;
}
.col-xs-10 {
  width: 83.33333333%;
}
.col-xs-9 {
  width: 75%;
}
.col-xs-8 {
  width: 66.66666667%;
}
.col-xs-7 {
  width: 58.33333333%;
}
.col-xs-6 {
  width: 50%;
}
.col-xs-5 {
  width: 41.66666667%;
}
.col-xs-4 {
  width: 33.33333333%;
}
.col-xs-3 {
  width: 25%;
}
.col-xs-2 {
  width: 16.66666667%;
}
.col-xs-1 {
  width: 8.33333333%;
}
.col-xs-pull-12 {
  right: 100%;
}
.col-xs-pull-11 {
  right: 91.66666667%;
}
.col-xs-pull-10 {
  right: 83.33333333%;
}
.col-xs-pull-9 {
  right: 75%;
}
.col-xs-pull-8 {
  right: 66.66666667%;
}
.col-xs-pull-7 {
  right: 58.33333333%;
}
.col-xs-pull-6 {
  right: 50%;
}
.col-xs-pull-5 {
  right: 41.66666667%;
}
.col-xs-pull-4 {
  right: 33.33333333%;
}
.col-xs-pull-3 {
  right: 25%;
}
.col-xs-pull-2 {
  right: 16.66666667%;
}
.col-xs-pull-1 {
  right: 8.33333333%;
}
.col-xs-pull-0 {
  right: auto;
}
.col-xs-push-12 {
  left: 100%;
}
.col-xs-push-11 {
  left: 91.66666667%;
}
.col-xs-push-10 {
  left: 83.33333333%;
}
.col-xs-push-9 {
  left: 75%;
}
.col-xs-push-8 {
  left: 66.66666667%;
}
.col-xs-push-7 {
  left: 58.33333333%;
}
.col-xs-push-6 {
  left: 50%;
}
.col-xs-push-5 {
  left: 41.66666667%;
}
.col-xs-push-4 {
  left: 33.33333333%;
}
.col-xs-push-3 {
  left: 25%;
}
.col-xs-push-2 {
  left: 16.66666667%;
}
.col-xs-push-1 {
  left: 8.33333333%;
}
.col-xs-push-0 {
  left: auto;
}
.col-xs-offset-12 {
  margin-left: 100%;
}
.col-xs-offset-11 {
  margin-left: 91.66666667%;
}
.col-xs-offset-10 {
  margin-left: 83.33333333%;
}
.col-xs-offset-9 {
  margin-left: 75%;
}
.col-xs-offset-8 {
  margin-left: 66.66666667%;
}
.col-xs-offset-7 {
  margin-left: 58.33333333%;
}
.col-xs-offset-6 {
  margin-left: 50%;
}
.col-xs-offset-5 {
  margin-left: 41.66666667%;
}
.col-xs-offset-4 {
  margin-left: 33.33333333%;
}
.col-xs-offset-3 {
  margin-left: 25%;
}
.col-xs-offset-2 {
  margin-left: 16.66666667%;
}
.col-xs-offset-1 {
  margin-left: 8.33333333%;
}
.col-xs-offset-0 {
  margin-left: 0%;
}
@media (min-width: 768px) {
  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
    float: left;
  }
  .col-sm-12 {
    width: 100%;
  }
  .col-sm-11 {
    width: 91.66666667%;
  }
  .col-sm-10 {
    width: 83.33333333%;
  }
  .col-sm-9 {
    width: 75%;
  }
  .col-sm-8 {
    width: 66.66666667%;
  }
  .col-sm-7 {
    width: 58.33333333%;
  }
  .col-sm-6 {
    width: 50%;
  }
  .col-sm-5 {
    width: 41.66666667%;
  }
  .col-sm-4 {
    width: 33.33333333%;
  }
  .col-sm-3 {
    width: 25%;
  }
  .col-sm-2 {
    width: 16.66666667%;
  }
  .col-sm-1 {
    width: 8.33333333%;
  }
  .col-sm-pull-12 {
    right: 100%;
  }
  .col-sm-pull-11 {
    right: 91.66666667%;
  }
  .col-sm-pull-10 {
    right: 83.33333333%;
  }
  .col-sm-pull-9 {
    right: 75%;
  }
  .col-sm-pull-8 {
    right: 66.66666667%;
  }
  .col-sm-pull-7 {
    right: 58.33333333%;
  }
  .col-sm-pull-6 {
    right: 50%;
  }
  .col-sm-pull-5 {
    right: 41.66666667%;
  }
  .col-sm-pull-4 {
    right: 33.33333333%;
  }
  .col-sm-pull-3 {
    right: 25%;
  }
  .col-sm-pull-2 {
    right: 16.66666667%;
  }
  .col-sm-pull-1 {
    right: 8.33333333%;
  }
  .col-sm-pull-0 {
    right: auto;
  }
  .col-sm-push-12 {
    left: 100%;
  }
  .col-sm-push-11 {
    left: 91.66666667%;
  }
  .col-sm-push-10 {
    left: 83.33333333%;
  }
  .col-sm-push-9 {
    left: 75%;
  }
  .col-sm-push-8 {
    left: 66.66666667%;
  }
  .col-sm-push-7 {
    left: 58.33333333%;
  }
  .col-sm-push-6 {
    left: 50%;
  }
  .col-sm-push-5 {
    left: 41.66666667%;
  }
  .col-sm-push-4 {
    left: 33.33333333%;
  }
  .col-sm-push-3 {
    left: 25%;
  }
  .col-sm-push-2 {
    left: 16.66666667%;
  }
  .col-sm-push-1 {
    left: 8.33333333%;
  }
  .col-sm-push-0 {
    left: auto;
  }
  .col-sm-offset-12 {
    margin-left: 100%;
  }
  .col-sm-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-sm-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-sm-offset-9 {
    margin-left: 75%;
  }
  .col-sm-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-sm-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-sm-offset-6 {
    margin-left: 50%;
  }
  .col-sm-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-sm-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-sm-offset-3 {
    margin-left: 25%;
  }
  .col-sm-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-sm-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-sm-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 992px) {
  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
    float: left;
  }
  .col-md-12 {
    width: 100%;
  }
  .col-md-11 {
    width: 91.66666667%;
  }
  .col-md-10 {
    width: 83.33333333%;
  }
  .col-md-9 {
    width: 75%;
  }
  .col-md-8 {
    width: 66.66666667%;
  }
  .col-md-7 {
    width: 58.33333333%;
  }
  .col-md-6 {
    width: 50%;
  }
  .col-md-5 {
    width: 41.66666667%;
  }
  .col-md-4 {
    width: 33.33333333%;
  }
  .col-md-3 {
    width: 25%;
  }
  .col-md-2 {
    width: 16.66666667%;
  }
  .col-md-1 {
    width: 8.33333333%;
  }
  .col-md-pull-12 {
    right: 100%;
  }
  .col-md-pull-11 {
    right: 91.66666667%;
  }
  .col-md-pull-10 {
    right: 83.33333333%;
  }
  .col-md-pull-9 {
    right: 75%;
  }
  .col-md-pull-8 {
    right: 66.66666667%;
  }
  .col-md-pull-7 {
    right: 58.33333333%;
  }
  .col-md-pull-6 {
    right: 50%;
  }
  .col-md-pull-5 {
    right: 41.66666667%;
  }
  .col-md-pull-4 {
    right: 33.33333333%;
  }
  .col-md-pull-3 {
    right: 25%;
  }
  .col-md-pull-2 {
    right: 16.66666667%;
  }
  .col-md-pull-1 {
    right: 8.33333333%;
  }
  .col-md-pull-0 {
    right: auto;
  }
  .col-md-push-12 {
    left: 100%;
  }
  .col-md-push-11 {
    left: 91.66666667%;
  }
  .col-md-push-10 {
    left: 83.33333333%;
  }
  .col-md-push-9 {
    left: 75%;
  }
  .col-md-push-8 {
    left: 66.66666667%;
  }
  .col-md-push-7 {
    left: 58.33333333%;
  }
  .col-md-push-6 {
    left: 50%;
  }
  .col-md-push-5 {
    left: 41.66666667%;
  }
  .col-md-push-4 {
    left: 33.33333333%;
  }
  .col-md-push-3 {
    left: 25%;
  }
  .col-md-push-2 {
    left: 16.66666667%;
  }
  .col-md-push-1 {
    left: 8.33333333%;
  }
  .col-md-push-0 {
    left: auto;
  }
  .col-md-offset-12 {
    margin-left: 100%;
  }
  .col-md-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-md-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-md-offset-9 {
    margin-left: 75%;
  }
  .col-md-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-md-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-md-offset-6 {
    margin-left: 50%;
  }
  .col-md-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-md-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-md-offset-3 {
    margin-left: 25%;
  }
  .col-md-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-md-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-md-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 1200px) {
  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
    float: left;
  }
  .col-lg-12 {
    width: 100%;
  }
  .col-lg-11 {
    width: 91.66666667%;
  }
  .col-lg-10 {
    width: 83.33333333%;
  }
  .col-lg-9 {
    width: 75%;
  }
  .col-lg-8 {
    width: 66.66666667%;
  }
  .col-lg-7 {
    width: 58.33333333%;
  }
  .col-lg-6 {
    width: 50%;
  }
  .col-lg-5 {
    width: 41.66666667%;
  }
  .col-lg-4 {
    width: 33.33333333%;
  }
  .col-lg-3 {
    width: 25%;
  }
  .col-lg-2 {
    width: 16.66666667%;
  }
  .col-lg-1 {
    width: 8.33333333%;
  }
  .col-lg-pull-12 {
    right: 100%;
  }
  .col-lg-pull-11 {
    right: 91.66666667%;
  }
  .col-lg-pull-10 {
    right: 83.33333333%;
  }
  .col-lg-pull-9 {
    right: 75%;
  }
  .col-lg-pull-8 {
    right: 66.66666667%;
  }
  .col-lg-pull-7 {
    right: 58.33333333%;
  }
  .col-lg-pull-6 {
    right: 50%;
  }
  .col-lg-pull-5 {
    right: 41.66666667%;
  }
  .col-lg-pull-4 {
    right: 33.33333333%;
  }
  .col-lg-pull-3 {
    right: 25%;
  }
  .col-lg-pull-2 {
    right: 16.66666667%;
  }
  .col-lg-pull-1 {
    right: 8.33333333%;
  }
  .col-lg-pull-0 {
    right: auto;
  }
  .col-lg-push-12 {
    left: 100%;
  }
  .col-lg-push-11 {
    left: 91.66666667%;
  }
  .col-lg-push-10 {
    left: 83.33333333%;
  }
  .col-lg-push-9 {
    left: 75%;
  }
  .col-lg-push-8 {
    left: 66.66666667%;
  }
  .col-lg-push-7 {
    left: 58.33333333%;
  }
  .col-lg-push-6 {
    left: 50%;
  }
  .col-lg-push-5 {
    left: 41.66666667%;
  }
  .col-lg-push-4 {
    left: 33.33333333%;
  }
  .col-lg-push-3 {
    left: 25%;
  }
  .col-lg-push-2 {
    left: 16.66666667%;
  }
  .col-lg-push-1 {
    left: 8.33333333%;
  }
  .col-lg-push-0 {
    left: auto;
  }
  .col-lg-offset-12 {
    margin-left: 100%;
  }
  .col-lg-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-lg-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-lg-offset-9 {
    margin-left: 75%;
  }
  .col-lg-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-lg-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-lg-offset-6 {
    margin-left: 50%;
  }
  .col-lg-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-lg-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-lg-offset-3 {
    margin-left: 25%;
  }
  .col-lg-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-lg-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-lg-offset-0 {
    margin-left: 0%;
  }
}
table {
  background-color: transparent;
}
caption {
  padding-top: 8px;
  padding-bottom: 8px;
  color: #777777;
  text-align: left;
}
th {
  text-align: left;
}
.table {
  width: 100%;
  max-width: 100%;
  margin-bottom: 18px;
}
.table > thead > tr > th,
.table > tbody > tr > th,
.table > tfoot > tr > th,
.table > thead > tr > td,
.table > tbody > tr > td,
.table > tfoot > tr > td {
  padding: 8px;
  line-height: 1.42857143;
  vertical-align: top;
  border-top: 1px solid #ddd;
}
.table > thead > tr > th {
  vertical-align: bottom;
  border-bottom: 2px solid #ddd;
}
.table > caption + thead > tr:first-child > th,
.table > colgroup + thead > tr:first-child > th,
.table > thead:first-child > tr:first-child > th,
.table > caption + thead > tr:first-child > td,
.table > colgroup + thead > tr:first-child > td,
.table > thead:first-child > tr:first-child > td {
  border-top: 0;
}
.table > tbody + tbody {
  border-top: 2px solid #ddd;
}
.table .table {
  background-color: #fff;
}
.table-condensed > thead > tr > th,
.table-condensed > tbody > tr > th,
.table-condensed > tfoot > tr > th,
.table-condensed > thead > tr > td,
.table-condensed > tbody > tr > td,
.table-condensed > tfoot > tr > td {
  padding: 5px;
}
.table-bordered {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > tbody > tr > th,
.table-bordered > tfoot > tr > th,
.table-bordered > thead > tr > td,
.table-bordered > tbody > tr > td,
.table-bordered > tfoot > tr > td {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > thead > tr > td {
  border-bottom-width: 2px;
}
.table-striped > tbody > tr:nth-of-type(odd) {
  background-color: #f9f9f9;
}
.table-hover > tbody > tr:hover {
  background-color: #f5f5f5;
}
table col[class*="col-"] {
  position: static;
  float: none;
  display: table-column;
}
table td[class*="col-"],
table th[class*="col-"] {
  position: static;
  float: none;
  display: table-cell;
}
.table > thead > tr > td.active,
.table > tbody > tr > td.active,
.table > tfoot > tr > td.active,
.table > thead > tr > th.active,
.table > tbody > tr > th.active,
.table > tfoot > tr > th.active,
.table > thead > tr.active > td,
.table > tbody > tr.active > td,
.table > tfoot > tr.active > td,
.table > thead > tr.active > th,
.table > tbody > tr.active > th,
.table > tfoot > tr.active > th {
  background-color: #f5f5f5;
}
.table-hover > tbody > tr > td.active:hover,
.table-hover > tbody > tr > th.active:hover,
.table-hover > tbody > tr.active:hover > td,
.table-hover > tbody > tr:hover > .active,
.table-hover > tbody > tr.active:hover > th {
  background-color: #e8e8e8;
}
.table > thead > tr > td.success,
.table > tbody > tr > td.success,
.table > tfoot > tr > td.success,
.table > thead > tr > th.success,
.table > tbody > tr > th.success,
.table > tfoot > tr > th.success,
.table > thead > tr.success > td,
.table > tbody > tr.success > td,
.table > tfoot > tr.success > td,
.table > thead > tr.success > th,
.table > tbody > tr.success > th,
.table > tfoot > tr.success > th {
  background-color: #dff0d8;
}
.table-hover > tbody > tr > td.success:hover,
.table-hover > tbody > tr > th.success:hover,
.table-hover > tbody > tr.success:hover > td,
.table-hover > tbody > tr:hover > .success,
.table-hover > tbody > tr.success:hover > th {
  background-color: #d0e9c6;
}
.table > thead > tr > td.info,
.table > tbody > tr > td.info,
.table > tfoot > tr > td.info,
.table > thead > tr > th.info,
.table > tbody > tr > th.info,
.table > tfoot > tr > th.info,
.table > thead > tr.info > td,
.table > tbody > tr.info > td,
.table > tfoot > tr.info > td,
.table > thead > tr.info > th,
.table > tbody > tr.info > th,
.table > tfoot > tr.info > th {
  background-color: #d9edf7;
}
.table-hover > tbody > tr > td.info:hover,
.table-hover > tbody > tr > th.info:hover,
.table-hover > tbody > tr.info:hover > td,
.table-hover > tbody > tr:hover > .info,
.table-hover > tbody > tr.info:hover > th {
  background-color: #c4e3f3;
}
.table > thead > tr > td.warning,
.table > tbody > tr > td.warning,
.table > tfoot > tr > td.warning,
.table > thead > tr > th.warning,
.table > tbody > tr > th.warning,
.table > tfoot > tr > th.warning,
.table > thead > tr.warning > td,
.table > tbody > tr.warning > td,
.table > tfoot > tr.warning > td,
.table > thead > tr.warning > th,
.table > tbody > tr.warning > th,
.table > tfoot > tr.warning > th {
  background-color: #fcf8e3;
}
.table-hover > tbody > tr > td.warning:hover,
.table-hover > tbody > tr > th.warning:hover,
.table-hover > tbody > tr.warning:hover > td,
.table-hover > tbody > tr:hover > .warning,
.table-hover > tbody > tr.warning:hover > th {
  background-color: #faf2cc;
}
.table > thead > tr > td.danger,
.table > tbody > tr > td.danger,
.table > tfoot > tr > td.danger,
.table > thead > tr > th.danger,
.table > tbody > tr > th.danger,
.table > tfoot > tr > th.danger,
.table > thead > tr.danger > td,
.table > tbody > tr.danger > td,
.table > tfoot > tr.danger > td,
.table > thead > tr.danger > th,
.table > tbody > tr.danger > th,
.table > tfoot > tr.danger > th {
  background-color: #f2dede;
}
.table-hover > tbody > tr > td.danger:hover,
.table-hover > tbody > tr > th.danger:hover,
.table-hover > tbody > tr.danger:hover > td,
.table-hover > tbody > tr:hover > .danger,
.table-hover > tbody > tr.danger:hover > th {
  background-color: #ebcccc;
}
.table-responsive {
  overflow-x: auto;
  min-height: 0.01%;
}
@media screen and (max-width: 767px) {
  .table-responsive {
    width: 100%;
    margin-bottom: 13.5px;
    overflow-y: hidden;
    -ms-overflow-style: -ms-autohiding-scrollbar;
    border: 1px solid #ddd;
  }
  .table-responsive > .table {
    margin-bottom: 0;
  }
  .table-responsive > .table > thead > tr > th,
  .table-responsive > .table > tbody > tr > th,
  .table-responsive > .table > tfoot > tr > th,
  .table-responsive > .table > thead > tr > td,
  .table-responsive > .table > tbody > tr > td,
  .table-responsive > .table > tfoot > tr > td {
    white-space: nowrap;
  }
  .table-responsive > .table-bordered {
    border: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:first-child,
  .table-responsive > .table-bordered > tbody > tr > th:first-child,
  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
  .table-responsive > .table-bordered > thead > tr > td:first-child,
  .table-responsive > .table-bordered > tbody > tr > td:first-child,
  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
    border-left: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:last-child,
  .table-responsive > .table-bordered > tbody > tr > th:last-child,
  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
  .table-responsive > .table-bordered > thead > tr > td:last-child,
  .table-responsive > .table-bordered > tbody > tr > td:last-child,
  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
    border-right: 0;
  }
  .table-responsive > .table-bordered > tbody > tr:last-child > th,
  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
  .table-responsive > .table-bordered > tbody > tr:last-child > td,
  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
    border-bottom: 0;
  }
}
fieldset {
  padding: 0;
  margin: 0;
  border: 0;
  min-width: 0;
}
legend {
  display: block;
  width: 100%;
  padding: 0;
  margin-bottom: 18px;
  font-size: 19.5px;
  line-height: inherit;
  color: #333333;
  border: 0;
  border-bottom: 1px solid #e5e5e5;
}
label {
  display: inline-block;
  max-width: 100%;
  margin-bottom: 5px;
  font-weight: bold;
}
input[type="search"] {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
input[type="radio"],
input[type="checkbox"] {
  margin: 4px 0 0;
  margin-top: 1px \9;
  line-height: normal;
}
input[type="file"] {
  display: block;
}
input[type="range"] {
  display: block;
  width: 100%;
}
select[multiple],
select[size] {
  height: auto;
}
input[type="file"]:focus,
input[type="radio"]:focus,
input[type="checkbox"]:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
output {
  display: block;
  padding-top: 7px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
}
.form-control {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
}
.form-control:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.form-control::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.form-control:-ms-input-placeholder {
  color: #999;
}
.form-control::-webkit-input-placeholder {
  color: #999;
}
.form-control::-ms-expand {
  border: 0;
  background-color: transparent;
}
.form-control[disabled],
.form-control[readonly],
fieldset[disabled] .form-control {
  background-color: #eeeeee;
  opacity: 1;
}
.form-control[disabled],
fieldset[disabled] .form-control {
  cursor: not-allowed;
}
textarea.form-control {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: none;
}
@media screen and (-webkit-min-device-pixel-ratio: 0) {
  input[type="date"].form-control,
  input[type="time"].form-control,
  input[type="datetime-local"].form-control,
  input[type="month"].form-control {
    line-height: 32px;
  }
  input[type="date"].input-sm,
  input[type="time"].input-sm,
  input[type="datetime-local"].input-sm,
  input[type="month"].input-sm,
  .input-group-sm input[type="date"],
  .input-group-sm input[type="time"],
  .input-group-sm input[type="datetime-local"],
  .input-group-sm input[type="month"] {
    line-height: 30px;
  }
  input[type="date"].input-lg,
  input[type="time"].input-lg,
  input[type="datetime-local"].input-lg,
  input[type="month"].input-lg,
  .input-group-lg input[type="date"],
  .input-group-lg input[type="time"],
  .input-group-lg input[type="datetime-local"],
  .input-group-lg input[type="month"] {
    line-height: 45px;
  }
}
.form-group {
  margin-bottom: 15px;
}
.radio,
.checkbox {
  position: relative;
  display: block;
  margin-top: 10px;
  margin-bottom: 10px;
}
.radio label,
.checkbox label {
  min-height: 18px;
  padding-left: 20px;
  margin-bottom: 0;
  font-weight: normal;
  cursor: pointer;
}
.radio input[type="radio"],
.radio-inline input[type="radio"],
.checkbox input[type="checkbox"],
.checkbox-inline input[type="checkbox"] {
  position: absolute;
  margin-left: -20px;
  margin-top: 4px \9;
}
.radio + .radio,
.checkbox + .checkbox {
  margin-top: -5px;
}
.radio-inline,
.checkbox-inline {
  position: relative;
  display: inline-block;
  padding-left: 20px;
  margin-bottom: 0;
  vertical-align: middle;
  font-weight: normal;
  cursor: pointer;
}
.radio-inline + .radio-inline,
.checkbox-inline + .checkbox-inline {
  margin-top: 0;
  margin-left: 10px;
}
input[type="radio"][disabled],
input[type="checkbox"][disabled],
input[type="radio"].disabled,
input[type="checkbox"].disabled,
fieldset[disabled] input[type="radio"],
fieldset[disabled] input[type="checkbox"] {
  cursor: not-allowed;
}
.radio-inline.disabled,
.checkbox-inline.disabled,
fieldset[disabled] .radio-inline,
fieldset[disabled] .checkbox-inline {
  cursor: not-allowed;
}
.radio.disabled label,
.checkbox.disabled label,
fieldset[disabled] .radio label,
fieldset[disabled] .checkbox label {
  cursor: not-allowed;
}
.form-control-static {
  padding-top: 7px;
  padding-bottom: 7px;
  margin-bottom: 0;
  min-height: 31px;
}
.form-control-static.input-lg,
.form-control-static.input-sm {
  padding-left: 0;
  padding-right: 0;
}
.input-sm {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-sm {
  height: 30px;
  line-height: 30px;
}
textarea.input-sm,
select[multiple].input-sm {
  height: auto;
}
.form-group-sm .form-control {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.form-group-sm select.form-control {
  height: 30px;
  line-height: 30px;
}
.form-group-sm textarea.form-control,
.form-group-sm select[multiple].form-control {
  height: auto;
}
.form-group-sm .form-control-static {
  height: 30px;
  min-height: 30px;
  padding: 6px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.input-lg {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-lg {
  height: 45px;
  line-height: 45px;
}
textarea.input-lg,
select[multiple].input-lg {
  height: auto;
}
.form-group-lg .form-control {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.form-group-lg select.form-control {
  height: 45px;
  line-height: 45px;
}
.form-group-lg textarea.form-control,
.form-group-lg select[multiple].form-control {
  height: auto;
}
.form-group-lg .form-control-static {
  height: 45px;
  min-height: 35px;
  padding: 11px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.has-feedback {
  position: relative;
}
.has-feedback .form-control {
  padding-right: 40px;
}
.form-control-feedback {
  position: absolute;
  top: 0;
  right: 0;
  z-index: 2;
  display: block;
  width: 32px;
  height: 32px;
  line-height: 32px;
  text-align: center;
  pointer-events: none;
}
.input-lg + .form-control-feedback,
.input-group-lg + .form-control-feedback,
.form-group-lg .form-control + .form-control-feedback {
  width: 45px;
  height: 45px;
  line-height: 45px;
}
.input-sm + .form-control-feedback,
.input-group-sm + .form-control-feedback,
.form-group-sm .form-control + .form-control-feedback {
  width: 30px;
  height: 30px;
  line-height: 30px;
}
.has-success .help-block,
.has-success .control-label,
.has-success .radio,
.has-success .checkbox,
.has-success .radio-inline,
.has-success .checkbox-inline,
.has-success.radio label,
.has-success.checkbox label,
.has-success.radio-inline label,
.has-success.checkbox-inline label {
  color: #3c763d;
}
.has-success .form-control {
  border-color: #3c763d;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-success .form-control:focus {
  border-color: #2b542c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
}
.has-success .input-group-addon {
  color: #3c763d;
  border-color: #3c763d;
  background-color: #dff0d8;
}
.has-success .form-control-feedback {
  color: #3c763d;
}
.has-warning .help-block,
.has-warning .control-label,
.has-warning .radio,
.has-warning .checkbox,
.has-warning .radio-inline,
.has-warning .checkbox-inline,
.has-warning.radio label,
.has-warning.checkbox label,
.has-warning.radio-inline label,
.has-warning.checkbox-inline label {
  color: #8a6d3b;
}
.has-warning .form-control {
  border-color: #8a6d3b;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-warning .form-control:focus {
  border-color: #66512c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
}
.has-warning .input-group-addon {
  color: #8a6d3b;
  border-color: #8a6d3b;
  background-color: #fcf8e3;
}
.has-warning .form-control-feedback {
  color: #8a6d3b;
}
.has-error .help-block,
.has-error .control-label,
.has-error .radio,
.has-error .checkbox,
.has-error .radio-inline,
.has-error .checkbox-inline,
.has-error.radio label,
.has-error.checkbox label,
.has-error.radio-inline label,
.has-error.checkbox-inline label {
  color: #a94442;
}
.has-error .form-control {
  border-color: #a94442;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-error .form-control:focus {
  border-color: #843534;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
}
.has-error .input-group-addon {
  color: #a94442;
  border-color: #a94442;
  background-color: #f2dede;
}
.has-error .form-control-feedback {
  color: #a94442;
}
.has-feedback label ~ .form-control-feedback {
  top: 23px;
}
.has-feedback label.sr-only ~ .form-control-feedback {
  top: 0;
}
.help-block {
  display: block;
  margin-top: 5px;
  margin-bottom: 10px;
  color: #404040;
}
@media (min-width: 768px) {
  .form-inline .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .form-inline .form-control-static {
    display: inline-block;
  }
  .form-inline .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .form-inline .input-group .input-group-addon,
  .form-inline .input-group .input-group-btn,
  .form-inline .input-group .form-control {
    width: auto;
  }
  .form-inline .input-group > .form-control {
    width: 100%;
  }
  .form-inline .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio,
  .form-inline .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio label,
  .form-inline .checkbox label {
    padding-left: 0;
  }
  .form-inline .radio input[type="radio"],
  .form-inline .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .form-inline .has-feedback .form-control-feedback {
    top: 0;
  }
}
.form-horizontal .radio,
.form-horizontal .checkbox,
.form-horizontal .radio-inline,
.form-horizontal .checkbox-inline {
  margin-top: 0;
  margin-bottom: 0;
  padding-top: 7px;
}
.form-horizontal .radio,
.form-horizontal .checkbox {
  min-height: 25px;
}
.form-horizontal .form-group {
  margin-left: 0px;
  margin-right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .control-label {
    text-align: right;
    margin-bottom: 0;
    padding-top: 7px;
  }
}
.form-horizontal .has-feedback .form-control-feedback {
  right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .form-group-lg .control-label {
    padding-top: 11px;
    font-size: 17px;
  }
}
@media (min-width: 768px) {
  .form-horizontal .form-group-sm .control-label {
    padding-top: 6px;
    font-size: 12px;
  }
}
.btn {
  display: inline-block;
  margin-bottom: 0;
  font-weight: normal;
  text-align: center;
  vertical-align: middle;
  touch-action: manipulation;
  cursor: pointer;
  background-image: none;
  border: 1px solid transparent;
  white-space: nowrap;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  border-radius: 2px;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}
.btn:focus,
.btn:active:focus,
.btn.active:focus,
.btn.focus,
.btn:active.focus,
.btn.active.focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
.btn:hover,
.btn:focus,
.btn.focus {
  color: #333;
  text-decoration: none;
}
.btn:active,
.btn.active {
  outline: 0;
  background-image: none;
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn.disabled,
.btn[disabled],
fieldset[disabled] .btn {
  cursor: not-allowed;
  opacity: 0.65;
  filter: alpha(opacity=65);
  -webkit-box-shadow: none;
  box-shadow: none;
}
a.btn.disabled,
fieldset[disabled] a.btn {
  pointer-events: none;
}
.btn-default {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.btn-default:focus,
.btn-default.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.btn-default:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active:hover,
.btn-default.active:hover,
.open > .dropdown-toggle.btn-default:hover,
.btn-default:active:focus,
.btn-default.active:focus,
.open > .dropdown-toggle.btn-default:focus,
.btn-default:active.focus,
.btn-default.active.focus,
.open > .dropdown-toggle.btn-default.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  background-image: none;
}
.btn-default.disabled:hover,
.btn-default[disabled]:hover,
fieldset[disabled] .btn-default:hover,
.btn-default.disabled:focus,
.btn-default[disabled]:focus,
fieldset[disabled] .btn-default:focus,
.btn-default.disabled.focus,
.btn-default[disabled].focus,
fieldset[disabled] .btn-default.focus {
  background-color: #fff;
  border-color: #ccc;
}
.btn-default .badge {
  color: #fff;
  background-color: #333;
}
.btn-primary {
  color: #fff;
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary:focus,
.btn-primary.focus {
  color: #fff;
  background-color: #286090;
  border-color: #122b40;
}
.btn-primary:hover {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active:hover,
.btn-primary.active:hover,
.open > .dropdown-toggle.btn-primary:hover,
.btn-primary:active:focus,
.btn-primary.active:focus,
.open > .dropdown-toggle.btn-primary:focus,
.btn-primary:active.focus,
.btn-primary.active.focus,
.open > .dropdown-toggle.btn-primary.focus {
  color: #fff;
  background-color: #204d74;
  border-color: #122b40;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  background-image: none;
}
.btn-primary.disabled:hover,
.btn-primary[disabled]:hover,
fieldset[disabled] .btn-primary:hover,
.btn-primary.disabled:focus,
.btn-primary[disabled]:focus,
fieldset[disabled] .btn-primary:focus,
.btn-primary.disabled.focus,
.btn-primary[disabled].focus,
fieldset[disabled] .btn-primary.focus {
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary .badge {
  color: #337ab7;
  background-color: #fff;
}
.btn-success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success:focus,
.btn-success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.btn-success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active:hover,
.btn-success.active:hover,
.open > .dropdown-toggle.btn-success:hover,
.btn-success:active:focus,
.btn-success.active:focus,
.open > .dropdown-toggle.btn-success:focus,
.btn-success:active.focus,
.btn-success.active.focus,
.open > .dropdown-toggle.btn-success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  background-image: none;
}
.btn-success.disabled:hover,
.btn-success[disabled]:hover,
fieldset[disabled] .btn-success:hover,
.btn-success.disabled:focus,
.btn-success[disabled]:focus,
fieldset[disabled] .btn-success:focus,
.btn-success.disabled.focus,
.btn-success[disabled].focus,
fieldset[disabled] .btn-success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.btn-info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info:focus,
.btn-info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.btn-info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active:hover,
.btn-info.active:hover,
.open > .dropdown-toggle.btn-info:hover,
.btn-info:active:focus,
.btn-info.active:focus,
.open > .dropdown-toggle.btn-info:focus,
.btn-info:active.focus,
.btn-info.active.focus,
.open > .dropdown-toggle.btn-info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  background-image: none;
}
.btn-info.disabled:hover,
.btn-info[disabled]:hover,
fieldset[disabled] .btn-info:hover,
.btn-info.disabled:focus,
.btn-info[disabled]:focus,
fieldset[disabled] .btn-info:focus,
.btn-info.disabled.focus,
.btn-info[disabled].focus,
fieldset[disabled] .btn-info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.btn-warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning:focus,
.btn-warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.btn-warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active:hover,
.btn-warning.active:hover,
.open > .dropdown-toggle.btn-warning:hover,
.btn-warning:active:focus,
.btn-warning.active:focus,
.open > .dropdown-toggle.btn-warning:focus,
.btn-warning:active.focus,
.btn-warning.active.focus,
.open > .dropdown-toggle.btn-warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  background-image: none;
}
.btn-warning.disabled:hover,
.btn-warning[disabled]:hover,
fieldset[disabled] .btn-warning:hover,
.btn-warning.disabled:focus,
.btn-warning[disabled]:focus,
fieldset[disabled] .btn-warning:focus,
.btn-warning.disabled.focus,
.btn-warning[disabled].focus,
fieldset[disabled] .btn-warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.btn-danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger:focus,
.btn-danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.btn-danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active:hover,
.btn-danger.active:hover,
.open > .dropdown-toggle.btn-danger:hover,
.btn-danger:active:focus,
.btn-danger.active:focus,
.open > .dropdown-toggle.btn-danger:focus,
.btn-danger:active.focus,
.btn-danger.active.focus,
.open > .dropdown-toggle.btn-danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  background-image: none;
}
.btn-danger.disabled:hover,
.btn-danger[disabled]:hover,
fieldset[disabled] .btn-danger:hover,
.btn-danger.disabled:focus,
.btn-danger[disabled]:focus,
fieldset[disabled] .btn-danger:focus,
.btn-danger.disabled.focus,
.btn-danger[disabled].focus,
fieldset[disabled] .btn-danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger .badge {
  color: #d9534f;
  background-color: #fff;
}
.btn-link {
  color: #337ab7;
  font-weight: normal;
  border-radius: 0;
}
.btn-link,
.btn-link:active,
.btn-link.active,
.btn-link[disabled],
fieldset[disabled] .btn-link {
  background-color: transparent;
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn-link,
.btn-link:hover,
.btn-link:focus,
.btn-link:active {
  border-color: transparent;
}
.btn-link:hover,
.btn-link:focus {
  color: #23527c;
  text-decoration: underline;
  background-color: transparent;
}
.btn-link[disabled]:hover,
fieldset[disabled] .btn-link:hover,
.btn-link[disabled]:focus,
fieldset[disabled] .btn-link:focus {
  color: #777777;
  text-decoration: none;
}
.btn-lg,
.btn-group-lg > .btn {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.btn-sm,
.btn-group-sm > .btn {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-xs,
.btn-group-xs > .btn {
  padding: 1px 5px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-block {
  display: block;
  width: 100%;
}
.btn-block + .btn-block {
  margin-top: 5px;
}
input[type="submit"].btn-block,
input[type="reset"].btn-block,
input[type="button"].btn-block {
  width: 100%;
}
.fade {
  opacity: 0;
  -webkit-transition: opacity 0.15s linear;
  -o-transition: opacity 0.15s linear;
  transition: opacity 0.15s linear;
}
.fade.in {
  opacity: 1;
}
.collapse {
  display: none;
}
.collapse.in {
  display: block;
}
tr.collapse.in {
  display: table-row;
}
tbody.collapse.in {
  display: table-row-group;
}
.collapsing {
  position: relative;
  height: 0;
  overflow: hidden;
  -webkit-transition-property: height, visibility;
  transition-property: height, visibility;
  -webkit-transition-duration: 0.35s;
  transition-duration: 0.35s;
  -webkit-transition-timing-function: ease;
  transition-timing-function: ease;
}
.caret {
  display: inline-block;
  width: 0;
  height: 0;
  margin-left: 2px;
  vertical-align: middle;
  border-top: 4px dashed;
  border-top: 4px solid \9;
  border-right: 4px solid transparent;
  border-left: 4px solid transparent;
}
.dropup,
.dropdown {
  position: relative;
}
.dropdown-toggle:focus {
  outline: 0;
}
.dropdown-menu {
  position: absolute;
  top: 100%;
  left: 0;
  z-index: 1000;
  display: none;
  float: left;
  min-width: 160px;
  padding: 5px 0;
  margin: 2px 0 0;
  list-style: none;
  font-size: 13px;
  text-align: left;
  background-color: #fff;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.15);
  border-radius: 2px;
  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  background-clip: padding-box;
}
.dropdown-menu.pull-right {
  right: 0;
  left: auto;
}
.dropdown-menu .divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.dropdown-menu > li > a {
  display: block;
  padding: 3px 20px;
  clear: both;
  font-weight: normal;
  line-height: 1.42857143;
  color: #333333;
  white-space: nowrap;
}
.dropdown-menu > li > a:hover,
.dropdown-menu > li > a:focus {
  text-decoration: none;
  color: #262626;
  background-color: #f5f5f5;
}
.dropdown-menu > .active > a,
.dropdown-menu > .active > a:hover,
.dropdown-menu > .active > a:focus {
  color: #fff;
  text-decoration: none;
  outline: 0;
  background-color: #337ab7;
}
.dropdown-menu > .disabled > a,
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  color: #777777;
}
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  text-decoration: none;
  background-color: transparent;
  background-image: none;
  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
  cursor: not-allowed;
}
.open > .dropdown-menu {
  display: block;
}
.open > a {
  outline: 0;
}
.dropdown-menu-right {
  left: auto;
  right: 0;
}
.dropdown-menu-left {
  left: 0;
  right: auto;
}
.dropdown-header {
  display: block;
  padding: 3px 20px;
  font-size: 12px;
  line-height: 1.42857143;
  color: #777777;
  white-space: nowrap;
}
.dropdown-backdrop {
  position: fixed;
  left: 0;
  right: 0;
  bottom: 0;
  top: 0;
  z-index: 990;
}
.pull-right > .dropdown-menu {
  right: 0;
  left: auto;
}
.dropup .caret,
.navbar-fixed-bottom .dropdown .caret {
  border-top: 0;
  border-bottom: 4px dashed;
  border-bottom: 4px solid \9;
  content: "";
}
.dropup .dropdown-menu,
.navbar-fixed-bottom .dropdown .dropdown-menu {
  top: auto;
  bottom: 100%;
  margin-bottom: 2px;
}
@media (min-width: 541px) {
  .navbar-right .dropdown-menu {
    left: auto;
    right: 0;
  }
  .navbar-right .dropdown-menu-left {
    left: 0;
    right: auto;
  }
}
.btn-group,
.btn-group-vertical {
  position: relative;
  display: inline-block;
  vertical-align: middle;
}
.btn-group > .btn,
.btn-group-vertical > .btn {
  position: relative;
  float: left;
}
.btn-group > .btn:hover,
.btn-group-vertical > .btn:hover,
.btn-group > .btn:focus,
.btn-group-vertical > .btn:focus,
.btn-group > .btn:active,
.btn-group-vertical > .btn:active,
.btn-group > .btn.active,
.btn-group-vertical > .btn.active {
  z-index: 2;
}
.btn-group .btn + .btn,
.btn-group .btn + .btn-group,
.btn-group .btn-group + .btn,
.btn-group .btn-group + .btn-group {
  margin-left: -1px;
}
.btn-toolbar {
  margin-left: -5px;
}
.btn-toolbar .btn,
.btn-toolbar .btn-group,
.btn-toolbar .input-group {
  float: left;
}
.btn-toolbar > .btn,
.btn-toolbar > .btn-group,
.btn-toolbar > .input-group {
  margin-left: 5px;
}
.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
  border-radius: 0;
}
.btn-group > .btn:first-child {
  margin-left: 0;
}
.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn:last-child:not(:first-child),
.btn-group > .dropdown-toggle:not(:first-child) {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group > .btn-group {
  float: left;
}
.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group .dropdown-toggle:active,
.btn-group.open .dropdown-toggle {
  outline: 0;
}
.btn-group > .btn + .dropdown-toggle {
  padding-left: 8px;
  padding-right: 8px;
}
.btn-group > .btn-lg + .dropdown-toggle {
  padding-left: 12px;
  padding-right: 12px;
}
.btn-group.open .dropdown-toggle {
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn-group.open .dropdown-toggle.btn-link {
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn .caret {
  margin-left: 0;
}
.btn-lg .caret {
  border-width: 5px 5px 0;
  border-bottom-width: 0;
}
.dropup .btn-lg .caret {
  border-width: 0 5px 5px;
}
.btn-group-vertical > .btn,
.btn-group-vertical > .btn-group,
.btn-group-vertical > .btn-group > .btn {
  display: block;
  float: none;
  width: 100%;
  max-width: 100%;
}
.btn-group-vertical > .btn-group > .btn {
  float: none;
}
.btn-group-vertical > .btn + .btn,
.btn-group-vertical > .btn + .btn-group,
.btn-group-vertical > .btn-group + .btn,
.btn-group-vertical > .btn-group + .btn-group {
  margin-top: -1px;
  margin-left: 0;
}
.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.btn-group-vertical > .btn:first-child:not(:last-child) {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn:last-child:not(:first-child) {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.btn-group-justified {
  display: table;
  width: 100%;
  table-layout: fixed;
  border-collapse: separate;
}
.btn-group-justified > .btn,
.btn-group-justified > .btn-group {
  float: none;
  display: table-cell;
  width: 1%;
}
.btn-group-justified > .btn-group .btn {
  width: 100%;
}
.btn-group-justified > .btn-group .dropdown-menu {
  left: auto;
}
[data-toggle="buttons"] > .btn input[type="radio"],
[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
[data-toggle="buttons"] > .btn input[type="checkbox"],
[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
  position: absolute;
  clip: rect(0, 0, 0, 0);
  pointer-events: none;
}
.input-group {
  position: relative;
  display: table;
  border-collapse: separate;
}
.input-group[class*="col-"] {
  float: none;
  padding-left: 0;
  padding-right: 0;
}
.input-group .form-control {
  position: relative;
  z-index: 2;
  float: left;
  width: 100%;
  margin-bottom: 0;
}
.input-group .form-control:focus {
  z-index: 3;
}
.input-group-lg > .form-control,
.input-group-lg > .input-group-addon,
.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-group-lg > .form-control,
select.input-group-lg > .input-group-addon,
select.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  line-height: 45px;
}
textarea.input-group-lg > .form-control,
textarea.input-group-lg > .input-group-addon,
textarea.input-group-lg > .input-group-btn > .btn,
select[multiple].input-group-lg > .form-control,
select[multiple].input-group-lg > .input-group-addon,
select[multiple].input-group-lg > .input-group-btn > .btn {
  height: auto;
}
.input-group-sm > .form-control,
.input-group-sm > .input-group-addon,
.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-group-sm > .form-control,
select.input-group-sm > .input-group-addon,
select.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  line-height: 30px;
}
textarea.input-group-sm > .form-control,
textarea.input-group-sm > .input-group-addon,
textarea.input-group-sm > .input-group-btn > .btn,
select[multiple].input-group-sm > .form-control,
select[multiple].input-group-sm > .input-group-addon,
select[multiple].input-group-sm > .input-group-btn > .btn {
  height: auto;
}
.input-group-addon,
.input-group-btn,
.input-group .form-control {
  display: table-cell;
}
.input-group-addon:not(:first-child):not(:last-child),
.input-group-btn:not(:first-child):not(:last-child),
.input-group .form-control:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.input-group-addon,
.input-group-btn {
  width: 1%;
  white-space: nowrap;
  vertical-align: middle;
}
.input-group-addon {
  padding: 6px 12px;
  font-size: 13px;
  font-weight: normal;
  line-height: 1;
  color: #555555;
  text-align: center;
  background-color: #eeeeee;
  border: 1px solid #ccc;
  border-radius: 2px;
}
.input-group-addon.input-sm {
  padding: 5px 10px;
  font-size: 12px;
  border-radius: 1px;
}
.input-group-addon.input-lg {
  padding: 10px 16px;
  font-size: 17px;
  border-radius: 3px;
}
.input-group-addon input[type="radio"],
.input-group-addon input[type="checkbox"] {
  margin-top: 0;
}
.input-group .form-control:first-child,
.input-group-addon:first-child,
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group > .btn,
.input-group-btn:first-child > .dropdown-toggle,
.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.input-group-addon:first-child {
  border-right: 0;
}
.input-group .form-control:last-child,
.input-group-addon:last-child,
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group > .btn,
.input-group-btn:last-child > .dropdown-toggle,
.input-group-btn:first-child > .btn:not(:first-child),
.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.input-group-addon:last-child {
  border-left: 0;
}
.input-group-btn {
  position: relative;
  font-size: 0;
  white-space: nowrap;
}
.input-group-btn > .btn {
  position: relative;
}
.input-group-btn > .btn + .btn {
  margin-left: -1px;
}
.input-group-btn > .btn:hover,
.input-group-btn > .btn:focus,
.input-group-btn > .btn:active {
  z-index: 2;
}
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group {
  margin-right: -1px;
}
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group {
  z-index: 2;
  margin-left: -1px;
}
.nav {
  margin-bottom: 0;
  padding-left: 0;
  list-style: none;
}
.nav > li {
  position: relative;
  display: block;
}
.nav > li > a {
  position: relative;
  display: block;
  padding: 10px 15px;
}
.nav > li > a:hover,
.nav > li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.nav > li.disabled > a {
  color: #777777;
}
.nav > li.disabled > a:hover,
.nav > li.disabled > a:focus {
  color: #777777;
  text-decoration: none;
  background-color: transparent;
  cursor: not-allowed;
}
.nav .open > a,
.nav .open > a:hover,
.nav .open > a:focus {
  background-color: #eeeeee;
  border-color: #337ab7;
}
.nav .nav-divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.nav > li > a > img {
  max-width: none;
}
.nav-tabs {
  border-bottom: 1px solid #ddd;
}
.nav-tabs > li {
  float: left;
  margin-bottom: -1px;
}
.nav-tabs > li > a {
  margin-right: 2px;
  line-height: 1.42857143;
  border: 1px solid transparent;
  border-radius: 2px 2px 0 0;
}
.nav-tabs > li > a:hover {
  border-color: #eeeeee #eeeeee #ddd;
}
.nav-tabs > li.active > a,
.nav-tabs > li.active > a:hover,
.nav-tabs > li.active > a:focus {
  color: #555555;
  background-color: #fff;
  border: 1px solid #ddd;
  border-bottom-color: transparent;
  cursor: default;
}
.nav-tabs.nav-justified {
  width: 100%;
  border-bottom: 0;
}
.nav-tabs.nav-justified > li {
  float: none;
}
.nav-tabs.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-tabs.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-tabs.nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs.nav-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs.nav-justified > .active > a,
.nav-tabs.nav-justified > .active > a:hover,
.nav-tabs.nav-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs.nav-justified > .active > a,
  .nav-tabs.nav-justified > .active > a:hover,
  .nav-tabs.nav-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.nav-pills > li {
  float: left;
}
.nav-pills > li > a {
  border-radius: 2px;
}
.nav-pills > li + li {
  margin-left: 2px;
}
.nav-pills > li.active > a,
.nav-pills > li.active > a:hover,
.nav-pills > li.active > a:focus {
  color: #fff;
  background-color: #337ab7;
}
.nav-stacked > li {
  float: none;
}
.nav-stacked > li + li {
  margin-top: 2px;
  margin-left: 0;
}
.nav-justified {
  width: 100%;
}
.nav-justified > li {
  float: none;
}
.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs-justified {
  border-bottom: 0;
}
.nav-tabs-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs-justified > .active > a,
.nav-tabs-justified > .active > a:hover,
.nav-tabs-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs-justified > .active > a,
  .nav-tabs-justified > .active > a:hover,
  .nav-tabs-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.tab-content > .tab-pane {
  display: none;
}
.tab-content > .active {
  display: block;
}
.nav-tabs .dropdown-menu {
  margin-top: -1px;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar {
  position: relative;
  min-height: 30px;
  margin-bottom: 18px;
  border: 1px solid transparent;
}
@media (min-width: 541px) {
  .navbar {
    border-radius: 2px;
  }
}
@media (min-width: 541px) {
  .navbar-header {
    float: left;
  }
}
.navbar-collapse {
  overflow-x: visible;
  padding-right: 0px;
  padding-left: 0px;
  border-top: 1px solid transparent;
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
  -webkit-overflow-scrolling: touch;
}
.navbar-collapse.in {
  overflow-y: auto;
}
@media (min-width: 541px) {
  .navbar-collapse {
    width: auto;
    border-top: 0;
    box-shadow: none;
  }
  .navbar-collapse.collapse {
    display: block !important;
    height: auto !important;
    padding-bottom: 0;
    overflow: visible !important;
  }
  .navbar-collapse.in {
    overflow-y: visible;
  }
  .navbar-fixed-top .navbar-collapse,
  .navbar-static-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    padding-left: 0;
    padding-right: 0;
  }
}
.navbar-fixed-top .navbar-collapse,
.navbar-fixed-bottom .navbar-collapse {
  max-height: 340px;
}
@media (max-device-width: 540px) and (orientation: landscape) {
  .navbar-fixed-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    max-height: 200px;
  }
}
.container > .navbar-header,
.container-fluid > .navbar-header,
.container > .navbar-collapse,
.container-fluid > .navbar-collapse {
  margin-right: 0px;
  margin-left: 0px;
}
@media (min-width: 541px) {
  .container > .navbar-header,
  .container-fluid > .navbar-header,
  .container > .navbar-collapse,
  .container-fluid > .navbar-collapse {
    margin-right: 0;
    margin-left: 0;
  }
}
.navbar-static-top {
  z-index: 1000;
  border-width: 0 0 1px;
}
@media (min-width: 541px) {
  .navbar-static-top {
    border-radius: 0;
  }
}
.navbar-fixed-top,
.navbar-fixed-bottom {
  position: fixed;
  right: 0;
  left: 0;
  z-index: 1030;
}
@media (min-width: 541px) {
  .navbar-fixed-top,
  .navbar-fixed-bottom {
    border-radius: 0;
  }
}
.navbar-fixed-top {
  top: 0;
  border-width: 0 0 1px;
}
.navbar-fixed-bottom {
  bottom: 0;
  margin-bottom: 0;
  border-width: 1px 0 0;
}
.navbar-brand {
  float: left;
  padding: 6px 0px;
  font-size: 17px;
  line-height: 18px;
  height: 30px;
}
.navbar-brand:hover,
.navbar-brand:focus {
  text-decoration: none;
}
.navbar-brand > img {
  display: block;
}
@media (min-width: 541px) {
  .navbar > .container .navbar-brand,
  .navbar > .container-fluid .navbar-brand {
    margin-left: 0px;
  }
}
.navbar-toggle {
  position: relative;
  float: right;
  margin-right: 0px;
  padding: 9px 10px;
  margin-top: -2px;
  margin-bottom: -2px;
  background-color: transparent;
  background-image: none;
  border: 1px solid transparent;
  border-radius: 2px;
}
.navbar-toggle:focus {
  outline: 0;
}
.navbar-toggle .icon-bar {
  display: block;
  width: 22px;
  height: 2px;
  border-radius: 1px;
}
.navbar-toggle .icon-bar + .icon-bar {
  margin-top: 4px;
}
@media (min-width: 541px) {
  .navbar-toggle {
    display: none;
  }
}
.navbar-nav {
  margin: 3px 0px;
}
.navbar-nav > li > a {
  padding-top: 10px;
  padding-bottom: 10px;
  line-height: 18px;
}
@media (max-width: 540px) {
  .navbar-nav .open .dropdown-menu {
    position: static;
    float: none;
    width: auto;
    margin-top: 0;
    background-color: transparent;
    border: 0;
    box-shadow: none;
  }
  .navbar-nav .open .dropdown-menu > li > a,
  .navbar-nav .open .dropdown-menu .dropdown-header {
    padding: 5px 15px 5px 25px;
  }
  .navbar-nav .open .dropdown-menu > li > a {
    line-height: 18px;
  }
  .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-nav .open .dropdown-menu > li > a:focus {
    background-image: none;
  }
}
@media (min-width: 541px) {
  .navbar-nav {
    float: left;
    margin: 0;
  }
  .navbar-nav > li {
    float: left;
  }
  .navbar-nav > li > a {
    padding-top: 6px;
    padding-bottom: 6px;
  }
}
.navbar-form {
  margin-left: 0px;
  margin-right: 0px;
  padding: 10px 0px;
  border-top: 1px solid transparent;
  border-bottom: 1px solid transparent;
  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  margin-top: -1px;
  margin-bottom: -1px;
}
@media (min-width: 768px) {
  .navbar-form .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .navbar-form .form-control-static {
    display: inline-block;
  }
  .navbar-form .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .navbar-form .input-group .input-group-addon,
  .navbar-form .input-group .input-group-btn,
  .navbar-form .input-group .form-control {
    width: auto;
  }
  .navbar-form .input-group > .form-control {
    width: 100%;
  }
  .navbar-form .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio,
  .navbar-form .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio label,
  .navbar-form .checkbox label {
    padding-left: 0;
  }
  .navbar-form .radio input[type="radio"],
  .navbar-form .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .navbar-form .has-feedback .form-control-feedback {
    top: 0;
  }
}
@media (max-width: 540px) {
  .navbar-form .form-group {
    margin-bottom: 5px;
  }
  .navbar-form .form-group:last-child {
    margin-bottom: 0;
  }
}
@media (min-width: 541px) {
  .navbar-form {
    width: auto;
    border: 0;
    margin-left: 0;
    margin-right: 0;
    padding-top: 0;
    padding-bottom: 0;
    -webkit-box-shadow: none;
    box-shadow: none;
  }
}
.navbar-nav > li > .dropdown-menu {
  margin-top: 0;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
  margin-bottom: 0;
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.navbar-btn {
  margin-top: -1px;
  margin-bottom: -1px;
}
.navbar-btn.btn-sm {
  margin-top: 0px;
  margin-bottom: 0px;
}
.navbar-btn.btn-xs {
  margin-top: 4px;
  margin-bottom: 4px;
}
.navbar-text {
  margin-top: 6px;
  margin-bottom: 6px;
}
@media (min-width: 541px) {
  .navbar-text {
    float: left;
    margin-left: 0px;
    margin-right: 0px;
  }
}
@media (min-width: 541px) {
  .navbar-left {
    float: left !important;
    float: left;
  }
  .navbar-right {
    float: right !important;
    float: right;
    margin-right: 0px;
  }
  .navbar-right ~ .navbar-right {
    margin-right: 0;
  }
}
.navbar-default {
  background-color: #f8f8f8;
  border-color: #e7e7e7;
}
.navbar-default .navbar-brand {
  color: #777;
}
.navbar-default .navbar-brand:hover,
.navbar-default .navbar-brand:focus {
  color: #5e5e5e;
  background-color: transparent;
}
.navbar-default .navbar-text {
  color: #777;
}
.navbar-default .navbar-nav > li > a {
  color: #777;
}
.navbar-default .navbar-nav > li > a:hover,
.navbar-default .navbar-nav > li > a:focus {
  color: #333;
  background-color: transparent;
}
.navbar-default .navbar-nav > .active > a,
.navbar-default .navbar-nav > .active > a:hover,
.navbar-default .navbar-nav > .active > a:focus {
  color: #555;
  background-color: #e7e7e7;
}
.navbar-default .navbar-nav > .disabled > a,
.navbar-default .navbar-nav > .disabled > a:hover,
.navbar-default .navbar-nav > .disabled > a:focus {
  color: #ccc;
  background-color: transparent;
}
.navbar-default .navbar-toggle {
  border-color: #ddd;
}
.navbar-default .navbar-toggle:hover,
.navbar-default .navbar-toggle:focus {
  background-color: #ddd;
}
.navbar-default .navbar-toggle .icon-bar {
  background-color: #888;
}
.navbar-default .navbar-collapse,
.navbar-default .navbar-form {
  border-color: #e7e7e7;
}
.navbar-default .navbar-nav > .open > a,
.navbar-default .navbar-nav > .open > a:hover,
.navbar-default .navbar-nav > .open > a:focus {
  background-color: #e7e7e7;
  color: #555;
}
@media (max-width: 540px) {
  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
    color: #777;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #333;
    background-color: transparent;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #555;
    background-color: #e7e7e7;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #ccc;
    background-color: transparent;
  }
}
.navbar-default .navbar-link {
  color: #777;
}
.navbar-default .navbar-link:hover {
  color: #333;
}
.navbar-default .btn-link {
  color: #777;
}
.navbar-default .btn-link:hover,
.navbar-default .btn-link:focus {
  color: #333;
}
.navbar-default .btn-link[disabled]:hover,
fieldset[disabled] .navbar-default .btn-link:hover,
.navbar-default .btn-link[disabled]:focus,
fieldset[disabled] .navbar-default .btn-link:focus {
  color: #ccc;
}
.navbar-inverse {
  background-color: #222;
  border-color: #080808;
}
.navbar-inverse .navbar-brand {
  color: #9d9d9d;
}
.navbar-inverse .navbar-brand:hover,
.navbar-inverse .navbar-brand:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-text {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a:hover,
.navbar-inverse .navbar-nav > li > a:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-nav > .active > a,
.navbar-inverse .navbar-nav > .active > a:hover,
.navbar-inverse .navbar-nav > .active > a:focus {
  color: #fff;
  background-color: #080808;
}
.navbar-inverse .navbar-nav > .disabled > a,
.navbar-inverse .navbar-nav > .disabled > a:hover,
.navbar-inverse .navbar-nav > .disabled > a:focus {
  color: #444;
  background-color: transparent;
}
.navbar-inverse .navbar-toggle {
  border-color: #333;
}
.navbar-inverse .navbar-toggle:hover,
.navbar-inverse .navbar-toggle:focus {
  background-color: #333;
}
.navbar-inverse .navbar-toggle .icon-bar {
  background-color: #fff;
}
.navbar-inverse .navbar-collapse,
.navbar-inverse .navbar-form {
  border-color: #101010;
}
.navbar-inverse .navbar-nav > .open > a,
.navbar-inverse .navbar-nav > .open > a:hover,
.navbar-inverse .navbar-nav > .open > a:focus {
  background-color: #080808;
  color: #fff;
}
@media (max-width: 540px) {
  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
    border-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
    color: #9d9d9d;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #fff;
    background-color: transparent;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #fff;
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #444;
    background-color: transparent;
  }
}
.navbar-inverse .navbar-link {
  color: #9d9d9d;
}
.navbar-inverse .navbar-link:hover {
  color: #fff;
}
.navbar-inverse .btn-link {
  color: #9d9d9d;
}
.navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link:focus {
  color: #fff;
}
.navbar-inverse .btn-link[disabled]:hover,
fieldset[disabled] .navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link[disabled]:focus,
fieldset[disabled] .navbar-inverse .btn-link:focus {
  color: #444;
}
.breadcrumb {
  padding: 8px 15px;
  margin-bottom: 18px;
  list-style: none;
  background-color: #f5f5f5;
  border-radius: 2px;
}
.breadcrumb > li {
  display: inline-block;
}
.breadcrumb > li + li:before {
  content: "/\00a0";
  padding: 0 5px;
  color: #5e5e5e;
}
.breadcrumb > .active {
  color: #777777;
}
.pagination {
  display: inline-block;
  padding-left: 0;
  margin: 18px 0;
  border-radius: 2px;
}
.pagination > li {
  display: inline;
}
.pagination > li > a,
.pagination > li > span {
  position: relative;
  float: left;
  padding: 6px 12px;
  line-height: 1.42857143;
  text-decoration: none;
  color: #337ab7;
  background-color: #fff;
  border: 1px solid #ddd;
  margin-left: -1px;
}
.pagination > li:first-child > a,
.pagination > li:first-child > span {
  margin-left: 0;
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.pagination > li:last-child > a,
.pagination > li:last-child > span {
  border-bottom-right-radius: 2px;
  border-top-right-radius: 2px;
}
.pagination > li > a:hover,
.pagination > li > span:hover,
.pagination > li > a:focus,
.pagination > li > span:focus {
  z-index: 2;
  color: #23527c;
  background-color: #eeeeee;
  border-color: #ddd;
}
.pagination > .active > a,
.pagination > .active > span,
.pagination > .active > a:hover,
.pagination > .active > span:hover,
.pagination > .active > a:focus,
.pagination > .active > span:focus {
  z-index: 3;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
  cursor: default;
}
.pagination > .disabled > span,
.pagination > .disabled > span:hover,
.pagination > .disabled > span:focus,
.pagination > .disabled > a,
.pagination > .disabled > a:hover,
.pagination > .disabled > a:focus {
  color: #777777;
  background-color: #fff;
  border-color: #ddd;
  cursor: not-allowed;
}
.pagination-lg > li > a,
.pagination-lg > li > span {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.pagination-lg > li:first-child > a,
.pagination-lg > li:first-child > span {
  border-bottom-left-radius: 3px;
  border-top-left-radius: 3px;
}
.pagination-lg > li:last-child > a,
.pagination-lg > li:last-child > span {
  border-bottom-right-radius: 3px;
  border-top-right-radius: 3px;
}
.pagination-sm > li > a,
.pagination-sm > li > span {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.pagination-sm > li:first-child > a,
.pagination-sm > li:first-child > span {
  border-bottom-left-radius: 1px;
  border-top-left-radius: 1px;
}
.pagination-sm > li:last-child > a,
.pagination-sm > li:last-child > span {
  border-bottom-right-radius: 1px;
  border-top-right-radius: 1px;
}
.pager {
  padding-left: 0;
  margin: 18px 0;
  list-style: none;
  text-align: center;
}
.pager li {
  display: inline;
}
.pager li > a,
.pager li > span {
  display: inline-block;
  padding: 5px 14px;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 15px;
}
.pager li > a:hover,
.pager li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.pager .next > a,
.pager .next > span {
  float: right;
}
.pager .previous > a,
.pager .previous > span {
  float: left;
}
.pager .disabled > a,
.pager .disabled > a:hover,
.pager .disabled > a:focus,
.pager .disabled > span {
  color: #777777;
  background-color: #fff;
  cursor: not-allowed;
}
.label {
  display: inline;
  padding: .2em .6em .3em;
  font-size: 75%;
  font-weight: bold;
  line-height: 1;
  color: #fff;
  text-align: center;
  white-space: nowrap;
  vertical-align: baseline;
  border-radius: .25em;
}
a.label:hover,
a.label:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.label:empty {
  display: none;
}
.btn .label {
  position: relative;
  top: -1px;
}
.label-default {
  background-color: #777777;
}
.label-default[href]:hover,
.label-default[href]:focus {
  background-color: #5e5e5e;
}
.label-primary {
  background-color: #337ab7;
}
.label-primary[href]:hover,
.label-primary[href]:focus {
  background-color: #286090;
}
.label-success {
  background-color: #5cb85c;
}
.label-success[href]:hover,
.label-success[href]:focus {
  background-color: #449d44;
}
.label-info {
  background-color: #5bc0de;
}
.label-info[href]:hover,
.label-info[href]:focus {
  background-color: #31b0d5;
}
.label-warning {
  background-color: #f0ad4e;
}
.label-warning[href]:hover,
.label-warning[href]:focus {
  background-color: #ec971f;
}
.label-danger {
  background-color: #d9534f;
}
.label-danger[href]:hover,
.label-danger[href]:focus {
  background-color: #c9302c;
}
.badge {
  display: inline-block;
  min-width: 10px;
  padding: 3px 7px;
  font-size: 12px;
  font-weight: bold;
  color: #fff;
  line-height: 1;
  vertical-align: middle;
  white-space: nowrap;
  text-align: center;
  background-color: #777777;
  border-radius: 10px;
}
.badge:empty {
  display: none;
}
.btn .badge {
  position: relative;
  top: -1px;
}
.btn-xs .badge,
.btn-group-xs > .btn .badge {
  top: 0;
  padding: 1px 5px;
}
a.badge:hover,
a.badge:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.list-group-item.active > .badge,
.nav-pills > .active > a > .badge {
  color: #337ab7;
  background-color: #fff;
}
.list-group-item > .badge {
  float: right;
}
.list-group-item > .badge + .badge {
  margin-right: 5px;
}
.nav-pills > li > a > .badge {
  margin-left: 3px;
}
.jumbotron {
  padding-top: 30px;
  padding-bottom: 30px;
  margin-bottom: 30px;
  color: inherit;
  background-color: #eeeeee;
}
.jumbotron h1,
.jumbotron .h1 {
  color: inherit;
}
.jumbotron p {
  margin-bottom: 15px;
  font-size: 20px;
  font-weight: 200;
}
.jumbotron > hr {
  border-top-color: #d5d5d5;
}
.container .jumbotron,
.container-fluid .jumbotron {
  border-radius: 3px;
  padding-left: 0px;
  padding-right: 0px;
}
.jumbotron .container {
  max-width: 100%;
}
@media screen and (min-width: 768px) {
  .jumbotron {
    padding-top: 48px;
    padding-bottom: 48px;
  }
  .container .jumbotron,
  .container-fluid .jumbotron {
    padding-left: 60px;
    padding-right: 60px;
  }
  .jumbotron h1,
  .jumbotron .h1 {
    font-size: 59px;
  }
}
.thumbnail {
  display: block;
  padding: 4px;
  margin-bottom: 18px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: border 0.2s ease-in-out;
  -o-transition: border 0.2s ease-in-out;
  transition: border 0.2s ease-in-out;
}
.thumbnail > img,
.thumbnail a > img {
  margin-left: auto;
  margin-right: auto;
}
a.thumbnail:hover,
a.thumbnail:focus,
a.thumbnail.active {
  border-color: #337ab7;
}
.thumbnail .caption {
  padding: 9px;
  color: #000;
}
.alert {
  padding: 15px;
  margin-bottom: 18px;
  border: 1px solid transparent;
  border-radius: 2px;
}
.alert h4 {
  margin-top: 0;
  color: inherit;
}
.alert .alert-link {
  font-weight: bold;
}
.alert > p,
.alert > ul {
  margin-bottom: 0;
}
.alert > p + p {
  margin-top: 5px;
}
.alert-dismissable,
.alert-dismissible {
  padding-right: 35px;
}
.alert-dismissable .close,
.alert-dismissible .close {
  position: relative;
  top: -2px;
  right: -21px;
  color: inherit;
}
.alert-success {
  background-color: #dff0d8;
  border-color: #d6e9c6;
  color: #3c763d;
}
.alert-success hr {
  border-top-color: #c9e2b3;
}
.alert-success .alert-link {
  color: #2b542c;
}
.alert-info {
  background-color: #d9edf7;
  border-color: #bce8f1;
  color: #31708f;
}
.alert-info hr {
  border-top-color: #a6e1ec;
}
.alert-info .alert-link {
  color: #245269;
}
.alert-warning {
  background-color: #fcf8e3;
  border-color: #faebcc;
  color: #8a6d3b;
}
.alert-warning hr {
  border-top-color: #f7e1b5;
}
.alert-warning .alert-link {
  color: #66512c;
}
.alert-danger {
  background-color: #f2dede;
  border-color: #ebccd1;
  color: #a94442;
}
.alert-danger hr {
  border-top-color: #e4b9c0;
}
.alert-danger .alert-link {
  color: #843534;
}
@-webkit-keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
@keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
.progress {
  overflow: hidden;
  height: 18px;
  margin-bottom: 18px;
  background-color: #f5f5f5;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
}
.progress-bar {
  float: left;
  width: 0%;
  height: 100%;
  font-size: 12px;
  line-height: 18px;
  color: #fff;
  text-align: center;
  background-color: #337ab7;
  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  -webkit-transition: width 0.6s ease;
  -o-transition: width 0.6s ease;
  transition: width 0.6s ease;
}
.progress-striped .progress-bar,
.progress-bar-striped {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-size: 40px 40px;
}
.progress.active .progress-bar,
.progress-bar.active {
  -webkit-animation: progress-bar-stripes 2s linear infinite;
  -o-animation: progress-bar-stripes 2s linear infinite;
  animation: progress-bar-stripes 2s linear infinite;
}
.progress-bar-success {
  background-color: #5cb85c;
}
.progress-striped .progress-bar-success {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-info {
  background-color: #5bc0de;
}
.progress-striped .progress-bar-info {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-warning {
  background-color: #f0ad4e;
}
.progress-striped .progress-bar-warning {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-danger {
  background-color: #d9534f;
}
.progress-striped .progress-bar-danger {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.media {
  margin-top: 15px;
}
.media:first-child {
  margin-top: 0;
}
.media,
.media-body {
  zoom: 1;
  overflow: hidden;
}
.media-body {
  width: 10000px;
}
.media-object {
  display: block;
}
.media-object.img-thumbnail {
  max-width: none;
}
.media-right,
.media > .pull-right {
  padding-left: 10px;
}
.media-left,
.media > .pull-left {
  padding-right: 10px;
}
.media-left,
.media-right,
.media-body {
  display: table-cell;
  vertical-align: top;
}
.media-middle {
  vertical-align: middle;
}
.media-bottom {
  vertical-align: bottom;
}
.media-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.media-list {
  padding-left: 0;
  list-style: none;
}
.list-group {
  margin-bottom: 20px;
  padding-left: 0;
}
.list-group-item {
  position: relative;
  display: block;
  padding: 10px 15px;
  margin-bottom: -1px;
  background-color: #fff;
  border: 1px solid #ddd;
}
.list-group-item:first-child {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
}
.list-group-item:last-child {
  margin-bottom: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
a.list-group-item,
button.list-group-item {
  color: #555;
}
a.list-group-item .list-group-item-heading,
button.list-group-item .list-group-item-heading {
  color: #333;
}
a.list-group-item:hover,
button.list-group-item:hover,
a.list-group-item:focus,
button.list-group-item:focus {
  text-decoration: none;
  color: #555;
  background-color: #f5f5f5;
}
button.list-group-item {
  width: 100%;
  text-align: left;
}
.list-group-item.disabled,
.list-group-item.disabled:hover,
.list-group-item.disabled:focus {
  background-color: #eeeeee;
  color: #777777;
  cursor: not-allowed;
}
.list-group-item.disabled .list-group-item-heading,
.list-group-item.disabled:hover .list-group-item-heading,
.list-group-item.disabled:focus .list-group-item-heading {
  color: inherit;
}
.list-group-item.disabled .list-group-item-text,
.list-group-item.disabled:hover .list-group-item-text,
.list-group-item.disabled:focus .list-group-item-text {
  color: #777777;
}
.list-group-item.active,
.list-group-item.active:hover,
.list-group-item.active:focus {
  z-index: 2;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.list-group-item.active .list-group-item-heading,
.list-group-item.active:hover .list-group-item-heading,
.list-group-item.active:focus .list-group-item-heading,
.list-group-item.active .list-group-item-heading > small,
.list-group-item.active:hover .list-group-item-heading > small,
.list-group-item.active:focus .list-group-item-heading > small,
.list-group-item.active .list-group-item-heading > .small,
.list-group-item.active:hover .list-group-item-heading > .small,
.list-group-item.active:focus .list-group-item-heading > .small {
  color: inherit;
}
.list-group-item.active .list-group-item-text,
.list-group-item.active:hover .list-group-item-text,
.list-group-item.active:focus .list-group-item-text {
  color: #c7ddef;
}
.list-group-item-success {
  color: #3c763d;
  background-color: #dff0d8;
}
a.list-group-item-success,
button.list-group-item-success {
  color: #3c763d;
}
a.list-group-item-success .list-group-item-heading,
button.list-group-item-success .list-group-item-heading {
  color: inherit;
}
a.list-group-item-success:hover,
button.list-group-item-success:hover,
a.list-group-item-success:focus,
button.list-group-item-success:focus {
  color: #3c763d;
  background-color: #d0e9c6;
}
a.list-group-item-success.active,
button.list-group-item-success.active,
a.list-group-item-success.active:hover,
button.list-group-item-success.active:hover,
a.list-group-item-success.active:focus,
button.list-group-item-success.active:focus {
  color: #fff;
  background-color: #3c763d;
  border-color: #3c763d;
}
.list-group-item-info {
  color: #31708f;
  background-color: #d9edf7;
}
a.list-group-item-info,
button.list-group-item-info {
  color: #31708f;
}
a.list-group-item-info .list-group-item-heading,
button.list-group-item-info .list-group-item-heading {
  color: inherit;
}
a.list-group-item-info:hover,
button.list-group-item-info:hover,
a.list-group-item-info:focus,
button.list-group-item-info:focus {
  color: #31708f;
  background-color: #c4e3f3;
}
a.list-group-item-info.active,
button.list-group-item-info.active,
a.list-group-item-info.active:hover,
button.list-group-item-info.active:hover,
a.list-group-item-info.active:focus,
button.list-group-item-info.active:focus {
  color: #fff;
  background-color: #31708f;
  border-color: #31708f;
}
.list-group-item-warning {
  color: #8a6d3b;
  background-color: #fcf8e3;
}
a.list-group-item-warning,
button.list-group-item-warning {
  color: #8a6d3b;
}
a.list-group-item-warning .list-group-item-heading,
button.list-group-item-warning .list-group-item-heading {
  color: inherit;
}
a.list-group-item-warning:hover,
button.list-group-item-warning:hover,
a.list-group-item-warning:focus,
button.list-group-item-warning:focus {
  color: #8a6d3b;
  background-color: #faf2cc;
}
a.list-group-item-warning.active,
button.list-group-item-warning.active,
a.list-group-item-warning.active:hover,
button.list-group-item-warning.active:hover,
a.list-group-item-warning.active:focus,
button.list-group-item-warning.active:focus {
  color: #fff;
  background-color: #8a6d3b;
  border-color: #8a6d3b;
}
.list-group-item-danger {
  color: #a94442;
  background-color: #f2dede;
}
a.list-group-item-danger,
button.list-group-item-danger {
  color: #a94442;
}
a.list-group-item-danger .list-group-item-heading,
button.list-group-item-danger .list-group-item-heading {
  color: inherit;
}
a.list-group-item-danger:hover,
button.list-group-item-danger:hover,
a.list-group-item-danger:focus,
button.list-group-item-danger:focus {
  color: #a94442;
  background-color: #ebcccc;
}
a.list-group-item-danger.active,
button.list-group-item-danger.active,
a.list-group-item-danger.active:hover,
button.list-group-item-danger.active:hover,
a.list-group-item-danger.active:focus,
button.list-group-item-danger.active:focus {
  color: #fff;
  background-color: #a94442;
  border-color: #a94442;
}
.list-group-item-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.list-group-item-text {
  margin-bottom: 0;
  line-height: 1.3;
}
.panel {
  margin-bottom: 18px;
  background-color: #fff;
  border: 1px solid transparent;
  border-radius: 2px;
  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
}
.panel-body {
  padding: 15px;
}
.panel-heading {
  padding: 10px 15px;
  border-bottom: 1px solid transparent;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel-heading > .dropdown .dropdown-toggle {
  color: inherit;
}
.panel-title {
  margin-top: 0;
  margin-bottom: 0;
  font-size: 15px;
  color: inherit;
}
.panel-title > a,
.panel-title > small,
.panel-title > .small,
.panel-title > small > a,
.panel-title > .small > a {
  color: inherit;
}
.panel-footer {
  padding: 10px 15px;
  background-color: #f5f5f5;
  border-top: 1px solid #ddd;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .list-group,
.panel > .panel-collapse > .list-group {
  margin-bottom: 0;
}
.panel > .list-group .list-group-item,
.panel > .panel-collapse > .list-group .list-group-item {
  border-width: 1px 0;
  border-radius: 0;
}
.panel > .list-group:first-child .list-group-item:first-child,
.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
  border-top: 0;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .list-group:last-child .list-group-item:last-child,
.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
  border-bottom: 0;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.panel-heading + .list-group .list-group-item:first-child {
  border-top-width: 0;
}
.list-group + .panel-footer {
  border-top-width: 0;
}
.panel > .table,
.panel > .table-responsive > .table,
.panel > .panel-collapse > .table {
  margin-bottom: 0;
}
.panel > .table caption,
.panel > .table-responsive > .table caption,
.panel > .panel-collapse > .table caption {
  padding-left: 15px;
  padding-right: 15px;
}
.panel > .table:first-child,
.panel > .table-responsive:first-child > .table:first-child {
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
  border-top-left-radius: 1px;
  border-top-right-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
  border-top-right-radius: 1px;
}
.panel > .table:last-child,
.panel > .table-responsive:last-child > .table:last-child {
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
  border-bottom-left-radius: 1px;
  border-bottom-right-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
  border-bottom-right-radius: 1px;
}
.panel > .panel-body + .table,
.panel > .panel-body + .table-responsive,
.panel > .table + .panel-body,
.panel > .table-responsive + .panel-body {
  border-top: 1px solid #ddd;
}
.panel > .table > tbody:first-child > tr:first-child th,
.panel > .table > tbody:first-child > tr:first-child td {
  border-top: 0;
}
.panel > .table-bordered,
.panel > .table-responsive > .table-bordered {
  border: 0;
}
.panel > .table-bordered > thead > tr > th:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
.panel > .table-bordered > tbody > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
.panel > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-bordered > thead > tr > td:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
.panel > .table-bordered > tbody > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
.panel > .table-bordered > tfoot > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
  border-left: 0;
}
.panel > .table-bordered > thead > tr > th:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
.panel > .table-bordered > tbody > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
.panel > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-bordered > thead > tr > td:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
.panel > .table-bordered > tbody > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
.panel > .table-bordered > tfoot > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
  border-right: 0;
}
.panel > .table-bordered > thead > tr:first-child > td,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
.panel > .table-bordered > tbody > tr:first-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
.panel > .table-bordered > thead > tr:first-child > th,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
.panel > .table-bordered > tbody > tr:first-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
  border-bottom: 0;
}
.panel > .table-bordered > tbody > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
.panel > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-bordered > tbody > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
.panel > .table-bordered > tfoot > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
  border-bottom: 0;
}
.panel > .table-responsive {
  border: 0;
  margin-bottom: 0;
}
.panel-group {
  margin-bottom: 18px;
}
.panel-group .panel {
  margin-bottom: 0;
  border-radius: 2px;
}
.panel-group .panel + .panel {
  margin-top: 5px;
}
.panel-group .panel-heading {
  border-bottom: 0;
}
.panel-group .panel-heading + .panel-collapse > .panel-body,
.panel-group .panel-heading + .panel-collapse > .list-group {
  border-top: 1px solid #ddd;
}
.panel-group .panel-footer {
  border-top: 0;
}
.panel-group .panel-footer + .panel-collapse .panel-body {
  border-bottom: 1px solid #ddd;
}
.panel-default {
  border-color: #ddd;
}
.panel-default > .panel-heading {
  color: #333333;
  background-color: #f5f5f5;
  border-color: #ddd;
}
.panel-default > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ddd;
}
.panel-default > .panel-heading .badge {
  color: #f5f5f5;
  background-color: #333333;
}
.panel-default > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ddd;
}
.panel-primary {
  border-color: #337ab7;
}
.panel-primary > .panel-heading {
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.panel-primary > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #337ab7;
}
.panel-primary > .panel-heading .badge {
  color: #337ab7;
  background-color: #fff;
}
.panel-primary > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #337ab7;
}
.panel-success {
  border-color: #d6e9c6;
}
.panel-success > .panel-heading {
  color: #3c763d;
  background-color: #dff0d8;
  border-color: #d6e9c6;
}
.panel-success > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #d6e9c6;
}
.panel-success > .panel-heading .badge {
  color: #dff0d8;
  background-color: #3c763d;
}
.panel-success > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #d6e9c6;
}
.panel-info {
  border-color: #bce8f1;
}
.panel-info > .panel-heading {
  color: #31708f;
  background-color: #d9edf7;
  border-color: #bce8f1;
}
.panel-info > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #bce8f1;
}
.panel-info > .panel-heading .badge {
  color: #d9edf7;
  background-color: #31708f;
}
.panel-info > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #bce8f1;
}
.panel-warning {
  border-color: #faebcc;
}
.panel-warning > .panel-heading {
  color: #8a6d3b;
  background-color: #fcf8e3;
  border-color: #faebcc;
}
.panel-warning > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #faebcc;
}
.panel-warning > .panel-heading .badge {
  color: #fcf8e3;
  background-color: #8a6d3b;
}
.panel-warning > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #faebcc;
}
.panel-danger {
  border-color: #ebccd1;
}
.panel-danger > .panel-heading {
  color: #a94442;
  background-color: #f2dede;
  border-color: #ebccd1;
}
.panel-danger > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ebccd1;
}
.panel-danger > .panel-heading .badge {
  color: #f2dede;
  background-color: #a94442;
}
.panel-danger > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ebccd1;
}
.embed-responsive {
  position: relative;
  display: block;
  height: 0;
  padding: 0;
  overflow: hidden;
}
.embed-responsive .embed-responsive-item,
.embed-responsive iframe,
.embed-responsive embed,
.embed-responsive object,
.embed-responsive video {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  height: 100%;
  width: 100%;
  border: 0;
}
.embed-responsive-16by9 {
  padding-bottom: 56.25%;
}
.embed-responsive-4by3 {
  padding-bottom: 75%;
}
.well {
  min-height: 20px;
  padding: 19px;
  margin-bottom: 20px;
  background-color: #f5f5f5;
  border: 1px solid #e3e3e3;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
}
.well blockquote {
  border-color: #ddd;
  border-color: rgba(0, 0, 0, 0.15);
}
.well-lg {
  padding: 24px;
  border-radius: 3px;
}
.well-sm {
  padding: 9px;
  border-radius: 1px;
}
.close {
  float: right;
  font-size: 19.5px;
  font-weight: bold;
  line-height: 1;
  color: #000;
  text-shadow: 0 1px 0 #fff;
  opacity: 0.2;
  filter: alpha(opacity=20);
}
.close:hover,
.close:focus {
  color: #000;
  text-decoration: none;
  cursor: pointer;
  opacity: 0.5;
  filter: alpha(opacity=50);
}
button.close {
  padding: 0;
  cursor: pointer;
  background: transparent;
  border: 0;
  -webkit-appearance: none;
}
.modal-open {
  overflow: hidden;
}
.modal {
  display: none;
  overflow: hidden;
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1050;
  -webkit-overflow-scrolling: touch;
  outline: 0;
}
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, -25%);
  -ms-transform: translate(0, -25%);
  -o-transform: translate(0, -25%);
  transform: translate(0, -25%);
  -webkit-transition: -webkit-transform 0.3s ease-out;
  -moz-transition: -moz-transform 0.3s ease-out;
  -o-transition: -o-transform 0.3s ease-out;
  transition: transform 0.3s ease-out;
}
.modal.in .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
.modal-open .modal {
  overflow-x: hidden;
  overflow-y: auto;
}
.modal-dialog {
  position: relative;
  width: auto;
  margin: 10px;
}
.modal-content {
  position: relative;
  background-color: #fff;
  border: 1px solid #999;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  background-clip: padding-box;
  outline: 0;
}
.modal-backdrop {
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1040;
  background-color: #000;
}
.modal-backdrop.fade {
  opacity: 0;
  filter: alpha(opacity=0);
}
.modal-backdrop.in {
  opacity: 0.5;
  filter: alpha(opacity=50);
}
.modal-header {
  padding: 15px;
  border-bottom: 1px solid #e5e5e5;
}
.modal-header .close {
  margin-top: -2px;
}
.modal-title {
  margin: 0;
  line-height: 1.42857143;
}
.modal-body {
  position: relative;
  padding: 15px;
}
.modal-footer {
  padding: 15px;
  text-align: right;
  border-top: 1px solid #e5e5e5;
}
.modal-footer .btn + .btn {
  margin-left: 5px;
  margin-bottom: 0;
}
.modal-footer .btn-group .btn + .btn {
  margin-left: -1px;
}
.modal-footer .btn-block + .btn-block {
  margin-left: 0;
}
.modal-scrollbar-measure {
  position: absolute;
  top: -9999px;
  width: 50px;
  height: 50px;
  overflow: scroll;
}
@media (min-width: 768px) {
  .modal-dialog {
    width: 600px;
    margin: 30px auto;
  }
  .modal-content {
    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
  }
  .modal-sm {
    width: 300px;
  }
}
@media (min-width: 992px) {
  .modal-lg {
    width: 900px;
  }
}
.tooltip {
  position: absolute;
  z-index: 1070;
  display: block;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 12px;
  opacity: 0;
  filter: alpha(opacity=0);
}
.tooltip.in {
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.tooltip.top {
  margin-top: -3px;
  padding: 5px 0;
}
.tooltip.right {
  margin-left: 3px;
  padding: 0 5px;
}
.tooltip.bottom {
  margin-top: 3px;
  padding: 5px 0;
}
.tooltip.left {
  margin-left: -3px;
  padding: 0 5px;
}
.tooltip-inner {
  max-width: 200px;
  padding: 3px 8px;
  color: #fff;
  text-align: center;
  background-color: #000;
  border-radius: 2px;
}
.tooltip-arrow {
  position: absolute;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.tooltip.top .tooltip-arrow {
  bottom: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-left .tooltip-arrow {
  bottom: 0;
  right: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-right .tooltip-arrow {
  bottom: 0;
  left: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.right .tooltip-arrow {
  top: 50%;
  left: 0;
  margin-top: -5px;
  border-width: 5px 5px 5px 0;
  border-right-color: #000;
}
.tooltip.left .tooltip-arrow {
  top: 50%;
  right: 0;
  margin-top: -5px;
  border-width: 5px 0 5px 5px;
  border-left-color: #000;
}
.tooltip.bottom .tooltip-arrow {
  top: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-left .tooltip-arrow {
  top: 0;
  right: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-right .tooltip-arrow {
  top: 0;
  left: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.popover {
  position: absolute;
  top: 0;
  left: 0;
  z-index: 1060;
  display: none;
  max-width: 276px;
  padding: 1px;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 13px;
  background-color: #fff;
  background-clip: padding-box;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
}
.popover.top {
  margin-top: -10px;
}
.popover.right {
  margin-left: 10px;
}
.popover.bottom {
  margin-top: 10px;
}
.popover.left {
  margin-left: -10px;
}
.popover-title {
  margin: 0;
  padding: 8px 14px;
  font-size: 13px;
  background-color: #f7f7f7;
  border-bottom: 1px solid #ebebeb;
  border-radius: 2px 2px 0 0;
}
.popover-content {
  padding: 9px 14px;
}
.popover > .arrow,
.popover > .arrow:after {
  position: absolute;
  display: block;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.popover > .arrow {
  border-width: 11px;
}
.popover > .arrow:after {
  border-width: 10px;
  content: "";
}
.popover.top > .arrow {
  left: 50%;
  margin-left: -11px;
  border-bottom-width: 0;
  border-top-color: #999999;
  border-top-color: rgba(0, 0, 0, 0.25);
  bottom: -11px;
}
.popover.top > .arrow:after {
  content: " ";
  bottom: 1px;
  margin-left: -10px;
  border-bottom-width: 0;
  border-top-color: #fff;
}
.popover.right > .arrow {
  top: 50%;
  left: -11px;
  margin-top: -11px;
  border-left-width: 0;
  border-right-color: #999999;
  border-right-color: rgba(0, 0, 0, 0.25);
}
.popover.right > .arrow:after {
  content: " ";
  left: 1px;
  bottom: -10px;
  border-left-width: 0;
  border-right-color: #fff;
}
.popover.bottom > .arrow {
  left: 50%;
  margin-left: -11px;
  border-top-width: 0;
  border-bottom-color: #999999;
  border-bottom-color: rgba(0, 0, 0, 0.25);
  top: -11px;
}
.popover.bottom > .arrow:after {
  content: " ";
  top: 1px;
  margin-left: -10px;
  border-top-width: 0;
  border-bottom-color: #fff;
}
.popover.left > .arrow {
  top: 50%;
  right: -11px;
  margin-top: -11px;
  border-right-width: 0;
  border-left-color: #999999;
  border-left-color: rgba(0, 0, 0, 0.25);
}
.popover.left > .arrow:after {
  content: " ";
  right: 1px;
  border-right-width: 0;
  border-left-color: #fff;
  bottom: -10px;
}
.carousel {
  position: relative;
}
.carousel-inner {
  position: relative;
  overflow: hidden;
  width: 100%;
}
.carousel-inner > .item {
  display: none;
  position: relative;
  -webkit-transition: 0.6s ease-in-out left;
  -o-transition: 0.6s ease-in-out left;
  transition: 0.6s ease-in-out left;
}
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  line-height: 1;
}
@media all and (transform-3d), (-webkit-transform-3d) {
  .carousel-inner > .item {
    -webkit-transition: -webkit-transform 0.6s ease-in-out;
    -moz-transition: -moz-transform 0.6s ease-in-out;
    -o-transition: -o-transform 0.6s ease-in-out;
    transition: transform 0.6s ease-in-out;
    -webkit-backface-visibility: hidden;
    -moz-backface-visibility: hidden;
    backface-visibility: hidden;
    -webkit-perspective: 1000px;
    -moz-perspective: 1000px;
    perspective: 1000px;
  }
  .carousel-inner > .item.next,
  .carousel-inner > .item.active.right {
    -webkit-transform: translate3d(100%, 0, 0);
    transform: translate3d(100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.prev,
  .carousel-inner > .item.active.left {
    -webkit-transform: translate3d(-100%, 0, 0);
    transform: translate3d(-100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.next.left,
  .carousel-inner > .item.prev.right,
  .carousel-inner > .item.active {
    -webkit-transform: translate3d(0, 0, 0);
    transform: translate3d(0, 0, 0);
    left: 0;
  }
}
.carousel-inner > .active,
.carousel-inner > .next,
.carousel-inner > .prev {
  display: block;
}
.carousel-inner > .active {
  left: 0;
}
.carousel-inner > .next,
.carousel-inner > .prev {
  position: absolute;
  top: 0;
  width: 100%;
}
.carousel-inner > .next {
  left: 100%;
}
.carousel-inner > .prev {
  left: -100%;
}
.carousel-inner > .next.left,
.carousel-inner > .prev.right {
  left: 0;
}
.carousel-inner > .active.left {
  left: -100%;
}
.carousel-inner > .active.right {
  left: 100%;
}
.carousel-control {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  width: 15%;
  opacity: 0.5;
  filter: alpha(opacity=50);
  font-size: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
  background-color: rgba(0, 0, 0, 0);
}
.carousel-control.left {
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
}
.carousel-control.right {
  left: auto;
  right: 0;
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
}
.carousel-control:hover,
.carousel-control:focus {
  outline: 0;
  color: #fff;
  text-decoration: none;
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.carousel-control .icon-prev,
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-left,
.carousel-control .glyphicon-chevron-right {
  position: absolute;
  top: 50%;
  margin-top: -10px;
  z-index: 5;
  display: inline-block;
}
.carousel-control .icon-prev,
.carousel-control .glyphicon-chevron-left {
  left: 50%;
  margin-left: -10px;
}
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-right {
  right: 50%;
  margin-right: -10px;
}
.carousel-control .icon-prev,
.carousel-control .icon-next {
  width: 20px;
  height: 20px;
  line-height: 1;
  font-family: serif;
}
.carousel-control .icon-prev:before {
  content: '\2039';
}
.carousel-control .icon-next:before {
  content: '\203a';
}
.carousel-indicators {
  position: absolute;
  bottom: 10px;
  left: 50%;
  z-index: 15;
  width: 60%;
  margin-left: -30%;
  padding-left: 0;
  list-style: none;
  text-align: center;
}
.carousel-indicators li {
  display: inline-block;
  width: 10px;
  height: 10px;
  margin: 1px;
  text-indent: -999px;
  border: 1px solid #fff;
  border-radius: 10px;
  cursor: pointer;
  background-color: #000 \9;
  background-color: rgba(0, 0, 0, 0);
}
.carousel-indicators .active {
  margin: 0;
  width: 12px;
  height: 12px;
  background-color: #fff;
}
.carousel-caption {
  position: absolute;
  left: 15%;
  right: 15%;
  bottom: 20px;
  z-index: 10;
  padding-top: 20px;
  padding-bottom: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
}
.carousel-caption .btn {
  text-shadow: none;
}
@media screen and (min-width: 768px) {
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-prev,
  .carousel-control .icon-next {
    width: 30px;
    height: 30px;
    margin-top: -10px;
    font-size: 30px;
  }
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .icon-prev {
    margin-left: -10px;
  }
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-next {
    margin-right: -10px;
  }
  .carousel-caption {
    left: 20%;
    right: 20%;
    padding-bottom: 30px;
  }
  .carousel-indicators {
    bottom: 20px;
  }
}
.clearfix:before,
.clearfix:after,
.dl-horizontal dd:before,
.dl-horizontal dd:after,
.container:before,
.container:after,
.container-fluid:before,
.container-fluid:after,
.row:before,
.row:after,
.form-horizontal .form-group:before,
.form-horizontal .form-group:after,
.btn-toolbar:before,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:before,
.btn-group-vertical > .btn-group:after,
.nav:before,
.nav:after,
.navbar:before,
.navbar:after,
.navbar-header:before,
.navbar-header:after,
.navbar-collapse:before,
.navbar-collapse:after,
.pager:before,
.pager:after,
.panel-body:before,
.panel-body:after,
.modal-header:before,
.modal-header:after,
.modal-footer:before,
.modal-footer:after,
.item_buttons:before,
.item_buttons:after {
  content: " ";
  display: table;
}
.clearfix:after,
.dl-horizontal dd:after,
.container:after,
.container-fluid:after,
.row:after,
.form-horizontal .form-group:after,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:after,
.nav:after,
.navbar:after,
.navbar-header:after,
.navbar-collapse:after,
.pager:after,
.panel-body:after,
.modal-header:after,
.modal-footer:after,
.item_buttons:after {
  clear: both;
}
.center-block {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.pull-right {
  float: right !important;
}
.pull-left {
  float: left !important;
}
.hide {
  display: none !important;
}
.show {
  display: block !important;
}
.invisible {
  visibility: hidden;
}
.text-hide {
  font: 0/0 a;
  color: transparent;
  text-shadow: none;
  background-color: transparent;
  border: 0;
}
.hidden {
  display: none !important;
}
.affix {
  position: fixed;
}
@-ms-viewport {
  width: device-width;
}
.visible-xs,
.visible-sm,
.visible-md,
.visible-lg {
  display: none !important;
}
.visible-xs-block,
.visible-xs-inline,
.visible-xs-inline-block,
.visible-sm-block,
.visible-sm-inline,
.visible-sm-inline-block,
.visible-md-block,
.visible-md-inline,
.visible-md-inline-block,
.visible-lg-block,
.visible-lg-inline,
.visible-lg-inline-block {
  display: none !important;
}
@media (max-width: 767px) {
  .visible-xs {
    display: block !important;
  }
  table.visible-xs {
    display: table !important;
  }
  tr.visible-xs {
    display: table-row !important;
  }
  th.visible-xs,
  td.visible-xs {
    display: table-cell !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-block {
    display: block !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline {
    display: inline !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm {
    display: block !important;
  }
  table.visible-sm {
    display: table !important;
  }
  tr.visible-sm {
    display: table-row !important;
  }
  th.visible-sm,
  td.visible-sm {
    display: table-cell !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-block {
    display: block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline {
    display: inline !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md {
    display: block !important;
  }
  table.visible-md {
    display: table !important;
  }
  tr.visible-md {
    display: table-row !important;
  }
  th.visible-md,
  td.visible-md {
    display: table-cell !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-block {
    display: block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline {
    display: inline !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg {
    display: block !important;
  }
  table.visible-lg {
    display: table !important;
  }
  tr.visible-lg {
    display: table-row !important;
  }
  th.visible-lg,
  td.visible-lg {
    display: table-cell !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-block {
    display: block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline {
    display: inline !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline-block {
    display: inline-block !important;
  }
}
@media (max-width: 767px) {
  .hidden-xs {
    display: none !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .hidden-sm {
    display: none !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .hidden-md {
    display: none !important;
  }
}
@media (min-width: 1200px) {
  .hidden-lg {
    display: none !important;
  }
}
.visible-print {
  display: none !important;
}
@media print {
  .visible-print {
    display: block !important;
  }
  table.visible-print {
    display: table !important;
  }
  tr.visible-print {
    display: table-row !important;
  }
  th.visible-print,
  td.visible-print {
    display: table-cell !important;
  }
}
.visible-print-block {
  display: none !important;
}
@media print {
  .visible-print-block {
    display: block !important;
  }
}
.visible-print-inline {
  display: none !important;
}
@media print {
  .visible-print-inline {
    display: inline !important;
  }
}
.visible-print-inline-block {
  display: none !important;
}
@media print {
  .visible-print-inline-block {
    display: inline-block !important;
  }
}
@media print {
  .hidden-print {
    display: none !important;
  }
}
/*!
*
* Font Awesome
*
*/
/*!
 *  Font Awesome 4.2.0 by @davegandy - http://fontawesome.io - @fontawesome
 *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
 */
/* FONT PATH
 * -------------------------- */
@font-face {
  font-family: 'FontAwesome';
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.2.0');
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.2.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.2.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.2.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.2.0#fontawesomeregular') format('svg');
  font-weight: normal;
  font-style: normal;
}
.fa {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
/* makes the font 33% larger relative to the icon container */
.fa-lg {
  font-size: 1.33333333em;
  line-height: 0.75em;
  vertical-align: -15%;
}
.fa-2x {
  font-size: 2em;
}
.fa-3x {
  font-size: 3em;
}
.fa-4x {
  font-size: 4em;
}
.fa-5x {
  font-size: 5em;
}
.fa-fw {
  width: 1.28571429em;
  text-align: center;
}
.fa-ul {
  padding-left: 0;
  margin-left: 2.14285714em;
  list-style-type: none;
}
.fa-ul > li {
  position: relative;
}
.fa-li {
  position: absolute;
  left: -2.14285714em;
  width: 2.14285714em;
  top: 0.14285714em;
  text-align: center;
}
.fa-li.fa-lg {
  left: -1.85714286em;
}
.fa-border {
  padding: .2em .25em .15em;
  border: solid 0.08em #eee;
  border-radius: .1em;
}
.pull-right {
  float: right;
}
.pull-left {
  float: left;
}
.fa.pull-left {
  margin-right: .3em;
}
.fa.pull-right {
  margin-left: .3em;
}
.fa-spin {
  -webkit-animation: fa-spin 2s infinite linear;
  animation: fa-spin 2s infinite linear;
}
@-webkit-keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
@keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
.fa-rotate-90 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=1);
  -webkit-transform: rotate(90deg);
  -ms-transform: rotate(90deg);
  transform: rotate(90deg);
}
.fa-rotate-180 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2);
  -webkit-transform: rotate(180deg);
  -ms-transform: rotate(180deg);
  transform: rotate(180deg);
}
.fa-rotate-270 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=3);
  -webkit-transform: rotate(270deg);
  -ms-transform: rotate(270deg);
  transform: rotate(270deg);
}
.fa-flip-horizontal {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1);
  -webkit-transform: scale(-1, 1);
  -ms-transform: scale(-1, 1);
  transform: scale(-1, 1);
}
.fa-flip-vertical {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1);
  -webkit-transform: scale(1, -1);
  -ms-transform: scale(1, -1);
  transform: scale(1, -1);
}
:root .fa-rotate-90,
:root .fa-rotate-180,
:root .fa-rotate-270,
:root .fa-flip-horizontal,
:root .fa-flip-vertical {
  filter: none;
}
.fa-stack {
  position: relative;
  display: inline-block;
  width: 2em;
  height: 2em;
  line-height: 2em;
  vertical-align: middle;
}
.fa-stack-1x,
.fa-stack-2x {
  position: absolute;
  left: 0;
  width: 100%;
  text-align: center;
}
.fa-stack-1x {
  line-height: inherit;
}
.fa-stack-2x {
  font-size: 2em;
}
.fa-inverse {
  color: #fff;
}
/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
   readers do not read off random characters that represent icons */
.fa-glass:before {
  content: "\f000";
}
.fa-music:before {
  content: "\f001";
}
.fa-search:before {
  content: "\f002";
}
.fa-envelope-o:before {
  content: "\f003";
}
.fa-heart:before {
  content: "\f004";
}
.fa-star:before {
  content: "\f005";
}
.fa-star-o:before {
  content: "\f006";
}
.fa-user:before {
  content: "\f007";
}
.fa-film:before {
  content: "\f008";
}
.fa-th-large:before {
  content: "\f009";
}
.fa-th:before {
  content: "\f00a";
}
.fa-th-list:before {
  content: "\f00b";
}
.fa-check:before {
  content: "\f00c";
}
.fa-remove:before,
.fa-close:before,
.fa-times:before {
  content: "\f00d";
}
.fa-search-plus:before {
  content: "\f00e";
}
.fa-search-minus:before {
  content: "\f010";
}
.fa-power-off:before {
  content: "\f011";
}
.fa-signal:before {
  content: "\f012";
}
.fa-gear:before,
.fa-cog:before {
  content: "\f013";
}
.fa-trash-o:before {
  content: "\f014";
}
.fa-home:before {
  content: "\f015";
}
.fa-file-o:before {
  content: "\f016";
}
.fa-clock-o:before {
  content: "\f017";
}
.fa-road:before {
  content: "\f018";
}
.fa-download:before {
  content: "\f019";
}
.fa-arrow-circle-o-down:before {
  content: "\f01a";
}
.fa-arrow-circle-o-up:before {
  content: "\f01b";
}
.fa-inbox:before {
  content: "\f01c";
}
.fa-play-circle-o:before {
  content: "\f01d";
}
.fa-rotate-right:before,
.fa-repeat:before {
  content: "\f01e";
}
.fa-refresh:before {
  content: "\f021";
}
.fa-list-alt:before {
  content: "\f022";
}
.fa-lock:before {
  content: "\f023";
}
.fa-flag:before {
  content: "\f024";
}
.fa-headphones:before {
  content: "\f025";
}
.fa-volume-off:before {
  content: "\f026";
}
.fa-volume-down:before {
  content: "\f027";
}
.fa-volume-up:before {
  content: "\f028";
}
.fa-qrcode:before {
  content: "\f029";
}
.fa-barcode:before {
  content: "\f02a";
}
.fa-tag:before {
  content: "\f02b";
}
.fa-tags:before {
  content: "\f02c";
}
.fa-book:before {
  content: "\f02d";
}
.fa-bookmark:before {
  content: "\f02e";
}
.fa-print:before {
  content: "\f02f";
}
.fa-camera:before {
  content: "\f030";
}
.fa-font:before {
  content: "\f031";
}
.fa-bold:before {
  content: "\f032";
}
.fa-italic:before {
  content: "\f033";
}
.fa-text-height:before {
  content: "\f034";
}
.fa-text-width:before {
  content: "\f035";
}
.fa-align-left:before {
  content: "\f036";
}
.fa-align-center:before {
  content: "\f037";
}
.fa-align-right:before {
  content: "\f038";
}
.fa-align-justify:before {
  content: "\f039";
}
.fa-list:before {
  content: "\f03a";
}
.fa-dedent:before,
.fa-outdent:before {
  content: "\f03b";
}
.fa-indent:before {
  content: "\f03c";
}
.fa-video-camera:before {
  content: "\f03d";
}
.fa-photo:before,
.fa-image:before,
.fa-picture-o:before {
  content: "\f03e";
}
.fa-pencil:before {
  content: "\f040";
}
.fa-map-marker:before {
  content: "\f041";
}
.fa-adjust:before {
  content: "\f042";
}
.fa-tint:before {
  content: "\f043";
}
.fa-edit:before,
.fa-pencil-square-o:before {
  content: "\f044";
}
.fa-share-square-o:before {
  content: "\f045";
}
.fa-check-square-o:before {
  content: "\f046";
}
.fa-arrows:before {
  content: "\f047";
}
.fa-step-backward:before {
  content: "\f048";
}
.fa-fast-backward:before {
  content: "\f049";
}
.fa-backward:before {
  content: "\f04a";
}
.fa-play:before {
  content: "\f04b";
}
.fa-pause:before {
  content: "\f04c";
}
.fa-stop:before {
  content: "\f04d";
}
.fa-forward:before {
  content: "\f04e";
}
.fa-fast-forward:before {
  content: "\f050";
}
.fa-step-forward:before {
  content: "\f051";
}
.fa-eject:before {
  content: "\f052";
}
.fa-chevron-left:before {
  content: "\f053";
}
.fa-chevron-right:before {
  content: "\f054";
}
.fa-plus-circle:before {
  content: "\f055";
}
.fa-minus-circle:before {
  content: "\f056";
}
.fa-times-circle:before {
  content: "\f057";
}
.fa-check-circle:before {
  content: "\f058";
}
.fa-question-circle:before {
  content: "\f059";
}
.fa-info-circle:before {
  content: "\f05a";
}
.fa-crosshairs:before {
  content: "\f05b";
}
.fa-times-circle-o:before {
  content: "\f05c";
}
.fa-check-circle-o:before {
  content: "\f05d";
}
.fa-ban:before {
  content: "\f05e";
}
.fa-arrow-left:before {
  content: "\f060";
}
.fa-arrow-right:before {
  content: "\f061";
}
.fa-arrow-up:before {
  content: "\f062";
}
.fa-arrow-down:before {
  content: "\f063";
}
.fa-mail-forward:before,
.fa-share:before {
  content: "\f064";
}
.fa-expand:before {
  content: "\f065";
}
.fa-compress:before {
  content: "\f066";
}
.fa-plus:before {
  content: "\f067";
}
.fa-minus:before {
  content: "\f068";
}
.fa-asterisk:before {
  content: "\f069";
}
.fa-exclamation-circle:before {
  content: "\f06a";
}
.fa-gift:before {
  content: "\f06b";
}
.fa-leaf:before {
  content: "\f06c";
}
.fa-fire:before {
  content: "\f06d";
}
.fa-eye:before {
  content: "\f06e";
}
.fa-eye-slash:before {
  content: "\f070";
}
.fa-warning:before,
.fa-exclamation-triangle:before {
  content: "\f071";
}
.fa-plane:before {
  content: "\f072";
}
.fa-calendar:before {
  content: "\f073";
}
.fa-random:before {
  content: "\f074";
}
.fa-comment:before {
  content: "\f075";
}
.fa-magnet:before {
  content: "\f076";
}
.fa-chevron-up:before {
  content: "\f077";
}
.fa-chevron-down:before {
  content: "\f078";
}
.fa-retweet:before {
  content: "\f079";
}
.fa-shopping-cart:before {
  content: "\f07a";
}
.fa-folder:before {
  content: "\f07b";
}
.fa-folder-open:before {
  content: "\f07c";
}
.fa-arrows-v:before {
  content: "\f07d";
}
.fa-arrows-h:before {
  content: "\f07e";
}
.fa-bar-chart-o:before,
.fa-bar-chart:before {
  content: "\f080";
}
.fa-twitter-square:before {
  content: "\f081";
}
.fa-facebook-square:before {
  content: "\f082";
}
.fa-camera-retro:before {
  content: "\f083";
}
.fa-key:before {
  content: "\f084";
}
.fa-gears:before,
.fa-cogs:before {
  content: "\f085";
}
.fa-comments:before {
  content: "\f086";
}
.fa-thumbs-o-up:before {
  content: "\f087";
}
.fa-thumbs-o-down:before {
  content: "\f088";
}
.fa-star-half:before {
  content: "\f089";
}
.fa-heart-o:before {
  content: "\f08a";
}
.fa-sign-out:before {
  content: "\f08b";
}
.fa-linkedin-square:before {
  content: "\f08c";
}
.fa-thumb-tack:before {
  content: "\f08d";
}
.fa-external-link:before {
  content: "\f08e";
}
.fa-sign-in:before {
  content: "\f090";
}
.fa-trophy:before {
  content: "\f091";
}
.fa-github-square:before {
  content: "\f092";
}
.fa-upload:before {
  content: "\f093";
}
.fa-lemon-o:before {
  content: "\f094";
}
.fa-phone:before {
  content: "\f095";
}
.fa-square-o:before {
  content: "\f096";
}
.fa-bookmark-o:before {
  content: "\f097";
}
.fa-phone-square:before {
  content: "\f098";
}
.fa-twitter:before {
  content: "\f099";
}
.fa-facebook:before {
  content: "\f09a";
}
.fa-github:before {
  content: "\f09b";
}
.fa-unlock:before {
  content: "\f09c";
}
.fa-credit-card:before {
  content: "\f09d";
}
.fa-rss:before {
  content: "\f09e";
}
.fa-hdd-o:before {
  content: "\f0a0";
}
.fa-bullhorn:before {
  content: "\f0a1";
}
.fa-bell:before {
  content: "\f0f3";
}
.fa-certificate:before {
  content: "\f0a3";
}
.fa-hand-o-right:before {
  content: "\f0a4";
}
.fa-hand-o-left:before {
  content: "\f0a5";
}
.fa-hand-o-up:before {
  content: "\f0a6";
}
.fa-hand-o-down:before {
  content: "\f0a7";
}
.fa-arrow-circle-left:before {
  content: "\f0a8";
}
.fa-arrow-circle-right:before {
  content: "\f0a9";
}
.fa-arrow-circle-up:before {
  content: "\f0aa";
}
.fa-arrow-circle-down:before {
  content: "\f0ab";
}
.fa-globe:before {
  content: "\f0ac";
}
.fa-wrench:before {
  content: "\f0ad";
}
.fa-tasks:before {
  content: "\f0ae";
}
.fa-filter:before {
  content: "\f0b0";
}
.fa-briefcase:before {
  content: "\f0b1";
}
.fa-arrows-alt:before {
  content: "\f0b2";
}
.fa-group:before,
.fa-users:before {
  content: "\f0c0";
}
.fa-chain:before,
.fa-link:before {
  content: "\f0c1";
}
.fa-cloud:before {
  content: "\f0c2";
}
.fa-flask:before {
  content: "\f0c3";
}
.fa-cut:before,
.fa-scissors:before {
  content: "\f0c4";
}
.fa-copy:before,
.fa-files-o:before {
  content: "\f0c5";
}
.fa-paperclip:before {
  content: "\f0c6";
}
.fa-save:before,
.fa-floppy-o:before {
  content: "\f0c7";
}
.fa-square:before {
  content: "\f0c8";
}
.fa-navicon:before,
.fa-reorder:before,
.fa-bars:before {
  content: "\f0c9";
}
.fa-list-ul:before {
  content: "\f0ca";
}
.fa-list-ol:before {
  content: "\f0cb";
}
.fa-strikethrough:before {
  content: "\f0cc";
}
.fa-underline:before {
  content: "\f0cd";
}
.fa-table:before {
  content: "\f0ce";
}
.fa-magic:before {
  content: "\f0d0";
}
.fa-truck:before {
  content: "\f0d1";
}
.fa-pinterest:before {
  content: "\f0d2";
}
.fa-pinterest-square:before {
  content: "\f0d3";
}
.fa-google-plus-square:before {
  content: "\f0d4";
}
.fa-google-plus:before {
  content: "\f0d5";
}
.fa-money:before {
  content: "\f0d6";
}
.fa-caret-down:before {
  content: "\f0d7";
}
.fa-caret-up:before {
  content: "\f0d8";
}
.fa-caret-left:before {
  content: "\f0d9";
}
.fa-caret-right:before {
  content: "\f0da";
}
.fa-columns:before {
  content: "\f0db";
}
.fa-unsorted:before,
.fa-sort:before {
  content: "\f0dc";
}
.fa-sort-down:before,
.fa-sort-desc:before {
  content: "\f0dd";
}
.fa-sort-up:before,
.fa-sort-asc:before {
  content: "\f0de";
}
.fa-envelope:before {
  content: "\f0e0";
}
.fa-linkedin:before {
  content: "\f0e1";
}
.fa-rotate-left:before,
.fa-undo:before {
  content: "\f0e2";
}
.fa-legal:before,
.fa-gavel:before {
  content: "\f0e3";
}
.fa-dashboard:before,
.fa-tachometer:before {
  content: "\f0e4";
}
.fa-comment-o:before {
  content: "\f0e5";
}
.fa-comments-o:before {
  content: "\f0e6";
}
.fa-flash:before,
.fa-bolt:before {
  content: "\f0e7";
}
.fa-sitemap:before {
  content: "\f0e8";
}
.fa-umbrella:before {
  content: "\f0e9";
}
.fa-paste:before,
.fa-clipboard:before {
  content: "\f0ea";
}
.fa-lightbulb-o:before {
  content: "\f0eb";
}
.fa-exchange:before {
  content: "\f0ec";
}
.fa-cloud-download:before {
  content: "\f0ed";
}
.fa-cloud-upload:before {
  content: "\f0ee";
}
.fa-user-md:before {
  content: "\f0f0";
}
.fa-stethoscope:before {
  content: "\f0f1";
}
.fa-suitcase:before {
  content: "\f0f2";
}
.fa-bell-o:before {
  content: "\f0a2";
}
.fa-coffee:before {
  content: "\f0f4";
}
.fa-cutlery:before {
  content: "\f0f5";
}
.fa-file-text-o:before {
  content: "\f0f6";
}
.fa-building-o:before {
  content: "\f0f7";
}
.fa-hospital-o:before {
  content: "\f0f8";
}
.fa-ambulance:before {
  content: "\f0f9";
}
.fa-medkit:before {
  content: "\f0fa";
}
.fa-fighter-jet:before {
  content: "\f0fb";
}
.fa-beer:before {
  content: "\f0fc";
}
.fa-h-square:before {
  content: "\f0fd";
}
.fa-plus-square:before {
  content: "\f0fe";
}
.fa-angle-double-left:before {
  content: "\f100";
}
.fa-angle-double-right:before {
  content: "\f101";
}
.fa-angle-double-up:before {
  content: "\f102";
}
.fa-angle-double-down:before {
  content: "\f103";
}
.fa-angle-left:before {
  content: "\f104";
}
.fa-angle-right:before {
  content: "\f105";
}
.fa-angle-up:before {
  content: "\f106";
}
.fa-angle-down:before {
  content: "\f107";
}
.fa-desktop:before {
  content: "\f108";
}
.fa-laptop:before {
  content: "\f109";
}
.fa-tablet:before {
  content: "\f10a";
}
.fa-mobile-phone:before,
.fa-mobile:before {
  content: "\f10b";
}
.fa-circle-o:before {
  content: "\f10c";
}
.fa-quote-left:before {
  content: "\f10d";
}
.fa-quote-right:before {
  content: "\f10e";
}
.fa-spinner:before {
  content: "\f110";
}
.fa-circle:before {
  content: "\f111";
}
.fa-mail-reply:before,
.fa-reply:before {
  content: "\f112";
}
.fa-github-alt:before {
  content: "\f113";
}
.fa-folder-o:before {
  content: "\f114";
}
.fa-folder-open-o:before {
  content: "\f115";
}
.fa-smile-o:before {
  content: "\f118";
}
.fa-frown-o:before {
  content: "\f119";
}
.fa-meh-o:before {
  content: "\f11a";
}
.fa-gamepad:before {
  content: "\f11b";
}
.fa-keyboard-o:before {
  content: "\f11c";
}
.fa-flag-o:before {
  content: "\f11d";
}
.fa-flag-checkered:before {
  content: "\f11e";
}
.fa-terminal:before {
  content: "\f120";
}
.fa-code:before {
  content: "\f121";
}
.fa-mail-reply-all:before,
.fa-reply-all:before {
  content: "\f122";
}
.fa-star-half-empty:before,
.fa-star-half-full:before,
.fa-star-half-o:before {
  content: "\f123";
}
.fa-location-arrow:before {
  content: "\f124";
}
.fa-crop:before {
  content: "\f125";
}
.fa-code-fork:before {
  content: "\f126";
}
.fa-unlink:before,
.fa-chain-broken:before {
  content: "\f127";
}
.fa-question:before {
  content: "\f128";
}
.fa-info:before {
  content: "\f129";
}
.fa-exclamation:before {
  content: "\f12a";
}
.fa-superscript:before {
  content: "\f12b";
}
.fa-subscript:before {
  content: "\f12c";
}
.fa-eraser:before {
  content: "\f12d";
}
.fa-puzzle-piece:before {
  content: "\f12e";
}
.fa-microphone:before {
  content: "\f130";
}
.fa-microphone-slash:before {
  content: "\f131";
}
.fa-shield:before {
  content: "\f132";
}
.fa-calendar-o:before {
  content: "\f133";
}
.fa-fire-extinguisher:before {
  content: "\f134";
}
.fa-rocket:before {
  content: "\f135";
}
.fa-maxcdn:before {
  content: "\f136";
}
.fa-chevron-circle-left:before {
  content: "\f137";
}
.fa-chevron-circle-right:before {
  content: "\f138";
}
.fa-chevron-circle-up:before {
  content: "\f139";
}
.fa-chevron-circle-down:before {
  content: "\f13a";
}
.fa-html5:before {
  content: "\f13b";
}
.fa-css3:before {
  content: "\f13c";
}
.fa-anchor:before {
  content: "\f13d";
}
.fa-unlock-alt:before {
  content: "\f13e";
}
.fa-bullseye:before {
  content: "\f140";
}
.fa-ellipsis-h:before {
  content: "\f141";
}
.fa-ellipsis-v:before {
  content: "\f142";
}
.fa-rss-square:before {
  content: "\f143";
}
.fa-play-circle:before {
  content: "\f144";
}
.fa-ticket:before {
  content: "\f145";
}
.fa-minus-square:before {
  content: "\f146";
}
.fa-minus-square-o:before {
  content: "\f147";
}
.fa-level-up:before {
  content: "\f148";
}
.fa-level-down:before {
  content: "\f149";
}
.fa-check-square:before {
  content: "\f14a";
}
.fa-pencil-square:before {
  content: "\f14b";
}
.fa-external-link-square:before {
  content: "\f14c";
}
.fa-share-square:before {
  content: "\f14d";
}
.fa-compass:before {
  content: "\f14e";
}
.fa-toggle-down:before,
.fa-caret-square-o-down:before {
  content: "\f150";
}
.fa-toggle-up:before,
.fa-caret-square-o-up:before {
  content: "\f151";
}
.fa-toggle-right:before,
.fa-caret-square-o-right:before {
  content: "\f152";
}
.fa-euro:before,
.fa-eur:before {
  content: "\f153";
}
.fa-gbp:before {
  content: "\f154";
}
.fa-dollar:before,
.fa-usd:before {
  content: "\f155";
}
.fa-rupee:before,
.fa-inr:before {
  content: "\f156";
}
.fa-cny:before,
.fa-rmb:before,
.fa-yen:before,
.fa-jpy:before {
  content: "\f157";
}
.fa-ruble:before,
.fa-rouble:before,
.fa-rub:before {
  content: "\f158";
}
.fa-won:before,
.fa-krw:before {
  content: "\f159";
}
.fa-bitcoin:before,
.fa-btc:before {
  content: "\f15a";
}
.fa-file:before {
  content: "\f15b";
}
.fa-file-text:before {
  content: "\f15c";
}
.fa-sort-alpha-asc:before {
  content: "\f15d";
}
.fa-sort-alpha-desc:before {
  content: "\f15e";
}
.fa-sort-amount-asc:before {
  content: "\f160";
}
.fa-sort-amount-desc:before {
  content: "\f161";
}
.fa-sort-numeric-asc:before {
  content: "\f162";
}
.fa-sort-numeric-desc:before {
  content: "\f163";
}
.fa-thumbs-up:before {
  content: "\f164";
}
.fa-thumbs-down:before {
  content: "\f165";
}
.fa-youtube-square:before {
  content: "\f166";
}
.fa-youtube:before {
  content: "\f167";
}
.fa-xing:before {
  content: "\f168";
}
.fa-xing-square:before {
  content: "\f169";
}
.fa-youtube-play:before {
  content: "\f16a";
}
.fa-dropbox:before {
  content: "\f16b";
}
.fa-stack-overflow:before {
  content: "\f16c";
}
.fa-instagram:before {
  content: "\f16d";
}
.fa-flickr:before {
  content: "\f16e";
}
.fa-adn:before {
  content: "\f170";
}
.fa-bitbucket:before {
  content: "\f171";
}
.fa-bitbucket-square:before {
  content: "\f172";
}
.fa-tumblr:before {
  content: "\f173";
}
.fa-tumblr-square:before {
  content: "\f174";
}
.fa-long-arrow-down:before {
  content: "\f175";
}
.fa-long-arrow-up:before {
  content: "\f176";
}
.fa-long-arrow-left:before {
  content: "\f177";
}
.fa-long-arrow-right:before {
  content: "\f178";
}
.fa-apple:before {
  content: "\f179";
}
.fa-windows:before {
  content: "\f17a";
}
.fa-android:before {
  content: "\f17b";
}
.fa-linux:before {
  content: "\f17c";
}
.fa-dribbble:before {
  content: "\f17d";
}
.fa-skype:before {
  content: "\f17e";
}
.fa-foursquare:before {
  content: "\f180";
}
.fa-trello:before {
  content: "\f181";
}
.fa-female:before {
  content: "\f182";
}
.fa-male:before {
  content: "\f183";
}
.fa-gittip:before {
  content: "\f184";
}
.fa-sun-o:before {
  content: "\f185";
}
.fa-moon-o:before {
  content: "\f186";
}
.fa-archive:before {
  content: "\f187";
}
.fa-bug:before {
  content: "\f188";
}
.fa-vk:before {
  content: "\f189";
}
.fa-weibo:before {
  content: "\f18a";
}
.fa-renren:before {
  content: "\f18b";
}
.fa-pagelines:before {
  content: "\f18c";
}
.fa-stack-exchange:before {
  content: "\f18d";
}
.fa-arrow-circle-o-right:before {
  content: "\f18e";
}
.fa-arrow-circle-o-left:before {
  content: "\f190";
}
.fa-toggle-left:before,
.fa-caret-square-o-left:before {
  content: "\f191";
}
.fa-dot-circle-o:before {
  content: "\f192";
}
.fa-wheelchair:before {
  content: "\f193";
}
.fa-vimeo-square:before {
  content: "\f194";
}
.fa-turkish-lira:before,
.fa-try:before {
  content: "\f195";
}
.fa-plus-square-o:before {
  content: "\f196";
}
.fa-space-shuttle:before {
  content: "\f197";
}
.fa-slack:before {
  content: "\f198";
}
.fa-envelope-square:before {
  content: "\f199";
}
.fa-wordpress:before {
  content: "\f19a";
}
.fa-openid:before {
  content: "\f19b";
}
.fa-institution:before,
.fa-bank:before,
.fa-university:before {
  content: "\f19c";
}
.fa-mortar-board:before,
.fa-graduation-cap:before {
  content: "\f19d";
}
.fa-yahoo:before {
  content: "\f19e";
}
.fa-google:before {
  content: "\f1a0";
}
.fa-reddit:before {
  content: "\f1a1";
}
.fa-reddit-square:before {
  content: "\f1a2";
}
.fa-stumbleupon-circle:before {
  content: "\f1a3";
}
.fa-stumbleupon:before {
  content: "\f1a4";
}
.fa-delicious:before {
  content: "\f1a5";
}
.fa-digg:before {
  content: "\f1a6";
}
.fa-pied-piper:before {
  content: "\f1a7";
}
.fa-pied-piper-alt:before {
  content: "\f1a8";
}
.fa-drupal:before {
  content: "\f1a9";
}
.fa-joomla:before {
  content: "\f1aa";
}
.fa-language:before {
  content: "\f1ab";
}
.fa-fax:before {
  content: "\f1ac";
}
.fa-building:before {
  content: "\f1ad";
}
.fa-child:before {
  content: "\f1ae";
}
.fa-paw:before {
  content: "\f1b0";
}
.fa-spoon:before {
  content: "\f1b1";
}
.fa-cube:before {
  content: "\f1b2";
}
.fa-cubes:before {
  content: "\f1b3";
}
.fa-behance:before {
  content: "\f1b4";
}
.fa-behance-square:before {
  content: "\f1b5";
}
.fa-steam:before {
  content: "\f1b6";
}
.fa-steam-square:before {
  content: "\f1b7";
}
.fa-recycle:before {
  content: "\f1b8";
}
.fa-automobile:before,
.fa-car:before {
  content: "\f1b9";
}
.fa-cab:before,
.fa-taxi:before {
  content: "\f1ba";
}
.fa-tree:before {
  content: "\f1bb";
}
.fa-spotify:before {
  content: "\f1bc";
}
.fa-deviantart:before {
  content: "\f1bd";
}
.fa-soundcloud:before {
  content: "\f1be";
}
.fa-database:before {
  content: "\f1c0";
}
.fa-file-pdf-o:before {
  content: "\f1c1";
}
.fa-file-word-o:before {
  content: "\f1c2";
}
.fa-file-excel-o:before {
  content: "\f1c3";
}
.fa-file-powerpoint-o:before {
  content: "\f1c4";
}
.fa-file-photo-o:before,
.fa-file-picture-o:before,
.fa-file-image-o:before {
  content: "\f1c5";
}
.fa-file-zip-o:before,
.fa-file-archive-o:before {
  content: "\f1c6";
}
.fa-file-sound-o:before,
.fa-file-audio-o:before {
  content: "\f1c7";
}
.fa-file-movie-o:before,
.fa-file-video-o:before {
  content: "\f1c8";
}
.fa-file-code-o:before {
  content: "\f1c9";
}
.fa-vine:before {
  content: "\f1ca";
}
.fa-codepen:before {
  content: "\f1cb";
}
.fa-jsfiddle:before {
  content: "\f1cc";
}
.fa-life-bouy:before,
.fa-life-buoy:before,
.fa-life-saver:before,
.fa-support:before,
.fa-life-ring:before {
  content: "\f1cd";
}
.fa-circle-o-notch:before {
  content: "\f1ce";
}
.fa-ra:before,
.fa-rebel:before {
  content: "\f1d0";
}
.fa-ge:before,
.fa-empire:before {
  content: "\f1d1";
}
.fa-git-square:before {
  content: "\f1d2";
}
.fa-git:before {
  content: "\f1d3";
}
.fa-hacker-news:before {
  content: "\f1d4";
}
.fa-tencent-weibo:before {
  content: "\f1d5";
}
.fa-qq:before {
  content: "\f1d6";
}
.fa-wechat:before,
.fa-weixin:before {
  content: "\f1d7";
}
.fa-send:before,
.fa-paper-plane:before {
  content: "\f1d8";
}
.fa-send-o:before,
.fa-paper-plane-o:before {
  content: "\f1d9";
}
.fa-history:before {
  content: "\f1da";
}
.fa-circle-thin:before {
  content: "\f1db";
}
.fa-header:before {
  content: "\f1dc";
}
.fa-paragraph:before {
  content: "\f1dd";
}
.fa-sliders:before {
  content: "\f1de";
}
.fa-share-alt:before {
  content: "\f1e0";
}
.fa-share-alt-square:before {
  content: "\f1e1";
}
.fa-bomb:before {
  content: "\f1e2";
}
.fa-soccer-ball-o:before,
.fa-futbol-o:before {
  content: "\f1e3";
}
.fa-tty:before {
  content: "\f1e4";
}
.fa-binoculars:before {
  content: "\f1e5";
}
.fa-plug:before {
  content: "\f1e6";
}
.fa-slideshare:before {
  content: "\f1e7";
}
.fa-twitch:before {
  content: "\f1e8";
}
.fa-yelp:before {
  content: "\f1e9";
}
.fa-newspaper-o:before {
  content: "\f1ea";
}
.fa-wifi:before {
  content: "\f1eb";
}
.fa-calculator:before {
  content: "\f1ec";
}
.fa-paypal:before {
  content: "\f1ed";
}
.fa-google-wallet:before {
  content: "\f1ee";
}
.fa-cc-visa:before {
  content: "\f1f0";
}
.fa-cc-mastercard:before {
  content: "\f1f1";
}
.fa-cc-discover:before {
  content: "\f1f2";
}
.fa-cc-amex:before {
  content: "\f1f3";
}
.fa-cc-paypal:before {
  content: "\f1f4";
}
.fa-cc-stripe:before {
  content: "\f1f5";
}
.fa-bell-slash:before {
  content: "\f1f6";
}
.fa-bell-slash-o:before {
  content: "\f1f7";
}
.fa-trash:before {
  content: "\f1f8";
}
.fa-copyright:before {
  content: "\f1f9";
}
.fa-at:before {
  content: "\f1fa";
}
.fa-eyedropper:before {
  content: "\f1fb";
}
.fa-paint-brush:before {
  content: "\f1fc";
}
.fa-birthday-cake:before {
  content: "\f1fd";
}
.fa-area-chart:before {
  content: "\f1fe";
}
.fa-pie-chart:before {
  content: "\f200";
}
.fa-line-chart:before {
  content: "\f201";
}
.fa-lastfm:before {
  content: "\f202";
}
.fa-lastfm-square:before {
  content: "\f203";
}
.fa-toggle-off:before {
  content: "\f204";
}
.fa-toggle-on:before {
  content: "\f205";
}
.fa-bicycle:before {
  content: "\f206";
}
.fa-bus:before {
  content: "\f207";
}
.fa-ioxhost:before {
  content: "\f208";
}
.fa-angellist:before {
  content: "\f209";
}
.fa-cc:before {
  content: "\f20a";
}
.fa-shekel:before,
.fa-sheqel:before,
.fa-ils:before {
  content: "\f20b";
}
.fa-meanpath:before {
  content: "\f20c";
}
/*!
*
* IPython base
*
*/
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
code {
  color: #000;
}
pre {
  font-size: inherit;
  line-height: inherit;
}
label {
  font-weight: normal;
}
/* Make the page background atleast 100% the height of the view port */
/* Make the page itself atleast 70% the height of the view port */
.border-box-sizing {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.corner-all {
  border-radius: 2px;
}
.no-padding {
  padding: 0px;
}
/* Flexible box model classes */
/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
/* This file is a compatability layer.  It allows the usage of flexible box 
model layouts accross multiple browsers, including older browsers.  The newest,
universal implementation of the flexible box model is used when available (see
`Modern browsers` comments below).  Browsers that are known to implement this 
new spec completely include:

    Firefox 28.0+
    Chrome 29.0+
    Internet Explorer 11+ 
    Opera 17.0+

Browsers not listed, including Safari, are supported via the styling under the
`Old browsers` comments below.
*/
.hbox {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
.hbox > * {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
}
.vbox {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
.vbox > * {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
}
.hbox.reverse,
.vbox.reverse,
.reverse {
  /* Old browsers */
  -webkit-box-direction: reverse;
  -moz-box-direction: reverse;
  box-direction: reverse;
  /* Modern browsers */
  flex-direction: row-reverse;
}
.hbox.box-flex0,
.vbox.box-flex0,
.box-flex0 {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
  width: auto;
}
.hbox.box-flex1,
.vbox.box-flex1,
.box-flex1 {
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
.hbox.box-flex,
.vbox.box-flex,
.box-flex {
  /* Old browsers */
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
.hbox.box-flex2,
.vbox.box-flex2,
.box-flex2 {
  /* Old browsers */
  -webkit-box-flex: 2;
  -moz-box-flex: 2;
  box-flex: 2;
  /* Modern browsers */
  flex: 2;
}
.box-group1 {
  /*  Deprecated */
  -webkit-box-flex-group: 1;
  -moz-box-flex-group: 1;
  box-flex-group: 1;
}
.box-group2 {
  /* Deprecated */
  -webkit-box-flex-group: 2;
  -moz-box-flex-group: 2;
  box-flex-group: 2;
}
.hbox.start,
.vbox.start,
.start {
  /* Old browsers */
  -webkit-box-pack: start;
  -moz-box-pack: start;
  box-pack: start;
  /* Modern browsers */
  justify-content: flex-start;
}
.hbox.end,
.vbox.end,
.end {
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
}
.hbox.center,
.vbox.center,
.center {
  /* Old browsers */
  -webkit-box-pack: center;
  -moz-box-pack: center;
  box-pack: center;
  /* Modern browsers */
  justify-content: center;
}
.hbox.baseline,
.vbox.baseline,
.baseline {
  /* Old browsers */
  -webkit-box-pack: baseline;
  -moz-box-pack: baseline;
  box-pack: baseline;
  /* Modern browsers */
  justify-content: baseline;
}
.hbox.stretch,
.vbox.stretch,
.stretch {
  /* Old browsers */
  -webkit-box-pack: stretch;
  -moz-box-pack: stretch;
  box-pack: stretch;
  /* Modern browsers */
  justify-content: stretch;
}
.hbox.align-start,
.vbox.align-start,
.align-start {
  /* Old browsers */
  -webkit-box-align: start;
  -moz-box-align: start;
  box-align: start;
  /* Modern browsers */
  align-items: flex-start;
}
.hbox.align-end,
.vbox.align-end,
.align-end {
  /* Old browsers */
  -webkit-box-align: end;
  -moz-box-align: end;
  box-align: end;
  /* Modern browsers */
  align-items: flex-end;
}
.hbox.align-center,
.vbox.align-center,
.align-center {
  /* Old browsers */
  -webkit-box-align: center;
  -moz-box-align: center;
  box-align: center;
  /* Modern browsers */
  align-items: center;
}
.hbox.align-baseline,
.vbox.align-baseline,
.align-baseline {
  /* Old browsers */
  -webkit-box-align: baseline;
  -moz-box-align: baseline;
  box-align: baseline;
  /* Modern browsers */
  align-items: baseline;
}
.hbox.align-stretch,
.vbox.align-stretch,
.align-stretch {
  /* Old browsers */
  -webkit-box-align: stretch;
  -moz-box-align: stretch;
  box-align: stretch;
  /* Modern browsers */
  align-items: stretch;
}
div.error {
  margin: 2em;
  text-align: center;
}
div.error > h1 {
  font-size: 500%;
  line-height: normal;
}
div.error > p {
  font-size: 200%;
  line-height: normal;
}
div.traceback-wrapper {
  text-align: left;
  max-width: 800px;
  margin: auto;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
body {
  background-color: #fff;
  /* This makes sure that the body covers the entire window and needs to
       be in a different element than the display: box in wrapper below */
  position: absolute;
  left: 0px;
  right: 0px;
  top: 0px;
  bottom: 0px;
  overflow: visible;
}
body > #header {
  /* Initially hidden to prevent FLOUC */
  display: none;
  background-color: #fff;
  /* Display over codemirror */
  position: relative;
  z-index: 100;
}
body > #header #header-container {
  padding-bottom: 5px;
  padding-top: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
body > #header .header-bar {
  width: 100%;
  height: 1px;
  background: #e7e7e7;
  margin-bottom: -1px;
}
@media print {
  body > #header {
    display: none !important;
  }
}
#header-spacer {
  width: 100%;
  visibility: hidden;
}
@media print {
  #header-spacer {
    display: none;
  }
}
#ipython_notebook {
  padding-left: 0px;
  padding-top: 1px;
  padding-bottom: 1px;
}
@media (max-width: 991px) {
  #ipython_notebook {
    margin-left: 10px;
  }
}
[dir="rtl"] #ipython_notebook {
  float: right !important;
}
#noscript {
  width: auto;
  padding-top: 16px;
  padding-bottom: 16px;
  text-align: center;
  font-size: 22px;
  color: red;
  font-weight: bold;
}
#ipython_notebook img {
  height: 28px;
}
#site {
  width: 100%;
  display: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  overflow: auto;
}
@media print {
  #site {
    height: auto !important;
  }
}
/* Smaller buttons */
.ui-button .ui-button-text {
  padding: 0.2em 0.8em;
  font-size: 77%;
}
input.ui-button {
  padding: 0.3em 0.9em;
}
span#login_widget {
  float: right;
}
span#login_widget > .button,
#logout {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
span#login_widget > .button:focus,
#logout:focus,
span#login_widget > .button.focus,
#logout.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
span#login_widget > .button:hover,
#logout:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
span#login_widget > .button:active:hover,
#logout:active:hover,
span#login_widget > .button.active:hover,
#logout.active:hover,
.open > .dropdown-togglespan#login_widget > .button:hover,
.open > .dropdown-toggle#logout:hover,
span#login_widget > .button:active:focus,
#logout:active:focus,
span#login_widget > .button.active:focus,
#logout.active:focus,
.open > .dropdown-togglespan#login_widget > .button:focus,
.open > .dropdown-toggle#logout:focus,
span#login_widget > .button:active.focus,
#logout:active.focus,
span#login_widget > .button.active.focus,
#logout.active.focus,
.open > .dropdown-togglespan#login_widget > .button.focus,
.open > .dropdown-toggle#logout.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
  background-image: none;
}
span#login_widget > .button.disabled:hover,
#logout.disabled:hover,
span#login_widget > .button[disabled]:hover,
#logout[disabled]:hover,
fieldset[disabled] span#login_widget > .button:hover,
fieldset[disabled] #logout:hover,
span#login_widget > .button.disabled:focus,
#logout.disabled:focus,
span#login_widget > .button[disabled]:focus,
#logout[disabled]:focus,
fieldset[disabled] span#login_widget > .button:focus,
fieldset[disabled] #logout:focus,
span#login_widget > .button.disabled.focus,
#logout.disabled.focus,
span#login_widget > .button[disabled].focus,
#logout[disabled].focus,
fieldset[disabled] span#login_widget > .button.focus,
fieldset[disabled] #logout.focus {
  background-color: #fff;
  border-color: #ccc;
}
span#login_widget > .button .badge,
#logout .badge {
  color: #fff;
  background-color: #333;
}
.nav-header {
  text-transform: none;
}
#header > span {
  margin-top: 10px;
}
.modal_stretch .modal-dialog {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  min-height: 80vh;
}
.modal_stretch .modal-dialog .modal-body {
  max-height: calc(100vh - 200px);
  overflow: auto;
  flex: 1;
}
@media (min-width: 768px) {
  .modal .modal-dialog {
    width: 700px;
  }
}
@media (min-width: 768px) {
  select.form-control {
    margin-left: 12px;
    margin-right: 12px;
  }
}
/*!
*
* IPython auth
*
*/
.center-nav {
  display: inline-block;
  margin-bottom: -4px;
}
/*!
*
* IPython tree view
*
*/
/* We need an invisible input field on top of the sentense*/
/* "Drag file onto the list ..." */
.alternate_upload {
  background-color: none;
  display: inline;
}
.alternate_upload.form {
  padding: 0;
  margin: 0;
}
.alternate_upload input.fileinput {
  text-align: center;
  vertical-align: middle;
  display: inline;
  opacity: 0;
  z-index: 2;
  width: 12ex;
  margin-right: -12ex;
}
.alternate_upload .btn-upload {
  height: 22px;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
[dir="rtl"] #tabs li {
  float: right;
}
ul#tabs {
  margin-bottom: 4px;
}
[dir="rtl"] ul#tabs {
  margin-right: 0px;
}
ul#tabs a {
  padding-top: 6px;
  padding-bottom: 4px;
}
ul.breadcrumb a:focus,
ul.breadcrumb a:hover {
  text-decoration: none;
}
ul.breadcrumb i.icon-home {
  font-size: 16px;
  margin-right: 4px;
}
ul.breadcrumb span {
  color: #5e5e5e;
}
.list_toolbar {
  padding: 4px 0 4px 0;
  vertical-align: middle;
}
.list_toolbar .tree-buttons {
  padding-top: 1px;
}
[dir="rtl"] .list_toolbar .tree-buttons {
  float: left !important;
}
[dir="rtl"] .list_toolbar .pull-right {
  padding-top: 1px;
  float: left !important;
}
[dir="rtl"] .list_toolbar .pull-left {
  float: right !important;
}
.dynamic-buttons {
  padding-top: 3px;
  display: inline-block;
}
.list_toolbar [class*="span"] {
  min-height: 24px;
}
.list_header {
  font-weight: bold;
  background-color: #EEE;
}
.list_placeholder {
  font-weight: bold;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
}
.list_container {
  margin-top: 4px;
  margin-bottom: 20px;
  border: 1px solid #ddd;
  border-radius: 2px;
}
.list_container > div {
  border-bottom: 1px solid #ddd;
}
.list_container > div:hover .list-item {
  background-color: red;
}
.list_container > div:last-child {
  border: none;
}
.list_item:hover .list_item {
  background-color: #ddd;
}
.list_item a {
  text-decoration: none;
}
.list_item:hover {
  background-color: #fafafa;
}
.list_header > div,
.list_item > div {
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
  line-height: 22px;
}
.list_header > div input,
.list_item > div input {
  margin-right: 7px;
  margin-left: 14px;
  vertical-align: baseline;
  line-height: 22px;
  position: relative;
  top: -1px;
}
.list_header > div .item_link,
.list_item > div .item_link {
  margin-left: -1px;
  vertical-align: baseline;
  line-height: 22px;
}
.new-file input[type=checkbox] {
  visibility: hidden;
}
.item_name {
  line-height: 22px;
  height: 24px;
}
.item_icon {
  font-size: 14px;
  color: #5e5e5e;
  margin-right: 7px;
  margin-left: 7px;
  line-height: 22px;
  vertical-align: baseline;
}
.item_buttons {
  line-height: 1em;
  margin-left: -5px;
}
.item_buttons .btn,
.item_buttons .btn-group,
.item_buttons .input-group {
  float: left;
}
.item_buttons > .btn,
.item_buttons > .btn-group,
.item_buttons > .input-group {
  margin-left: 5px;
}
.item_buttons .btn {
  min-width: 13ex;
}
.item_buttons .running-indicator {
  padding-top: 4px;
  color: #5cb85c;
}
.item_buttons .kernel-name {
  padding-top: 4px;
  color: #5bc0de;
  margin-right: 7px;
  float: left;
}
.toolbar_info {
  height: 24px;
  line-height: 24px;
}
.list_item input:not([type=checkbox]) {
  padding-top: 3px;
  padding-bottom: 3px;
  height: 22px;
  line-height: 14px;
  margin: 0px;
}
.highlight_text {
  color: blue;
}
#project_name {
  display: inline-block;
  padding-left: 7px;
  margin-left: -2px;
}
#project_name > .breadcrumb {
  padding: 0px;
  margin-bottom: 0px;
  background-color: transparent;
  font-weight: bold;
}
#tree-selector {
  padding-right: 0px;
}
[dir="rtl"] #tree-selector a {
  float: right;
}
#button-select-all {
  min-width: 50px;
}
#select-all {
  margin-left: 7px;
  margin-right: 2px;
}
.menu_icon {
  margin-right: 2px;
}
.tab-content .row {
  margin-left: 0px;
  margin-right: 0px;
}
.folder_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f114";
}
.folder_icon:before.pull-left {
  margin-right: .3em;
}
.folder_icon:before.pull-right {
  margin-left: .3em;
}
.notebook_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f02d";
  position: relative;
  top: -1px;
}
.notebook_icon:before.pull-left {
  margin-right: .3em;
}
.notebook_icon:before.pull-right {
  margin-left: .3em;
}
.running_notebook_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f02d";
  position: relative;
  top: -1px;
  color: #5cb85c;
}
.running_notebook_icon:before.pull-left {
  margin-right: .3em;
}
.running_notebook_icon:before.pull-right {
  margin-left: .3em;
}
.file_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f016";
  position: relative;
  top: -2px;
}
.file_icon:before.pull-left {
  margin-right: .3em;
}
.file_icon:before.pull-right {
  margin-left: .3em;
}
#notebook_toolbar .pull-right {
  padding-top: 0px;
  margin-right: -1px;
}
ul#new-menu {
  left: auto;
  right: 0;
}
[dir="rtl"] #new-menu {
  text-align: right;
}
.kernel-menu-icon {
  padding-right: 12px;
  width: 24px;
  content: "\f096";
}
.kernel-menu-icon:before {
  content: "\f096";
}
.kernel-menu-icon-current:before {
  content: "\f00c";
}
#tab_content {
  padding-top: 20px;
}
#running .panel-group .panel {
  margin-top: 3px;
  margin-bottom: 1em;
}
#running .panel-group .panel .panel-heading {
  background-color: #EEE;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
  line-height: 22px;
}
#running .panel-group .panel .panel-heading a:focus,
#running .panel-group .panel .panel-heading a:hover {
  text-decoration: none;
}
#running .panel-group .panel .panel-body {
  padding: 0px;
}
#running .panel-group .panel .panel-body .list_container {
  margin-top: 0px;
  margin-bottom: 0px;
  border: 0px;
  border-radius: 0px;
}
#running .panel-group .panel .panel-body .list_container .list_item {
  border-bottom: 1px solid #ddd;
}
#running .panel-group .panel .panel-body .list_container .list_item:last-child {
  border-bottom: 0px;
}
[dir="rtl"] #running .col-sm-8 {
  float: right !important;
}
.delete-button {
  display: none;
}
.duplicate-button {
  display: none;
}
.rename-button {
  display: none;
}
.shutdown-button {
  display: none;
}
.dynamic-instructions {
  display: inline-block;
  padding-top: 4px;
}
/*!
*
* IPython text editor webapp
*
*/
.selected-keymap i.fa {
  padding: 0px 5px;
}
.selected-keymap i.fa:before {
  content: "\f00c";
}
#mode-menu {
  overflow: auto;
  max-height: 20em;
}
.edit_app #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.edit_app #menubar .navbar {
  /* Use a negative 1 bottom margin, so the border overlaps the border of the
    header */
  margin-bottom: -1px;
}
.dirty-indicator {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator.pull-left {
  margin-right: .3em;
}
.dirty-indicator.pull-right {
  margin-left: .3em;
}
.dirty-indicator-dirty {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator-dirty.pull-left {
  margin-right: .3em;
}
.dirty-indicator-dirty.pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator-clean.pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean.pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f00c";
}
.dirty-indicator-clean:before.pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean:before.pull-right {
  margin-left: .3em;
}
#filename {
  font-size: 16pt;
  display: table;
  padding: 0px 5px;
}
#current-mode {
  padding-left: 5px;
  padding-right: 5px;
}
#texteditor-backdrop {
  padding-top: 20px;
  padding-bottom: 20px;
}
@media not print {
  #texteditor-backdrop {
    background-color: #EEE;
  }
}
@media print {
  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
    background-color: #fff;
  }
}
@media not print {
  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
    background-color: #fff;
  }
}
@media not print {
  #texteditor-backdrop #texteditor-container {
    padding: 0px;
    background-color: #fff;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
/*!
*
* IPython notebook
*
*/
/* CSS font colors for translated ANSI colors. */
.ansibold {
  font-weight: bold;
}
/* use dark versions for foreground, to improve visibility */
.ansiblack {
  color: black;
}
.ansired {
  color: darkred;
}
.ansigreen {
  color: darkgreen;
}
.ansiyellow {
  color: #c4a000;
}
.ansiblue {
  color: darkblue;
}
.ansipurple {
  color: darkviolet;
}
.ansicyan {
  color: steelblue;
}
.ansigray {
  color: gray;
}
/* and light for background, for the same reason */
.ansibgblack {
  background-color: black;
}
.ansibgred {
  background-color: red;
}
.ansibggreen {
  background-color: green;
}
.ansibgyellow {
  background-color: yellow;
}
.ansibgblue {
  background-color: blue;
}
.ansibgpurple {
  background-color: magenta;
}
.ansibgcyan {
  background-color: cyan;
}
.ansibggray {
  background-color: gray;
}
div.cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  border-radius: 2px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  border-width: 1px;
  border-style: solid;
  border-color: transparent;
  width: 100%;
  padding: 5px;
  /* This acts as a spacer between cells, that is outside the border */
  margin: 0px;
  outline: none;
  border-left-width: 1px;
  padding-left: 5px;
  background: linear-gradient(to right, transparent -40px, transparent 1px, transparent 1px, transparent 100%);
}
div.cell.jupyter-soft-selected {
  border-left-color: #90CAF9;
  border-left-color: #E3F2FD;
  border-left-width: 1px;
  padding-left: 5px;
  border-right-color: #E3F2FD;
  border-right-width: 1px;
  background: #E3F2FD;
}
@media print {
  div.cell.jupyter-soft-selected {
    border-color: transparent;
  }
}
div.cell.selected {
  border-color: #ababab;
  border-left-width: 0px;
  padding-left: 6px;
  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 5px, transparent 5px, transparent 100%);
}
@media print {
  div.cell.selected {
    border-color: transparent;
  }
}
div.cell.selected.jupyter-soft-selected {
  border-left-width: 0;
  padding-left: 6px;
  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 7px, #E3F2FD 7px, #E3F2FD 100%);
}
.edit_mode div.cell.selected {
  border-color: #66BB6A;
  border-left-width: 0px;
  padding-left: 6px;
  background: linear-gradient(to right, #66BB6A -40px, #66BB6A 5px, transparent 5px, transparent 100%);
}
@media print {
  .edit_mode div.cell.selected {
    border-color: transparent;
  }
}
.prompt {
  /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
  min-width: 14ex;
  /* This padding is tuned to match the padding on the CodeMirror editor. */
  padding: 0.4em;
  margin: 0px;
  font-family: monospace;
  text-align: right;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
  /* Don't highlight prompt number selection */
  -webkit-touch-callout: none;
  -webkit-user-select: none;
  -khtml-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
  /* Use default cursor */
  cursor: default;
}
@media (max-width: 540px) {
  .prompt {
    text-align: left;
  }
}
div.inner_cell {
  min-width: 0;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_area {
  border: 1px solid #cfcfcf;
  border-radius: 2px;
  background: #f7f7f7;
  line-height: 1.21429em;
}
/* This is needed so that empty prompt areas can collapse to zero height when there
   is no content in the output_subarea and the prompt. The main purpose of this is
   to make sure that empty JavaScript output_subareas have no height. */
div.prompt:empty {
  padding-top: 0;
  padding-bottom: 0;
}
div.unrecognized_cell {
  padding: 5px 5px 5px 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.unrecognized_cell .inner_cell {
  border-radius: 2px;
  padding: 5px;
  font-weight: bold;
  color: red;
  border: 1px solid #cfcfcf;
  background: #eaeaea;
}
div.unrecognized_cell .inner_cell a {
  color: inherit;
  text-decoration: none;
}
div.unrecognized_cell .inner_cell a:hover {
  color: inherit;
  text-decoration: none;
}
@media (max-width: 540px) {
  div.unrecognized_cell > div.prompt {
    display: none;
  }
}
div.code_cell {
  /* avoid page breaking on code cells when printing */
}
@media print {
  div.code_cell {
    page-break-inside: avoid;
  }
}
/* any special styling for code cells that are currently running goes here */
div.input {
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.input {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_prompt {
  color: #303F9F;
  border-top: 1px solid transparent;
}
div.input_area > div.highlight {
  margin: 0.4em;
  border: none;
  padding: 0px;
  background-color: transparent;
}
div.input_area > div.highlight > pre {
  margin: 0px;
  border: none;
  padding: 0px;
  background-color: transparent;
}
/* The following gets added to the <head> if it is detected that the user has a
 * monospace font with inconsistent normal/bold/italic height.  See
 * notebookmain.js.  Such fonts will have keywords vertically offset with
 * respect to the rest of the text.  The user should select a better font.
 * See: https://github.com/ipython/ipython/issues/1503
 *
 * .CodeMirror span {
 *      vertical-align: bottom;
 * }
 */
.CodeMirror {
  line-height: 1.21429em;
  /* Changed from 1em to our global default */
  font-size: 14px;
  height: auto;
  /* Changed to auto to autogrow */
  background: none;
  /* Changed from white to allow our bg to show through */
}
.CodeMirror-scroll {
  /*  The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
  /*  We have found that if it is visible, vertical scrollbars appear with font size changes.*/
  overflow-y: hidden;
  overflow-x: auto;
}
.CodeMirror-lines {
  /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
  /* we have set a different line-height and want this to scale with that. */
  padding: 0.4em;
}
.CodeMirror-linenumber {
  padding: 0 8px 0 4px;
}
.CodeMirror-gutters {
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.CodeMirror pre {
  /* In CM3 this went to 4px from 0 in CM2. We need the 0 value because of how we size */
  /* .CodeMirror-lines */
  padding: 0;
  border: 0;
  border-radius: 0;
}
/*

Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
Adapted from GitHub theme

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul {
  list-style: disc;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ul ul {
  list-style: square;
  margin: 0em 2em;
}
.rendered_html ul ul ul {
  list-style: circle;
  margin: 0em 2em;
}
.rendered_html ol {
  list-style: decimal;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
  margin: 0em 2em;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  background-color: #fff;
  color: #000;
  font-size: 100%;
  padding: 0px;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: 1px solid black;
  border-collapse: collapse;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  border: 1px solid black;
  border-collapse: collapse;
  margin: 1em 2em;
}
.rendered_html td,
.rendered_html th {
  text-align: left;
  vertical-align: middle;
  padding: 4px;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget {
  float: right !important;
  float: right;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  background-image: none;
}
.notification_widget.success.disabled:hover,
.notification_widget.success[disabled]:hover,
fieldset[disabled] .notification_widget.success:hover,
.notification_widget.success.disabled:focus,
.notification_widget.success[disabled]:focus,
fieldset[disabled] .notification_widget.success:focus,
.notification_widget.success.disabled.focus,
.notification_widget.success[disabled].focus,
fieldset[disabled] .notification_widget.success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.notification_widget.info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info:focus,
.notification_widget.info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.notification_widget.info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active:hover,
.notification_widget.info.active:hover,
.open > .dropdown-toggle.notification_widget.info:hover,
.notification_widget.info:active:focus,
.notification_widget.info.active:focus,
.open > .dropdown-toggle.notification_widget.info:focus,
.notification_widget.info:active.focus,
.notification_widget.info.active.focus,
.open > .dropdown-toggle.notification_widget.info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  background-image: none;
}
.notification_widget.info.disabled:hover,
.notification_widget.info[disabled]:hover,
fieldset[disabled] .notification_widget.info:hover,
.notification_widget.info.disabled:focus,
.notification_widget.info[disabled]:focus,
fieldset[disabled] .notification_widget.info:focus,
.notification_widget.info.disabled.focus,
.notification_widget.info[disabled].focus,
fieldset[disabled] .notification_widget.info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.notification_widget.danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger:focus,
.notification_widget.danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.notification_widget.danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active:hover,
.notification_widget.danger.active:hover,
.open > .dropdown-toggle.notification_widget.danger:hover,
.notification_widget.danger:active:focus,
.notification_widget.danger.active:focus,
.open > .dropdown-toggle.notification_widget.danger:focus,
.notification_widget.danger:active.focus,
.notification_widget.danger.active.focus,
.open > .dropdown-toggle.notification_widget.danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  background-image: none;
}
.notification_widget.danger.disabled:hover,
.notification_widget.danger[disabled]:hover,
fieldset[disabled] .notification_widget.danger:hover,
.notification_widget.danger.disabled:focus,
.notification_widget.danger[disabled]:focus,
fieldset[disabled] .notification_widget.danger:focus,
.notification_widget.danger.disabled.focus,
.notification_widget.danger[disabled].focus,
fieldset[disabled] .notification_widget.danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger .badge {
  color: #d9534f;
  background-color: #fff;
}
div#pager {
  background-color: #fff;
  font-size: 14px;
  line-height: 20px;
  overflow: hidden;
  display: none;
  position: fixed;
  bottom: 0px;
  width: 100%;
  max-height: 50%;
  padding-top: 8px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  /* Display over codemirror */
  z-index: 100;
  /* Hack which prevents jquery ui resizable from changing top. */
  top: auto !important;
}
div#pager pre {
  line-height: 1.21429em;
  color: #000;
  background-color: #f7f7f7;
  padding: 0.4em;
}
div#pager #pager-button-area {
  position: absolute;
  top: 8px;
  right: 20px;
}
div#pager #pager-contents {
  position: relative;
  overflow: auto;
  width: 100%;
  height: 100%;
}
div#pager #pager-contents #pager-container {
  position: relative;
  padding: 15px 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
div#pager .ui-resizable-handle {
  top: 0px;
  height: 8px;
  background: #f7f7f7;
  border-top: 1px solid #cfcfcf;
  border-bottom: 1px solid #cfcfcf;
  /* This injects handle bars (a short, wide = symbol) for 
        the resize handle. */
}
div#pager .ui-resizable-handle::after {
  content: '';
  top: 2px;
  left: 50%;
  height: 3px;
  width: 30px;
  margin-left: -15px;
  position: absolute;
  border-top: 1px solid #cfcfcf;
}
.quickhelp {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  line-height: 1.8em;
}
.shortcut_key {
  display: inline-block;
  width: 21ex;
  text-align: right;
  font-family: monospace;
}
.shortcut_descr {
  display: inline-block;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
span.save_widget {
  margin-top: 6px;
}
span.save_widget span.filename {
  height: 1em;
  line-height: 1em;
  padding: 3px;
  margin-left: 16px;
  border: none;
  font-size: 146.5%;
  border-radius: 2px;
}
span.save_widget span.filename:hover {
  background-color: #e6e6e6;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
}
@media (max-width: 767px) {
  span.save_widget {
    font-size: small;
  }
  span.checkpoint_status,
  span.autosave_status {
    display: none;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  span.checkpoint_status {
    display: none;
  }
  span.autosave_status {
    font-size: x-small;
  }
}
.toolbar {
  padding: 0px;
  margin-left: -5px;
  margin-top: 2px;
  margin-bottom: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.toolbar select,
.toolbar label {
  width: auto;
  vertical-align: middle;
  margin-right: 2px;
  margin-bottom: 0px;
  display: inline;
  font-size: 92%;
  margin-left: 0.3em;
  margin-right: 0.3em;
  padding: 0px;
  padding-top: 3px;
}
.toolbar .btn {
  padding: 2px 8px;
}
.toolbar .btn-group {
  margin-top: 0px;
  margin-left: 5px;
}
#maintoolbar {
  margin-bottom: -3px;
  margin-top: -8px;
  border: 0px;
  min-height: 27px;
  margin-left: 0px;
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">sklearn</span>

<span class="o">%</span><span class="k">matplotlib</span> inline
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># import the audio features</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_pickle</span><span class="p">(</span><span class="s1">&#39;audio_features.pkl&#39;</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">int_loc_map</span> <span class="o">=</span> <span class="p">{</span><span class="mi">14</span><span class="p">:</span> <span class="s1">&#39;beach&#39;</span><span class="p">,</span> <span class="mi">11</span><span class="p">:</span><span class="s1">&#39;bus&#39;</span><span class="p">,</span> <span class="mi">7</span><span class="p">:</span> <span class="s1">&#39;cafe/restaurant&#39;</span><span class="p">,</span> <span class="mi">2</span><span class="p">:</span><span class="s1">&#39;car&#39;</span><span class="p">,</span> <span class="mi">3</span><span class="p">:</span><span class="s1">&#39;city_center&#39;</span><span class="p">,</span> <span class="mi">6</span><span class="p">:</span><span class="s1">&#39;forest_path&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">:</span><span class="s1">&#39;grocery_store&#39;</span><span class="p">,</span> <span class="mi">8</span><span class="p">:</span><span class="s1">&#39;home&#39;</span><span class="p">,</span> <span class="mi">12</span><span class="p">:</span><span class="s1">&#39;library&#39;</span><span class="p">,</span> <span class="mi">13</span><span class="p">:</span><span class="s1">&#39;metro_station&#39;</span><span class="p">,</span> <span class="mi">15</span><span class="p">:</span><span class="s1">&#39;office&#39;</span><span class="p">,</span> <span class="mi">9</span><span class="p">:</span><span class="s1">&#39;park&#39;</span><span class="p">,</span> <span class="mi">4</span><span class="p">:</span><span class="s1">&#39;residential_area&#39;</span><span class="p">,</span> <span class="mi">5</span><span class="p">:</span><span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="mi">10</span><span class="p">:</span><span class="s1">&#39;tram&#39;</span><span class="p">}</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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      <td>...</td>
      <td>0.003940</td>
      <td>0.001392</td>
      <td>5783.338734</td>
      <td>405.385235</td>
      <td>3230.235048</td>
      <td>301.789578</td>
      <td>1.271621</td>
      <td>0.412785</td>
      <td>0.085348</td>
      <td>0.019375</td>
    </tr>
    <tr>
      <th>4</th>
      <td>14</td>
      <td>-438.519412</td>
      <td>31.680932</td>
      <td>129.181425</td>
      <td>8.782248</td>
      <td>-58.355813</td>
      <td>6.728568</td>
      <td>25.899696</td>
      <td>6.163876</td>
      <td>-4.961766</td>
      <td>...</td>
      <td>0.003133</td>
      <td>0.000929</td>
      <td>5841.843097</td>
      <td>325.315548</td>
      <td>3342.164413</td>
      <td>236.548179</td>
      <td>1.270838</td>
      <td>0.386981</td>
      <td>0.099330</td>
      <td>0.013661</td>
    </tr>
  </tbody>
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<p>5 rows × 51 columns</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">y</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;label&#39;</span><span class="p">]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">X</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;label&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">X</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>mfcc_01_mean</th>
      <th>mfcc_01_std</th>
      <th>mfcc_02_mean</th>
      <th>mfcc_02_std</th>
      <th>mfcc_03_mean</th>
      <th>mfcc_03_std</th>
      <th>mfcc_04_mean</th>
      <th>mfcc_04_std</th>
      <th>mfcc_05_mean</th>
      <th>mfcc_05_std</th>
      <th>...</th>
      <th>rmse_mean</th>
      <th>rmse_std</th>
      <th>rolloff_mean</th>
      <th>rolloff_std</th>
      <th>spec_centoid_mean</th>
      <th>spec_centoid_std</th>
      <th>spec_flux_mean</th>
      <th>spec_flux_std</th>
      <th>zcr_mean</th>
      <th>zcr_std</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>-431.829531</td>
      <td>31.289929</td>
      <td>135.895387</td>
      <td>8.809357</td>
      <td>-53.725513</td>
      <td>5.927891</td>
      <td>24.895814</td>
      <td>5.725864</td>
      <td>-3.183241</td>
      <td>5.771691</td>
      <td>...</td>
      <td>0.003790</td>
      <td>0.000803</td>
      <td>5579.098051</td>
      <td>346.033500</td>
      <td>3120.416928</td>
      <td>302.216856</td>
      <td>1.249412</td>
      <td>0.333877</td>
      <td>0.082505</td>
      <td>0.019671</td>
    </tr>
    <tr>
      <th>1</th>
      <td>-436.539917</td>
      <td>26.518017</td>
      <td>144.580086</td>
      <td>10.041389</td>
      <td>-50.765986</td>
      <td>6.809961</td>
      <td>23.333789</td>
      <td>6.239996</td>
      <td>-0.538261</td>
      <td>6.159258</td>
      <td>...</td>
      <td>0.005367</td>
      <td>0.002189</td>
      <td>5121.480013</td>
      <td>532.699615</td>
      <td>2677.467822</td>
      <td>423.000073</td>
      <td>1.254992</td>
      <td>0.343668</td>
      <td>0.056097</td>
      <td>0.022570</td>
    </tr>
    <tr>
      <th>2</th>
      <td>-434.430916</td>
      <td>24.856232</td>
      <td>137.971707</td>
      <td>9.206689</td>
      <td>-53.072679</td>
      <td>6.447099</td>
      <td>24.839483</td>
      <td>5.899629</td>
      <td>-2.037454</td>
      <td>5.491702</td>
      <td>...</td>
      <td>0.004325</td>
      <td>0.001139</td>
      <td>5450.023734</td>
      <td>399.612084</td>
      <td>2972.668779</td>
      <td>329.724678</td>
      <td>1.257049</td>
      <td>0.339699</td>
      <td>0.069154</td>
      <td>0.020514</td>
    </tr>
    <tr>
      <th>3</th>
      <td>-429.411378</td>
      <td>29.635465</td>
      <td>130.319024</td>
      <td>9.218251</td>
      <td>-53.530134</td>
      <td>5.817122</td>
      <td>25.646563</td>
      <td>6.147800</td>
      <td>-3.226847</td>
      <td>5.835915</td>
      <td>...</td>
      <td>0.003940</td>
      <td>0.001392</td>
      <td>5783.338734</td>
      <td>405.385235</td>
      <td>3230.235048</td>
      <td>301.789578</td>
      <td>1.271621</td>
      <td>0.412785</td>
      <td>0.085348</td>
      <td>0.019375</td>
    </tr>
    <tr>
      <th>4</th>
      <td>-438.519412</td>
      <td>31.680932</td>
      <td>129.181425</td>
      <td>8.782248</td>
      <td>-58.355813</td>
      <td>6.728568</td>
      <td>25.899696</td>
      <td>6.163876</td>
      <td>-4.961766</td>
      <td>6.464432</td>
      <td>...</td>
      <td>0.003133</td>
      <td>0.000929</td>
      <td>5841.843097</td>
      <td>325.315548</td>
      <td>3342.164413</td>
      <td>236.548179</td>
      <td>1.270838</td>
      <td>0.386981</td>
      <td>0.099330</td>
      <td>0.013661</td>
    </tr>
  </tbody>
</table>
<p>5 rows × 50 columns</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="k">import</span> <span class="n">train_test_split</span>

<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1356</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">X_train</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<table border="1" class="dataframe">
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    <tr style="text-align: right;">
      <th></th>
      <th>mfcc_01_mean</th>
      <th>mfcc_01_std</th>
      <th>mfcc_02_mean</th>
      <th>mfcc_02_std</th>
      <th>mfcc_03_mean</th>
      <th>mfcc_03_std</th>
      <th>mfcc_04_mean</th>
      <th>mfcc_04_std</th>
      <th>mfcc_05_mean</th>
      <th>mfcc_05_std</th>
      <th>...</th>
      <th>rmse_mean</th>
      <th>rmse_std</th>
      <th>rolloff_mean</th>
      <th>rolloff_std</th>
      <th>spec_centoid_mean</th>
      <th>spec_centoid_std</th>
      <th>spec_flux_mean</th>
      <th>spec_flux_std</th>
      <th>zcr_mean</th>
      <th>zcr_std</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>4065</th>
      <td>-440.187558</td>
      <td>82.527257</td>
      <td>64.392595</td>
      <td>30.802939</td>
      <td>-35.776806</td>
      <td>21.030260</td>
      <td>41.708039</td>
      <td>11.703217</td>
      <td>-0.533726</td>
      <td>12.253490</td>
      <td>...</td>
      <td>0.003889</td>
      <td>0.003346</td>
      <td>9721.266971</td>
      <td>1787.999061</td>
      <td>5302.897524</td>
      <td>1035.480297</td>
      <td>1.254196</td>
      <td>0.801711</td>
      <td>0.167646</td>
      <td>0.063456</td>
    </tr>
    <tr>
      <th>1416</th>
      <td>-512.741784</td>
      <td>19.889379</td>
      <td>149.537087</td>
      <td>14.668363</td>
      <td>-14.520807</td>
      <td>9.296644</td>
      <td>50.816664</td>
      <td>9.956619</td>
      <td>16.900872</td>
      <td>7.939784</td>
      <td>...</td>
      <td>0.005219</td>
      <td>0.001632</td>
      <td>4669.332709</td>
      <td>938.561235</td>
      <td>2167.623639</td>
      <td>465.505843</td>
      <td>1.220943</td>
      <td>0.439326</td>
      <td>0.043411</td>
      <td>0.022282</td>
    </tr>
    <tr>
      <th>2261</th>
      <td>-456.054945</td>
      <td>21.662896</td>
      <td>205.446998</td>
      <td>6.448543</td>
      <td>-26.614992</td>
      <td>8.533651</td>
      <td>31.997193</td>
      <td>9.615701</td>
      <td>27.096909</td>
      <td>6.234048</td>
      <td>...</td>
      <td>0.012295</td>
      <td>0.003563</td>
      <td>2371.500122</td>
      <td>341.708539</td>
      <td>1122.420405</td>
      <td>137.324864</td>
      <td>1.204051</td>
      <td>0.364150</td>
      <td>0.017761</td>
      <td>0.006143</td>
    </tr>
    <tr>
      <th>2173</th>
      <td>-549.517751</td>
      <td>10.961404</td>
      <td>191.978345</td>
      <td>8.297782</td>
      <td>-4.994147</td>
      <td>6.922257</td>
      <td>32.207744</td>
      <td>6.121659</td>
      <td>21.103760</td>
      <td>6.014747</td>
      <td>...</td>
      <td>0.005182</td>
      <td>0.001053</td>
      <td>2075.805777</td>
      <td>480.966629</td>
      <td>1216.923590</td>
      <td>178.405499</td>
      <td>1.221205</td>
      <td>0.299320</td>
      <td>0.019082</td>
      <td>0.007279</td>
    </tr>
    <tr>
      <th>3326</th>
      <td>-441.079252</td>
      <td>26.010250</td>
      <td>172.588627</td>
      <td>12.746169</td>
      <td>55.294870</td>
      <td>16.623113</td>
      <td>25.539507</td>
      <td>9.572419</td>
      <td>25.285492</td>
      <td>6.527616</td>
      <td>...</td>
      <td>0.046476</td>
      <td>0.022036</td>
      <td>485.171544</td>
      <td>294.009338</td>
      <td>398.186692</td>
      <td>142.897878</td>
      <td>1.113851</td>
      <td>0.439893</td>
      <td>0.004888</td>
      <td>0.002192</td>
    </tr>
  </tbody>
</table>
<p>5 rows × 50 columns</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">y_train</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<pre>4065     8
1416     1
2261    13
2173    13
3326    10
Name: label, dtype: int64</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># pre-process the data</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="k">import</span> <span class="n">preprocessing</span>

<span class="c1"># standard scaling</span>
<span class="n">std_scaler</span> <span class="o">=</span> <span class="n">preprocessing</span><span class="o">.</span><span class="n">StandardScaler</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="n">X_train_std</span> <span class="o">=</span> <span class="n">std_scaler</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="n">X_test_std</span> <span class="o">=</span> <span class="n">std_scaler</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>

<span class="c1"># min max scaling</span>
<span class="n">minmax_scaler</span> <span class="o">=</span> <span class="n">preprocessing</span><span class="o">.</span><span class="n">MinMaxScaler</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="n">X_train_minmax</span> <span class="o">=</span> <span class="n">minmax_scaler</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="n">X_test_minmax</span> <span class="o">=</span> <span class="n">minmax_scaler</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># k nearest neighbours</span>
<span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="k">import</span> <span class="n">KNeighborsClassifier</span>

<span class="n">scores</span><span class="o">=</span><span class="p">[]</span>

<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">11</span><span class="p">):</span>
    <span class="n">neigh</span><span class="o">=</span><span class="n">KNeighborsClassifier</span><span class="p">(</span><span class="n">n_neighbors</span> <span class="o">=</span> <span class="n">i</span><span class="p">)</span>
    <span class="n">neigh</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_std</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
    <span class="n">scores</span> <span class="o">+=</span> <span class="p">[</span><span class="n">neigh</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test_std</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)]</span>
    
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">11</span><span class="p">),</span> <span class="n">scores</span><span class="p">)</span>
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<pre>[&lt;matplotlib.lines.Line2D at 0xb6729dde48&gt;]</pre>
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<div class="prompt input_prompt">In&nbsp;[41]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># the same but without preprocessing</span>

<span class="n">scores</span><span class="o">=</span><span class="p">[]</span>

<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">11</span><span class="p">):</span>
    <span class="n">neigh</span><span class="o">=</span><span class="n">KNeighborsClassifier</span><span class="p">(</span><span class="n">n_neighbors</span> <span class="o">=</span> <span class="n">i</span><span class="p">)</span>
    <span class="n">neigh</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
    <span class="n">scores</span> <span class="o">+=</span> <span class="p">[</span><span class="n">neigh</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)]</span>
    
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">11</span><span class="p">),</span> <span class="n">scores</span><span class="p">)</span>
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<div class="prompt output_prompt">Out[41]:</div>


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<pre>[&lt;matplotlib.lines.Line2D at 0xb672a69b38&gt;]</pre>
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<div class="prompt input_prompt">In&nbsp;[42]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># the same but minmax scaling</span>
<span class="n">scores</span><span class="o">=</span><span class="p">[]</span>

<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">11</span><span class="p">):</span>
    <span class="n">neigh</span><span class="o">=</span><span class="n">KNeighborsClassifier</span><span class="p">(</span><span class="n">n_neighbors</span> <span class="o">=</span> <span class="n">i</span><span class="p">)</span>
    <span class="n">neigh</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
    <span class="n">scores</span> <span class="o">+=</span> <span class="p">[</span><span class="n">neigh</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)]</span>
    
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">11</span><span class="p">),</span> <span class="n">scores</span><span class="p">)</span>
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<div class="prompt output_prompt">Out[42]:</div>


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<pre>[&lt;matplotlib.lines.Line2D at 0xb672af2f60&gt;]</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">neigh</span><span class="o">=</span><span class="n">KNeighborsClassifier</span><span class="p">(</span><span class="n">n_neighbors</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">neigh</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">neigh</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">))</span>
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<pre>0.9538461538461539
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">loc_int_map</span> <span class="o">=</span> <span class="p">{</span><span class="n">int_loc_map</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">int_loc_map</span><span class="o">.</span><span class="n">keys</span><span class="p">()}</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># save the mappings to and from for convenience</span>

<span class="kn">import</span> <span class="nn">pickle</span>

<span class="n">f1</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;locintmap.pkl&#39;</span><span class="p">,</span> <span class="s1">&#39;wb&#39;</span><span class="p">)</span>
<span class="n">f2</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;intlocmap.pkl&#39;</span><span class="p">,</span> <span class="s1">&#39;wb&#39;</span><span class="p">)</span>

<span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">loc_int_map</span><span class="p">,</span> <span class="n">f1</span><span class="p">)</span>
<span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">int_loc_map</span><span class="p">,</span> <span class="n">f2</span><span class="p">)</span>

<span class="n">f1</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
<span class="n">f2</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">precision_recall_fscore_support</span> <span class="k">as</span> <span class="n">metrics</span>

<span class="n">mdf</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">metrics</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">neigh</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_minmax</span><span class="p">))))</span>
<span class="n">mdf</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">16</span><span class="p">)</span>
<span class="n">mdf</span> <span class="o">=</span> <span class="n">mdf</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="n">int_loc_map</span><span class="p">)</span>
<span class="n">mdf</span> <span class="o">=</span> <span class="n">mdf</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="n">index</span><span class="o">=</span><span class="p">{</span><span class="mi">0</span><span class="p">:</span><span class="s1">&#39;precision&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">:</span><span class="s1">&#39;recall&#39;</span><span class="p">,</span> <span class="mi">2</span><span class="p">:</span><span class="s1">&#39;fscore&#39;</span><span class="p">,</span> <span class="mi">3</span><span class="p">:</span><span class="s1">&#39;support&#39;</span><span class="p">})</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="k">import</span> <span class="n">display</span>

<span class="c1"># create a helper function to print the following</span>
<span class="k">def</span> <span class="nf">showmetrics</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">classifier</span><span class="p">):</span>
    <span class="n">mdf</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">metrics</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">))))</span>
    <span class="n">mdf</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">16</span><span class="p">)</span>
    <span class="n">mdf</span> <span class="o">=</span> <span class="n">mdf</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="n">columns</span> <span class="o">=</span> <span class="n">int_loc_map</span><span class="p">)</span>
    <span class="n">mdf</span> <span class="o">=</span> <span class="n">mdf</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="n">index</span><span class="o">=</span><span class="p">{</span><span class="mi">0</span><span class="p">:</span><span class="s1">&#39;precision&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">:</span><span class="s1">&#39;recall&#39;</span><span class="p">,</span> <span class="mi">2</span><span class="p">:</span><span class="s1">&#39;fscore&#39;</span><span class="p">,</span> <span class="mi">3</span><span class="p">:</span><span class="s1">&#39;support&#39;</span><span class="p">})</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Average precision: </span><span class="si">{:.3f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">mdf</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">&#39;precision&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Average recall: </span><span class="si">{:.3f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">mdf</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">&#39;recall&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Average fscore: </span><span class="si">{:.3f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">mdf</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">&#39;fscore&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()))</span>
    <span class="n">display</span><span class="p">(</span><span class="n">mdf</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">showmetrics</span><span class="p">(</span><span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">neigh</span><span class="p">)</span>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.927835</td>
      <td>0.982143</td>
      <td>0.891566</td>
      <td>1.000000</td>
      <td>0.972603</td>
      <td>1.000000</td>
      <td>0.917526</td>
      <td>0.978022</td>
      <td>0.950000</td>
      <td>0.901235</td>
      <td>1.000000</td>
      <td>0.925373</td>
      <td>0.934426</td>
      <td>0.947368</td>
      <td>1.000000</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.957447</td>
      <td>0.932203</td>
      <td>0.973684</td>
      <td>0.806818</td>
      <td>0.972603</td>
      <td>0.976471</td>
      <td>0.956989</td>
      <td>0.946809</td>
      <td>0.974359</td>
      <td>0.948052</td>
      <td>0.962500</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>0.972973</td>
      <td>0.962500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.942408</td>
      <td>0.956522</td>
      <td>0.930818</td>
      <td>0.893082</td>
      <td>0.972603</td>
      <td>0.988095</td>
      <td>0.936842</td>
      <td>0.962162</td>
      <td>0.962025</td>
      <td>0.924051</td>
      <td>0.980892</td>
      <td>0.961240</td>
      <td>0.966102</td>
      <td>0.960000</td>
      <td>0.980892</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># create a helper function that trains, tests and prints the metrics</span>
<span class="k">def</span> <span class="nf">train_test_report</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">classifier</span><span class="p">):</span>
    <span class="n">classifier</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Accuracy: </span><span class="si">{:.3f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">classifier</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)))</span>
    <span class="n">showmetrics</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">classifier</span><span class="p">)</span>
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<div class="prompt input_prompt">In&nbsp;[129]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># the output for the k-nearest neighbours (1 neighbour)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">neigh</span><span class="p">)</span>
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<pre>Accuracy: 0.954
Average precision: 0.955
Average recall: 0.956
Average fscore: 0.955
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    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.927835</td>
      <td>0.982143</td>
      <td>0.891566</td>
      <td>1.000000</td>
      <td>0.972603</td>
      <td>1.000000</td>
      <td>0.917526</td>
      <td>0.978022</td>
      <td>0.950000</td>
      <td>0.901235</td>
      <td>1.000000</td>
      <td>0.925373</td>
      <td>0.934426</td>
      <td>0.947368</td>
      <td>1.000000</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.957447</td>
      <td>0.932203</td>
      <td>0.973684</td>
      <td>0.806818</td>
      <td>0.972603</td>
      <td>0.976471</td>
      <td>0.956989</td>
      <td>0.946809</td>
      <td>0.974359</td>
      <td>0.948052</td>
      <td>0.962500</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>0.972973</td>
      <td>0.962500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.942408</td>
      <td>0.956522</td>
      <td>0.930818</td>
      <td>0.893082</td>
      <td>0.972603</td>
      <td>0.988095</td>
      <td>0.936842</td>
      <td>0.962162</td>
      <td>0.962025</td>
      <td>0.924051</td>
      <td>0.980892</td>
      <td>0.961240</td>
      <td>0.966102</td>
      <td>0.960000</td>
      <td>0.980892</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
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<div class="prompt input_prompt">In&nbsp;[137]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># logistic regression</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="k">import</span> <span class="n">LogisticRegression</span>

<span class="n">logre</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="s1">&#39;lbfgs&#39;</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Without preprocessing&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">logre</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Standard scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_std</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_std</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">logre</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Minmax scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">logre</span><span class="p">)</span>
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<pre>Without preprocessing
Accuracy: 0.785
Average precision: 0.780
Average recall: 0.785
Average fscore: 0.778
</pre>
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    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.882979</td>
      <td>0.852459</td>
      <td>0.791209</td>
      <td>0.812500</td>
      <td>0.600000</td>
      <td>0.918605</td>
      <td>0.715686</td>
      <td>0.843373</td>
      <td>0.747126</td>
      <td>0.666667</td>
      <td>0.807692</td>
      <td>0.681159</td>
      <td>0.700000</td>
      <td>0.838710</td>
      <td>0.838710</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.882979</td>
      <td>0.881356</td>
      <td>0.947368</td>
      <td>0.590909</td>
      <td>0.452055</td>
      <td>0.929412</td>
      <td>0.784946</td>
      <td>0.744681</td>
      <td>0.833333</td>
      <td>0.649351</td>
      <td>0.787500</td>
      <td>0.758065</td>
      <td>0.859649</td>
      <td>0.702703</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.882979</td>
      <td>0.866667</td>
      <td>0.862275</td>
      <td>0.684211</td>
      <td>0.515625</td>
      <td>0.923977</td>
      <td>0.748718</td>
      <td>0.790960</td>
      <td>0.787879</td>
      <td>0.657895</td>
      <td>0.797468</td>
      <td>0.717557</td>
      <td>0.771654</td>
      <td>0.764706</td>
      <td>0.901734</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Standard scaling
Accuracy: 0.857
Average precision: 0.853
Average recall: 0.859
Average fscore: 0.854
</pre>
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<div class="output_html rendered_html output_subarea ">
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    .dataframe thead tr:only-child th {
        text-align: right;
    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.945055</td>
      <td>0.903226</td>
      <td>0.873563</td>
      <td>0.815789</td>
      <td>0.758065</td>
      <td>0.952381</td>
      <td>0.819149</td>
      <td>0.951220</td>
      <td>0.779070</td>
      <td>0.753425</td>
      <td>0.892857</td>
      <td>0.800000</td>
      <td>0.753623</td>
      <td>0.885714</td>
      <td>0.917647</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.914894</td>
      <td>0.949153</td>
      <td>1.000000</td>
      <td>0.704545</td>
      <td>0.643836</td>
      <td>0.941176</td>
      <td>0.827957</td>
      <td>0.829787</td>
      <td>0.858974</td>
      <td>0.714286</td>
      <td>0.937500</td>
      <td>0.838710</td>
      <td>0.912281</td>
      <td>0.837838</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.929730</td>
      <td>0.925620</td>
      <td>0.932515</td>
      <td>0.756098</td>
      <td>0.696296</td>
      <td>0.946746</td>
      <td>0.823529</td>
      <td>0.886364</td>
      <td>0.817073</td>
      <td>0.733333</td>
      <td>0.914634</td>
      <td>0.818898</td>
      <td>0.825397</td>
      <td>0.861111</td>
      <td>0.945455</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.806
Average precision: 0.800
Average recall: 0.805
Average fscore: 0.799
</pre>
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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.841584</td>
      <td>0.787879</td>
      <td>0.902439</td>
      <td>0.833333</td>
      <td>0.679245</td>
      <td>0.952941</td>
      <td>0.818182</td>
      <td>0.857143</td>
      <td>0.764045</td>
      <td>0.623377</td>
      <td>0.811765</td>
      <td>0.711864</td>
      <td>0.675325</td>
      <td>0.887097</td>
      <td>0.855556</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.904255</td>
      <td>0.881356</td>
      <td>0.973684</td>
      <td>0.681818</td>
      <td>0.493151</td>
      <td>0.952941</td>
      <td>0.774194</td>
      <td>0.765957</td>
      <td>0.871795</td>
      <td>0.623377</td>
      <td>0.862500</td>
      <td>0.677419</td>
      <td>0.912281</td>
      <td>0.743243</td>
      <td>0.962500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.871795</td>
      <td>0.832000</td>
      <td>0.936709</td>
      <td>0.750000</td>
      <td>0.571429</td>
      <td>0.952941</td>
      <td>0.795580</td>
      <td>0.808989</td>
      <td>0.814371</td>
      <td>0.623377</td>
      <td>0.836364</td>
      <td>0.694215</td>
      <td>0.776119</td>
      <td>0.808824</td>
      <td>0.905882</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<div class="cell border-box-sizing code_cell rendered">
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<div class="prompt input_prompt">In&nbsp;[139]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># naive-bayes</span>
<span class="kn">from</span> <span class="nn">sklearn.naive_bayes</span> <span class="k">import</span> <span class="n">GaussianNB</span>

<span class="n">gnb</span> <span class="o">=</span> <span class="n">GaussianNB</span><span class="p">()</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Without preprocessing&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">gnb</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Standard scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_std</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_std</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">gnb</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Minmax scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">gnb</span><span class="p">)</span>
</pre></div>

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<pre>Without preprocessing
Accuracy: 0.669
Average precision: 0.683
Average recall: 0.665
Average fscore: 0.662
</pre>
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    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.690000</td>
      <td>0.648649</td>
      <td>0.736264</td>
      <td>0.844444</td>
      <td>0.553191</td>
      <td>0.931507</td>
      <td>0.629213</td>
      <td>0.621622</td>
      <td>0.425373</td>
      <td>0.691176</td>
      <td>0.777778</td>
      <td>0.520000</td>
      <td>0.631579</td>
      <td>0.700000</td>
      <td>0.851351</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.734043</td>
      <td>0.813559</td>
      <td>0.881579</td>
      <td>0.431818</td>
      <td>0.356164</td>
      <td>0.800000</td>
      <td>0.602151</td>
      <td>0.734043</td>
      <td>0.730769</td>
      <td>0.610390</td>
      <td>0.787500</td>
      <td>0.629032</td>
      <td>0.421053</td>
      <td>0.662162</td>
      <td>0.787500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.711340</td>
      <td>0.721805</td>
      <td>0.802395</td>
      <td>0.571429</td>
      <td>0.433333</td>
      <td>0.860759</td>
      <td>0.615385</td>
      <td>0.673171</td>
      <td>0.537736</td>
      <td>0.648276</td>
      <td>0.782609</td>
      <td>0.569343</td>
      <td>0.505263</td>
      <td>0.680556</td>
      <td>0.818182</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Standard scaling
Accuracy: 0.662
Average precision: 0.676
Average recall: 0.656
Average fscore: 0.652
</pre>
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    .dataframe thead tr:only-child th {
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    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.669903</td>
      <td>0.631579</td>
      <td>0.770115</td>
      <td>0.844444</td>
      <td>0.617021</td>
      <td>0.942029</td>
      <td>0.582418</td>
      <td>0.657407</td>
      <td>0.408163</td>
      <td>0.650794</td>
      <td>0.741176</td>
      <td>0.550000</td>
      <td>0.552632</td>
      <td>0.671429</td>
      <td>0.851852</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.734043</td>
      <td>0.813559</td>
      <td>0.881579</td>
      <td>0.431818</td>
      <td>0.397260</td>
      <td>0.764706</td>
      <td>0.569892</td>
      <td>0.755319</td>
      <td>0.769231</td>
      <td>0.532468</td>
      <td>0.787500</td>
      <td>0.532258</td>
      <td>0.368421</td>
      <td>0.635135</td>
      <td>0.862500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.700508</td>
      <td>0.711111</td>
      <td>0.822086</td>
      <td>0.571429</td>
      <td>0.483333</td>
      <td>0.844156</td>
      <td>0.576087</td>
      <td>0.702970</td>
      <td>0.533333</td>
      <td>0.585714</td>
      <td>0.763636</td>
      <td>0.540984</td>
      <td>0.442105</td>
      <td>0.652778</td>
      <td>0.857143</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.662
Average precision: 0.676
Average recall: 0.656
Average fscore: 0.652
</pre>
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<div class="output_html rendered_html output_subarea ">
<div>
<style>
    .dataframe thead tr:only-child th {
        text-align: right;
    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.669903</td>
      <td>0.631579</td>
      <td>0.770115</td>
      <td>0.844444</td>
      <td>0.617021</td>
      <td>0.942029</td>
      <td>0.582418</td>
      <td>0.657407</td>
      <td>0.408163</td>
      <td>0.650794</td>
      <td>0.741176</td>
      <td>0.550000</td>
      <td>0.552632</td>
      <td>0.671429</td>
      <td>0.851852</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.734043</td>
      <td>0.813559</td>
      <td>0.881579</td>
      <td>0.431818</td>
      <td>0.397260</td>
      <td>0.764706</td>
      <td>0.569892</td>
      <td>0.755319</td>
      <td>0.769231</td>
      <td>0.532468</td>
      <td>0.787500</td>
      <td>0.532258</td>
      <td>0.368421</td>
      <td>0.635135</td>
      <td>0.862500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.700508</td>
      <td>0.711111</td>
      <td>0.822086</td>
      <td>0.571429</td>
      <td>0.483333</td>
      <td>0.844156</td>
      <td>0.576087</td>
      <td>0.702970</td>
      <td>0.533333</td>
      <td>0.585714</td>
      <td>0.763636</td>
      <td>0.540984</td>
      <td>0.442105</td>
      <td>0.652778</td>
      <td>0.857143</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
</div>
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<div class="cell border-box-sizing code_cell rendered">
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<div class="prompt input_prompt">In&nbsp;[141]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># svm</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="k">import</span> <span class="n">svm</span>

<span class="n">svm</span> <span class="o">=</span> <span class="n">svm</span><span class="o">.</span><span class="n">SVC</span><span class="p">()</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Without preprocessing&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">svm</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Standard scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_std</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_std</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">svm</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Minmax scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">svm</span><span class="p">)</span>
</pre></div>

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<pre>Without preprocessing
Accuracy: 0.056
Average precision: 0.137
Average recall: 0.075
Average fscore: 0.022
</pre>
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<pre>c:\users\jerem_000\appdata\local\programs\python\python36\lib\site-packages\sklearn\metrics\classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.
  &#39;precision&#39;, &#39;predicted&#39;, average, warn_for)
</pre>
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<div class="output_html rendered_html output_subarea ">
<div>
<style>
    .dataframe thead tr:only-child th {
        text-align: right;
    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.0</td>
      <td>1.000000</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.000000</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.049053</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.0</td>
      <td>0.118644</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.013699</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.000000</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.0</td>
      <td>0.212121</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.027027</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.093519</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.0</td>
      <td>59.000000</td>
      <td>76.0</td>
      <td>88.0</td>
      <td>73.000000</td>
      <td>85.0</td>
      <td>93.0</td>
      <td>94.0</td>
      <td>78.0</td>
      <td>77.0</td>
      <td>80.0</td>
      <td>62.0</td>
      <td>57.000000</td>
      <td>74.0</td>
      <td>80.0</td>
    </tr>
  </tbody>
</table>
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<pre>
Standard scaling
Accuracy: 0.947
Average precision: 0.945
Average recall: 0.948
Average fscore: 0.946
</pre>
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  <thead>
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      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.978947</td>
      <td>0.918033</td>
      <td>0.987013</td>
      <td>0.963415</td>
      <td>0.885714</td>
      <td>0.976190</td>
      <td>0.967742</td>
      <td>0.988636</td>
      <td>0.903614</td>
      <td>0.860759</td>
      <td>0.935897</td>
      <td>0.898551</td>
      <td>0.919355</td>
      <td>1.000000</td>
      <td>0.987342</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.989362</td>
      <td>0.949153</td>
      <td>1.000000</td>
      <td>0.897727</td>
      <td>0.849315</td>
      <td>0.964706</td>
      <td>0.967742</td>
      <td>0.925532</td>
      <td>0.961538</td>
      <td>0.883117</td>
      <td>0.912500</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>0.945946</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.984127</td>
      <td>0.933333</td>
      <td>0.993464</td>
      <td>0.929412</td>
      <td>0.867133</td>
      <td>0.970414</td>
      <td>0.967742</td>
      <td>0.956044</td>
      <td>0.931677</td>
      <td>0.871795</td>
      <td>0.924051</td>
      <td>0.946565</td>
      <td>0.957983</td>
      <td>0.972222</td>
      <td>0.981132</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
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<pre>
Minmax scaling
Accuracy: 0.707
Average precision: 0.725
Average recall: 0.709
Average fscore: 0.697
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.804124</td>
      <td>0.710145</td>
      <td>0.811111</td>
      <td>0.866667</td>
      <td>0.666667</td>
      <td>0.916667</td>
      <td>0.766234</td>
      <td>0.785714</td>
      <td>0.558559</td>
      <td>0.560440</td>
      <td>0.780488</td>
      <td>0.417476</td>
      <td>0.511111</td>
      <td>0.901961</td>
      <td>0.818182</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.829787</td>
      <td>0.830508</td>
      <td>0.960526</td>
      <td>0.443182</td>
      <td>0.273973</td>
      <td>0.905882</td>
      <td>0.634409</td>
      <td>0.702128</td>
      <td>0.794872</td>
      <td>0.662338</td>
      <td>0.800000</td>
      <td>0.693548</td>
      <td>0.807018</td>
      <td>0.621622</td>
      <td>0.675000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.816754</td>
      <td>0.765625</td>
      <td>0.879518</td>
      <td>0.586466</td>
      <td>0.388350</td>
      <td>0.911243</td>
      <td>0.694118</td>
      <td>0.741573</td>
      <td>0.656085</td>
      <td>0.607143</td>
      <td>0.790123</td>
      <td>0.521212</td>
      <td>0.625850</td>
      <td>0.736000</td>
      <td>0.739726</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<div class="cell border-box-sizing code_cell rendered">
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<div class="prompt input_prompt">In&nbsp;[146]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># svm</span>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="k">import</span> <span class="n">SVC</span>

<span class="c1"># trying various C</span>
<span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="p">[</span><span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">100</span><span class="p">]:</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;C parameter: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">c</span><span class="p">))</span>
    <span class="n">svm</span> <span class="o">=</span> <span class="n">SVC</span><span class="p">(</span><span class="n">C</span><span class="o">=</span><span class="n">c</span><span class="p">)</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Standard scaling&quot;</span><span class="p">)</span>
    <span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_std</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_std</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">svm</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">()</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Minmax scaling&quot;</span><span class="p">)</span>
    <span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">svm</span><span class="p">)</span>
</pre></div>

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<pre>C parameter: 0.01
Standard scaling
Accuracy: 0.110
Average precision: 0.106
Average recall: 0.143
Average fscore: 0.066
</pre>
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<pre>c:\users\jerem_000\appdata\local\programs\python\python36\lib\site-packages\sklearn\metrics\classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.
  &#39;precision&#39;, &#39;predicted&#39;, average, warn_for)
</pre>
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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.0</td>
      <td>0.307229</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.000000</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.225000</td>
      <td>0.059874</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.0</td>
      <td>0.864407</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.141176</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.145161</td>
      <td>1.000000</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.0</td>
      <td>0.453333</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.247423</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.176471</td>
      <td>0.112983</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.0</td>
      <td>59.000000</td>
      <td>76.0</td>
      <td>88.0</td>
      <td>73.0</td>
      <td>85.000000</td>
      <td>93.0</td>
      <td>94.0</td>
      <td>78.0</td>
      <td>77.0</td>
      <td>80.0</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.0</td>
      <td>80.0</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.049
Average precision: 0.003
Average recall: 0.067
Average fscore: 0.006
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.048718</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.000000</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.092910</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.0</td>
      <td>59.0</td>
      <td>76.0</td>
      <td>88.0</td>
      <td>73.0</td>
      <td>85.0</td>
      <td>93.0</td>
      <td>94.0</td>
      <td>78.0</td>
      <td>77.0</td>
      <td>80.0</td>
      <td>62.0</td>
      <td>57.000000</td>
      <td>74.0</td>
      <td>80.0</td>
    </tr>
  </tbody>
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<pre>C parameter: 0.1
Standard scaling
Accuracy: 0.815
Average precision: 0.819
Average recall: 0.815
Average fscore: 0.812
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.892473</td>
      <td>0.833333</td>
      <td>0.912500</td>
      <td>0.888889</td>
      <td>0.736111</td>
      <td>0.950617</td>
      <td>0.888889</td>
      <td>0.762376</td>
      <td>0.680412</td>
      <td>0.701149</td>
      <td>0.886076</td>
      <td>0.700000</td>
      <td>0.644737</td>
      <td>0.950000</td>
      <td>0.858824</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.882979</td>
      <td>0.762712</td>
      <td>0.960526</td>
      <td>0.636364</td>
      <td>0.726027</td>
      <td>0.905882</td>
      <td>0.688172</td>
      <td>0.819149</td>
      <td>0.846154</td>
      <td>0.792208</td>
      <td>0.875000</td>
      <td>0.790323</td>
      <td>0.859649</td>
      <td>0.770270</td>
      <td>0.912500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.887701</td>
      <td>0.796460</td>
      <td>0.935897</td>
      <td>0.741722</td>
      <td>0.731034</td>
      <td>0.927711</td>
      <td>0.775758</td>
      <td>0.789744</td>
      <td>0.754286</td>
      <td>0.743902</td>
      <td>0.880503</td>
      <td>0.742424</td>
      <td>0.736842</td>
      <td>0.850746</td>
      <td>0.884848</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
</div>
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<pre>
Minmax scaling
Accuracy: 0.115
Average precision: 0.107
Average recall: 0.144
Average fscore: 0.063
</pre>
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<style>
    .dataframe thead tr:only-child th {
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    }

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        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.0</td>
      <td>0.251185</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.000000</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.285714</td>
      <td>0.061290</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.0</td>
      <td>0.898305</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.234043</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.032258</td>
      <td>1.000000</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.0</td>
      <td>0.392593</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.379310</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.057971</td>
      <td>0.115502</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.0</td>
      <td>59.000000</td>
      <td>76.0</td>
      <td>88.0</td>
      <td>73.0</td>
      <td>85.0</td>
      <td>93.0</td>
      <td>94.000000</td>
      <td>78.0</td>
      <td>77.0</td>
      <td>80.0</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.0</td>
      <td>80.0</td>
    </tr>
  </tbody>
</table>
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<pre>C parameter: 1
Standard scaling
Accuracy: 0.947
Average precision: 0.945
Average recall: 0.948
Average fscore: 0.946
</pre>
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    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.978947</td>
      <td>0.918033</td>
      <td>0.987013</td>
      <td>0.963415</td>
      <td>0.885714</td>
      <td>0.976190</td>
      <td>0.967742</td>
      <td>0.988636</td>
      <td>0.903614</td>
      <td>0.860759</td>
      <td>0.935897</td>
      <td>0.898551</td>
      <td>0.919355</td>
      <td>1.000000</td>
      <td>0.987342</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.989362</td>
      <td>0.949153</td>
      <td>1.000000</td>
      <td>0.897727</td>
      <td>0.849315</td>
      <td>0.964706</td>
      <td>0.967742</td>
      <td>0.925532</td>
      <td>0.961538</td>
      <td>0.883117</td>
      <td>0.912500</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>0.945946</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.984127</td>
      <td>0.933333</td>
      <td>0.993464</td>
      <td>0.929412</td>
      <td>0.867133</td>
      <td>0.970414</td>
      <td>0.967742</td>
      <td>0.956044</td>
      <td>0.931677</td>
      <td>0.871795</td>
      <td>0.924051</td>
      <td>0.946565</td>
      <td>0.957983</td>
      <td>0.972222</td>
      <td>0.981132</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.707
Average precision: 0.725
Average recall: 0.709
Average fscore: 0.697
</pre>
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        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.804124</td>
      <td>0.710145</td>
      <td>0.811111</td>
      <td>0.866667</td>
      <td>0.666667</td>
      <td>0.916667</td>
      <td>0.766234</td>
      <td>0.785714</td>
      <td>0.558559</td>
      <td>0.560440</td>
      <td>0.780488</td>
      <td>0.417476</td>
      <td>0.511111</td>
      <td>0.901961</td>
      <td>0.818182</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.829787</td>
      <td>0.830508</td>
      <td>0.960526</td>
      <td>0.443182</td>
      <td>0.273973</td>
      <td>0.905882</td>
      <td>0.634409</td>
      <td>0.702128</td>
      <td>0.794872</td>
      <td>0.662338</td>
      <td>0.800000</td>
      <td>0.693548</td>
      <td>0.807018</td>
      <td>0.621622</td>
      <td>0.675000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.816754</td>
      <td>0.765625</td>
      <td>0.879518</td>
      <td>0.586466</td>
      <td>0.388350</td>
      <td>0.911243</td>
      <td>0.694118</td>
      <td>0.741573</td>
      <td>0.656085</td>
      <td>0.607143</td>
      <td>0.790123</td>
      <td>0.521212</td>
      <td>0.625850</td>
      <td>0.736000</td>
      <td>0.739726</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>C parameter: 10
Standard scaling
Accuracy: 0.964
Average precision: 0.964
Average recall: 0.966
Average fscore: 0.964
</pre>
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    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.978947</td>
      <td>0.966667</td>
      <td>0.962025</td>
      <td>0.952941</td>
      <td>0.971831</td>
      <td>0.988095</td>
      <td>0.946809</td>
      <td>0.988889</td>
      <td>0.948052</td>
      <td>0.925000</td>
      <td>0.949367</td>
      <td>0.939394</td>
      <td>0.950000</td>
      <td>1.000000</td>
      <td>0.987342</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.989362</td>
      <td>0.983051</td>
      <td>1.000000</td>
      <td>0.920455</td>
      <td>0.945205</td>
      <td>0.976471</td>
      <td>0.956989</td>
      <td>0.946809</td>
      <td>0.935897</td>
      <td>0.961039</td>
      <td>0.937500</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>0.959459</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.984127</td>
      <td>0.974790</td>
      <td>0.980645</td>
      <td>0.936416</td>
      <td>0.958333</td>
      <td>0.982249</td>
      <td>0.951872</td>
      <td>0.967391</td>
      <td>0.941935</td>
      <td>0.942675</td>
      <td>0.943396</td>
      <td>0.968750</td>
      <td>0.974359</td>
      <td>0.979310</td>
      <td>0.981132</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.836
Average precision: 0.834
Average recall: 0.837
Average fscore: 0.832
</pre>
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        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.906250</td>
      <td>0.830769</td>
      <td>0.937500</td>
      <td>0.863014</td>
      <td>0.683333</td>
      <td>0.941176</td>
      <td>0.864198</td>
      <td>0.880952</td>
      <td>0.778947</td>
      <td>0.604396</td>
      <td>0.905405</td>
      <td>0.777778</td>
      <td>0.724638</td>
      <td>0.924242</td>
      <td>0.886364</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.925532</td>
      <td>0.915254</td>
      <td>0.986842</td>
      <td>0.715909</td>
      <td>0.561644</td>
      <td>0.941176</td>
      <td>0.752688</td>
      <td>0.787234</td>
      <td>0.948718</td>
      <td>0.714286</td>
      <td>0.837500</td>
      <td>0.790323</td>
      <td>0.877193</td>
      <td>0.824324</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.915789</td>
      <td>0.870968</td>
      <td>0.961538</td>
      <td>0.782609</td>
      <td>0.616541</td>
      <td>0.941176</td>
      <td>0.804598</td>
      <td>0.831461</td>
      <td>0.855491</td>
      <td>0.654762</td>
      <td>0.870130</td>
      <td>0.784000</td>
      <td>0.793651</td>
      <td>0.871429</td>
      <td>0.928571</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>C parameter: 100
Standard scaling
Accuracy: 0.967
Average precision: 0.966
Average recall: 0.969
Average fscore: 0.967
</pre>
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<style>
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        text-align: right;
    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.978947</td>
      <td>0.983051</td>
      <td>0.962025</td>
      <td>0.952941</td>
      <td>0.972973</td>
      <td>0.987952</td>
      <td>0.946809</td>
      <td>0.988889</td>
      <td>0.948052</td>
      <td>0.937500</td>
      <td>0.974026</td>
      <td>0.939394</td>
      <td>0.950000</td>
      <td>0.986111</td>
      <td>0.987342</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.989362</td>
      <td>0.983051</td>
      <td>1.000000</td>
      <td>0.920455</td>
      <td>0.986301</td>
      <td>0.964706</td>
      <td>0.956989</td>
      <td>0.946809</td>
      <td>0.935897</td>
      <td>0.974026</td>
      <td>0.937500</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>0.959459</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.984127</td>
      <td>0.983051</td>
      <td>0.980645</td>
      <td>0.936416</td>
      <td>0.979592</td>
      <td>0.976190</td>
      <td>0.951872</td>
      <td>0.967391</td>
      <td>0.941935</td>
      <td>0.955414</td>
      <td>0.955414</td>
      <td>0.968750</td>
      <td>0.974359</td>
      <td>0.972603</td>
      <td>0.981132</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.909
Average precision: 0.907
Average recall: 0.908
Average fscore: 0.907
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.938144</td>
      <td>0.901639</td>
      <td>0.915663</td>
      <td>0.865854</td>
      <td>0.833333</td>
      <td>0.976471</td>
      <td>0.898990</td>
      <td>0.964706</td>
      <td>0.864198</td>
      <td>0.802632</td>
      <td>0.947368</td>
      <td>0.865672</td>
      <td>0.866667</td>
      <td>0.984848</td>
      <td>0.975</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.968085</td>
      <td>0.932203</td>
      <td>1.000000</td>
      <td>0.806818</td>
      <td>0.821918</td>
      <td>0.976471</td>
      <td>0.956989</td>
      <td>0.872340</td>
      <td>0.897436</td>
      <td>0.792208</td>
      <td>0.900000</td>
      <td>0.935484</td>
      <td>0.912281</td>
      <td>0.878378</td>
      <td>0.975</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.952880</td>
      <td>0.916667</td>
      <td>0.955975</td>
      <td>0.835294</td>
      <td>0.827586</td>
      <td>0.976471</td>
      <td>0.927083</td>
      <td>0.916201</td>
      <td>0.880503</td>
      <td>0.797386</td>
      <td>0.923077</td>
      <td>0.899225</td>
      <td>0.888889</td>
      <td>0.928571</td>
      <td>0.975</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000</td>
    </tr>
  </tbody>
</table>
</div>
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<div class="cell border-box-sizing code_cell rendered">
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<div class="prompt input_prompt">In&nbsp;[147]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># svm</span>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="k">import</span> <span class="n">SVC</span>

<span class="c1"># trying various kernels</span>
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;linear&#39;</span><span class="p">,</span><span class="s1">&#39;rbf&#39;</span><span class="p">,</span><span class="s1">&#39;poly&#39;</span><span class="p">,</span><span class="s1">&#39;sigmoid&#39;</span><span class="p">]:</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Kernel: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">k</span><span class="p">))</span>
    <span class="n">svm</span> <span class="o">=</span> <span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">k</span><span class="p">)</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Standard scaling&quot;</span><span class="p">)</span>
    <span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_std</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_std</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">svm</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">()</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Minmax scaling&quot;</span><span class="p">)</span>
    <span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">svm</span><span class="p">)</span>
</pre></div>

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<pre>Kernel: linear
Standard scaling
Accuracy: 0.909
Average precision: 0.911
Average recall: 0.912
Average fscore: 0.910
</pre>
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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.956522</td>
      <td>0.933333</td>
      <td>0.904762</td>
      <td>0.839080</td>
      <td>0.768293</td>
      <td>0.987805</td>
      <td>0.875000</td>
      <td>0.965116</td>
      <td>0.862500</td>
      <td>0.900000</td>
      <td>0.935897</td>
      <td>0.909091</td>
      <td>0.870968</td>
      <td>1.000000</td>
      <td>0.950617</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.936170</td>
      <td>0.949153</td>
      <td>1.000000</td>
      <td>0.829545</td>
      <td>0.863014</td>
      <td>0.952941</td>
      <td>0.903226</td>
      <td>0.882979</td>
      <td>0.884615</td>
      <td>0.818182</td>
      <td>0.912500</td>
      <td>0.967742</td>
      <td>0.947368</td>
      <td>0.864865</td>
      <td>0.962500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.946237</td>
      <td>0.941176</td>
      <td>0.950000</td>
      <td>0.834286</td>
      <td>0.812903</td>
      <td>0.970060</td>
      <td>0.888889</td>
      <td>0.922222</td>
      <td>0.873418</td>
      <td>0.857143</td>
      <td>0.924051</td>
      <td>0.937500</td>
      <td>0.907563</td>
      <td>0.927536</td>
      <td>0.956522</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.860
Average precision: 0.858
Average recall: 0.860
Average fscore: 0.857
</pre>
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    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.916667</td>
      <td>0.843750</td>
      <td>0.926829</td>
      <td>0.866667</td>
      <td>0.691176</td>
      <td>0.942529</td>
      <td>0.855556</td>
      <td>0.917647</td>
      <td>0.776596</td>
      <td>0.670732</td>
      <td>0.921053</td>
      <td>0.828125</td>
      <td>0.819672</td>
      <td>0.967742</td>
      <td>0.928571</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.936170</td>
      <td>0.915254</td>
      <td>1.000000</td>
      <td>0.738636</td>
      <td>0.643836</td>
      <td>0.964706</td>
      <td>0.827957</td>
      <td>0.829787</td>
      <td>0.935897</td>
      <td>0.714286</td>
      <td>0.875000</td>
      <td>0.854839</td>
      <td>0.877193</td>
      <td>0.810811</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.926316</td>
      <td>0.878049</td>
      <td>0.962025</td>
      <td>0.797546</td>
      <td>0.666667</td>
      <td>0.953488</td>
      <td>0.841530</td>
      <td>0.871508</td>
      <td>0.848837</td>
      <td>0.691824</td>
      <td>0.897436</td>
      <td>0.841270</td>
      <td>0.847458</td>
      <td>0.882353</td>
      <td>0.951220</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>Kernel: rbf
Standard scaling
Accuracy: 0.947
Average precision: 0.945
Average recall: 0.948
Average fscore: 0.946
</pre>
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    .dataframe tbody tr th {
        vertical-align: top;
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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.978947</td>
      <td>0.918033</td>
      <td>0.987013</td>
      <td>0.963415</td>
      <td>0.885714</td>
      <td>0.976190</td>
      <td>0.967742</td>
      <td>0.988636</td>
      <td>0.903614</td>
      <td>0.860759</td>
      <td>0.935897</td>
      <td>0.898551</td>
      <td>0.919355</td>
      <td>1.000000</td>
      <td>0.987342</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.989362</td>
      <td>0.949153</td>
      <td>1.000000</td>
      <td>0.897727</td>
      <td>0.849315</td>
      <td>0.964706</td>
      <td>0.967742</td>
      <td>0.925532</td>
      <td>0.961538</td>
      <td>0.883117</td>
      <td>0.912500</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>0.945946</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.984127</td>
      <td>0.933333</td>
      <td>0.993464</td>
      <td>0.929412</td>
      <td>0.867133</td>
      <td>0.970414</td>
      <td>0.967742</td>
      <td>0.956044</td>
      <td>0.931677</td>
      <td>0.871795</td>
      <td>0.924051</td>
      <td>0.946565</td>
      <td>0.957983</td>
      <td>0.972222</td>
      <td>0.981132</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.707
Average precision: 0.725
Average recall: 0.709
Average fscore: 0.697
</pre>
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        text-align: left;
    }

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        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.804124</td>
      <td>0.710145</td>
      <td>0.811111</td>
      <td>0.866667</td>
      <td>0.666667</td>
      <td>0.916667</td>
      <td>0.766234</td>
      <td>0.785714</td>
      <td>0.558559</td>
      <td>0.560440</td>
      <td>0.780488</td>
      <td>0.417476</td>
      <td>0.511111</td>
      <td>0.901961</td>
      <td>0.818182</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.829787</td>
      <td>0.830508</td>
      <td>0.960526</td>
      <td>0.443182</td>
      <td>0.273973</td>
      <td>0.905882</td>
      <td>0.634409</td>
      <td>0.702128</td>
      <td>0.794872</td>
      <td>0.662338</td>
      <td>0.800000</td>
      <td>0.693548</td>
      <td>0.807018</td>
      <td>0.621622</td>
      <td>0.675000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.816754</td>
      <td>0.765625</td>
      <td>0.879518</td>
      <td>0.586466</td>
      <td>0.388350</td>
      <td>0.911243</td>
      <td>0.694118</td>
      <td>0.741573</td>
      <td>0.656085</td>
      <td>0.607143</td>
      <td>0.790123</td>
      <td>0.521212</td>
      <td>0.625850</td>
      <td>0.736000</td>
      <td>0.739726</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>Kernel: poly
Standard scaling
Accuracy: 0.849
Average precision: 0.890
Average recall: 0.857
Average fscore: 0.857
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.954023</td>
      <td>0.948276</td>
      <td>0.850575</td>
      <td>0.969231</td>
      <td>0.966102</td>
      <td>0.962963</td>
      <td>0.952381</td>
      <td>0.987654</td>
      <td>0.868421</td>
      <td>0.840000</td>
      <td>0.985075</td>
      <td>0.383648</td>
      <td>0.692308</td>
      <td>0.984848</td>
      <td>1.000000</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.882979</td>
      <td>0.932203</td>
      <td>0.973684</td>
      <td>0.715909</td>
      <td>0.780822</td>
      <td>0.917647</td>
      <td>0.645161</td>
      <td>0.851064</td>
      <td>0.846154</td>
      <td>0.818182</td>
      <td>0.825000</td>
      <td>0.983871</td>
      <td>0.947368</td>
      <td>0.878378</td>
      <td>0.850000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.917127</td>
      <td>0.940171</td>
      <td>0.907975</td>
      <td>0.823529</td>
      <td>0.863636</td>
      <td>0.939759</td>
      <td>0.769231</td>
      <td>0.914286</td>
      <td>0.857143</td>
      <td>0.828947</td>
      <td>0.897959</td>
      <td>0.552036</td>
      <td>0.800000</td>
      <td>0.928571</td>
      <td>0.918919</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.049
Average precision: 0.003
Average recall: 0.067
Average fscore: 0.006
</pre>
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<pre>c:\users\jerem_000\appdata\local\programs\python\python36\lib\site-packages\sklearn\metrics\classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.
  &#39;precision&#39;, &#39;predicted&#39;, average, warn_for)
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.048718</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.000000</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.092910</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.0</td>
      <td>59.0</td>
      <td>76.0</td>
      <td>88.0</td>
      <td>73.0</td>
      <td>85.0</td>
      <td>93.0</td>
      <td>94.0</td>
      <td>78.0</td>
      <td>77.0</td>
      <td>80.0</td>
      <td>62.0</td>
      <td>57.000000</td>
      <td>74.0</td>
      <td>80.0</td>
    </tr>
  </tbody>
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<pre>Kernel: sigmoid
Standard scaling
Accuracy: 0.540
Average precision: 0.629
Average recall: 0.541
Average fscore: 0.562
</pre>
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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.649485</td>
      <td>0.340206</td>
      <td>0.730769</td>
      <td>0.745455</td>
      <td>0.160000</td>
      <td>0.873239</td>
      <td>0.750000</td>
      <td>0.269663</td>
      <td>0.720000</td>
      <td>0.25000</td>
      <td>0.877551</td>
      <td>0.587302</td>
      <td>0.704545</td>
      <td>0.930233</td>
      <td>0.852459</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.670213</td>
      <td>0.559322</td>
      <td>0.750000</td>
      <td>0.465909</td>
      <td>0.383562</td>
      <td>0.729412</td>
      <td>0.354839</td>
      <td>0.510638</td>
      <td>0.692308</td>
      <td>0.12987</td>
      <td>0.537500</td>
      <td>0.596774</td>
      <td>0.543860</td>
      <td>0.540541</td>
      <td>0.650000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.659686</td>
      <td>0.423077</td>
      <td>0.740260</td>
      <td>0.573427</td>
      <td>0.225806</td>
      <td>0.794872</td>
      <td>0.481752</td>
      <td>0.352941</td>
      <td>0.705882</td>
      <td>0.17094</td>
      <td>0.666667</td>
      <td>0.592000</td>
      <td>0.613861</td>
      <td>0.683761</td>
      <td>0.737589</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.00000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
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<pre>
Minmax scaling
Accuracy: 0.593
Average precision: 0.659
Average recall: 0.603
Average fscore: 0.580
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.792208</td>
      <td>0.595238</td>
      <td>0.687500</td>
      <td>1.000000</td>
      <td>0.360465</td>
      <td>0.818182</td>
      <td>0.723077</td>
      <td>0.777778</td>
      <td>0.534091</td>
      <td>0.636364</td>
      <td>0.777778</td>
      <td>0.281046</td>
      <td>0.320896</td>
      <td>0.844828</td>
      <td>0.739130</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.648936</td>
      <td>0.847458</td>
      <td>0.868421</td>
      <td>0.125000</td>
      <td>0.424658</td>
      <td>0.847059</td>
      <td>0.505376</td>
      <td>0.670213</td>
      <td>0.602564</td>
      <td>0.181818</td>
      <td>0.787500</td>
      <td>0.693548</td>
      <td>0.754386</td>
      <td>0.662162</td>
      <td>0.425000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.713450</td>
      <td>0.699301</td>
      <td>0.767442</td>
      <td>0.222222</td>
      <td>0.389937</td>
      <td>0.832370</td>
      <td>0.594937</td>
      <td>0.720000</td>
      <td>0.566265</td>
      <td>0.282828</td>
      <td>0.782609</td>
      <td>0.400000</td>
      <td>0.450262</td>
      <td>0.742424</td>
      <td>0.539683</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<div class="cell border-box-sizing code_cell rendered">
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<div class="prompt input_prompt">In&nbsp;[148]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># svm</span>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="k">import</span> <span class="n">LinearSVC</span>

<span class="n">svm</span> <span class="o">=</span> <span class="n">LinearSVC</span><span class="p">()</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Without preprocessing&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">svm</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Standard scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_std</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_std</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">svm</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Minmax scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">svm</span><span class="p">)</span>
</pre></div>

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<pre>Without preprocessing
Accuracy: 0.664
Average precision: 0.751
Average recall: 0.668
Average fscore: 0.637
</pre>
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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.840000</td>
      <td>0.836066</td>
      <td>0.728155</td>
      <td>0.883721</td>
      <td>0.636364</td>
      <td>0.860759</td>
      <td>0.653846</td>
      <td>1.00000</td>
      <td>0.870968</td>
      <td>0.958333</td>
      <td>0.608696</td>
      <td>0.541176</td>
      <td>0.935484</td>
      <td>0.442029</td>
      <td>0.467456</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.893617</td>
      <td>0.864407</td>
      <td>0.986842</td>
      <td>0.431818</td>
      <td>0.479452</td>
      <td>0.800000</td>
      <td>0.913978</td>
      <td>0.06383</td>
      <td>0.346154</td>
      <td>0.298701</td>
      <td>0.875000</td>
      <td>0.741935</td>
      <td>0.508772</td>
      <td>0.824324</td>
      <td>0.987500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.865979</td>
      <td>0.850000</td>
      <td>0.837989</td>
      <td>0.580153</td>
      <td>0.546875</td>
      <td>0.829268</td>
      <td>0.762332</td>
      <td>0.12000</td>
      <td>0.495413</td>
      <td>0.455446</td>
      <td>0.717949</td>
      <td>0.625850</td>
      <td>0.659091</td>
      <td>0.575472</td>
      <td>0.634538</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.00000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Standard scaling
Accuracy: 0.863
Average precision: 0.860
Average recall: 0.866
Average fscore: 0.860
</pre>
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<style>
    .dataframe thead tr:only-child th {
        text-align: right;
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    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.915789</td>
      <td>0.918033</td>
      <td>0.883721</td>
      <td>0.859155</td>
      <td>0.766667</td>
      <td>0.963855</td>
      <td>0.810526</td>
      <td>0.987500</td>
      <td>0.790698</td>
      <td>0.750000</td>
      <td>0.901235</td>
      <td>0.779412</td>
      <td>0.746479</td>
      <td>0.878378</td>
      <td>0.951807</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.925532</td>
      <td>0.949153</td>
      <td>1.000000</td>
      <td>0.693182</td>
      <td>0.630137</td>
      <td>0.941176</td>
      <td>0.827957</td>
      <td>0.840426</td>
      <td>0.871795</td>
      <td>0.740260</td>
      <td>0.912500</td>
      <td>0.854839</td>
      <td>0.929825</td>
      <td>0.878378</td>
      <td>0.987500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.920635</td>
      <td>0.933333</td>
      <td>0.938272</td>
      <td>0.767296</td>
      <td>0.691729</td>
      <td>0.952381</td>
      <td>0.819149</td>
      <td>0.908046</td>
      <td>0.829268</td>
      <td>0.745098</td>
      <td>0.906832</td>
      <td>0.815385</td>
      <td>0.828125</td>
      <td>0.878378</td>
      <td>0.969325</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.835
Average precision: 0.831
Average recall: 0.836
Average fscore: 0.830
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.906250</td>
      <td>0.873016</td>
      <td>0.880952</td>
      <td>0.855072</td>
      <td>0.745455</td>
      <td>0.930233</td>
      <td>0.806452</td>
      <td>0.915663</td>
      <td>0.755814</td>
      <td>0.712329</td>
      <td>0.840909</td>
      <td>0.757576</td>
      <td>0.693333</td>
      <td>0.893939</td>
      <td>0.896552</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.925532</td>
      <td>0.932203</td>
      <td>0.973684</td>
      <td>0.670455</td>
      <td>0.561644</td>
      <td>0.941176</td>
      <td>0.806452</td>
      <td>0.808511</td>
      <td>0.833333</td>
      <td>0.675325</td>
      <td>0.925000</td>
      <td>0.806452</td>
      <td>0.912281</td>
      <td>0.797297</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.915789</td>
      <td>0.901639</td>
      <td>0.925000</td>
      <td>0.751592</td>
      <td>0.640625</td>
      <td>0.935673</td>
      <td>0.806452</td>
      <td>0.858757</td>
      <td>0.792683</td>
      <td>0.693333</td>
      <td>0.880952</td>
      <td>0.781250</td>
      <td>0.787879</td>
      <td>0.842857</td>
      <td>0.934132</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<div class="cell border-box-sizing code_cell rendered">
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<div class="prompt input_prompt">In&nbsp;[149]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># svm</span>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="k">import</span> <span class="n">LinearSVC</span>

<span class="c1"># trying various C</span>
<span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="p">[</span><span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">100</span><span class="p">]:</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;C parameter: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">c</span><span class="p">))</span>
    <span class="n">svm</span> <span class="o">=</span> <span class="n">LinearSVC</span><span class="p">(</span><span class="n">C</span><span class="o">=</span><span class="n">c</span><span class="p">)</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Standard scaling&quot;</span><span class="p">)</span>
    <span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_std</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_std</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">svm</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">()</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Minmax scaling&quot;</span><span class="p">)</span>
    <span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">svm</span><span class="p">)</span>
</pre></div>

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<pre>C parameter: 0.01
Standard scaling
Accuracy: 0.831
Average precision: 0.826
Average recall: 0.830
Average fscore: 0.822
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.887755</td>
      <td>0.811594</td>
      <td>0.894118</td>
      <td>0.893939</td>
      <td>0.782609</td>
      <td>0.891304</td>
      <td>0.793814</td>
      <td>0.937500</td>
      <td>0.779070</td>
      <td>0.724638</td>
      <td>0.822222</td>
      <td>0.771930</td>
      <td>0.712329</td>
      <td>0.857143</td>
      <td>0.836957</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.925532</td>
      <td>0.949153</td>
      <td>1.000000</td>
      <td>0.670455</td>
      <td>0.493151</td>
      <td>0.964706</td>
      <td>0.827957</td>
      <td>0.797872</td>
      <td>0.858974</td>
      <td>0.649351</td>
      <td>0.925000</td>
      <td>0.709677</td>
      <td>0.912281</td>
      <td>0.810811</td>
      <td>0.962500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.906250</td>
      <td>0.875000</td>
      <td>0.944099</td>
      <td>0.766234</td>
      <td>0.605042</td>
      <td>0.926554</td>
      <td>0.810526</td>
      <td>0.862069</td>
      <td>0.817073</td>
      <td>0.684932</td>
      <td>0.870588</td>
      <td>0.739496</td>
      <td>0.800000</td>
      <td>0.833333</td>
      <td>0.895349</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.725
Average precision: 0.726
Average recall: 0.720
Average fscore: 0.707
</pre>
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        vertical-align: top;
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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.808081</td>
      <td>0.500000</td>
      <td>0.797872</td>
      <td>0.886792</td>
      <td>0.647059</td>
      <td>0.824742</td>
      <td>0.720000</td>
      <td>0.685714</td>
      <td>0.666667</td>
      <td>0.566038</td>
      <td>0.777778</td>
      <td>0.708333</td>
      <td>0.656250</td>
      <td>0.886792</td>
      <td>0.763441</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.851064</td>
      <td>0.898305</td>
      <td>0.986842</td>
      <td>0.534091</td>
      <td>0.301370</td>
      <td>0.941176</td>
      <td>0.774194</td>
      <td>0.765957</td>
      <td>0.769231</td>
      <td>0.389610</td>
      <td>0.787500</td>
      <td>0.548387</td>
      <td>0.736842</td>
      <td>0.635135</td>
      <td>0.887500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.829016</td>
      <td>0.642424</td>
      <td>0.882353</td>
      <td>0.666667</td>
      <td>0.411215</td>
      <td>0.879121</td>
      <td>0.746114</td>
      <td>0.723618</td>
      <td>0.714286</td>
      <td>0.461538</td>
      <td>0.782609</td>
      <td>0.618182</td>
      <td>0.694215</td>
      <td>0.740157</td>
      <td>0.820809</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>C parameter: 0.1
Standard scaling
Accuracy: 0.858
Average precision: 0.856
Average recall: 0.860
Average fscore: 0.854
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.934783</td>
      <td>0.903226</td>
      <td>0.883721</td>
      <td>0.873239</td>
      <td>0.818182</td>
      <td>0.941176</td>
      <td>0.802083</td>
      <td>0.975000</td>
      <td>0.779070</td>
      <td>0.767123</td>
      <td>0.872093</td>
      <td>0.812500</td>
      <td>0.706667</td>
      <td>0.875000</td>
      <td>0.896552</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.914894</td>
      <td>0.949153</td>
      <td>1.000000</td>
      <td>0.704545</td>
      <td>0.616438</td>
      <td>0.941176</td>
      <td>0.827957</td>
      <td>0.829787</td>
      <td>0.858974</td>
      <td>0.727273</td>
      <td>0.937500</td>
      <td>0.838710</td>
      <td>0.929825</td>
      <td>0.851351</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.924731</td>
      <td>0.925620</td>
      <td>0.938272</td>
      <td>0.779874</td>
      <td>0.703125</td>
      <td>0.941176</td>
      <td>0.814815</td>
      <td>0.896552</td>
      <td>0.817073</td>
      <td>0.746667</td>
      <td>0.903614</td>
      <td>0.825397</td>
      <td>0.803030</td>
      <td>0.863014</td>
      <td>0.934132</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.802
Average precision: 0.795
Average recall: 0.801
Average fscore: 0.791
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.826923</td>
      <td>0.753425</td>
      <td>0.862069</td>
      <td>0.890625</td>
      <td>0.681818</td>
      <td>0.921348</td>
      <td>0.784946</td>
      <td>0.852273</td>
      <td>0.747126</td>
      <td>0.647059</td>
      <td>0.791209</td>
      <td>0.769231</td>
      <td>0.675325</td>
      <td>0.873016</td>
      <td>0.855556</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.914894</td>
      <td>0.932203</td>
      <td>0.986842</td>
      <td>0.647727</td>
      <td>0.410959</td>
      <td>0.964706</td>
      <td>0.784946</td>
      <td>0.797872</td>
      <td>0.833333</td>
      <td>0.571429</td>
      <td>0.900000</td>
      <td>0.645161</td>
      <td>0.912281</td>
      <td>0.743243</td>
      <td>0.962500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.868687</td>
      <td>0.833333</td>
      <td>0.920245</td>
      <td>0.750000</td>
      <td>0.512821</td>
      <td>0.942529</td>
      <td>0.784946</td>
      <td>0.824176</td>
      <td>0.787879</td>
      <td>0.606897</td>
      <td>0.842105</td>
      <td>0.701754</td>
      <td>0.776119</td>
      <td>0.802920</td>
      <td>0.905882</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
</div>
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<pre>C parameter: 1
Standard scaling
Accuracy: 0.864
Average precision: 0.861
Average recall: 0.866
Average fscore: 0.861
</pre>
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<style>
    .dataframe thead tr:only-child th {
        text-align: right;
    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.915789</td>
      <td>0.918033</td>
      <td>0.883721</td>
      <td>0.871429</td>
      <td>0.766667</td>
      <td>0.963855</td>
      <td>0.812500</td>
      <td>0.987500</td>
      <td>0.790698</td>
      <td>0.76000</td>
      <td>0.901235</td>
      <td>0.779412</td>
      <td>0.746479</td>
      <td>0.866667</td>
      <td>0.951807</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.925532</td>
      <td>0.949153</td>
      <td>1.000000</td>
      <td>0.693182</td>
      <td>0.630137</td>
      <td>0.941176</td>
      <td>0.838710</td>
      <td>0.840426</td>
      <td>0.871795</td>
      <td>0.74026</td>
      <td>0.912500</td>
      <td>0.854839</td>
      <td>0.929825</td>
      <td>0.878378</td>
      <td>0.987500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.920635</td>
      <td>0.933333</td>
      <td>0.938272</td>
      <td>0.772152</td>
      <td>0.691729</td>
      <td>0.952381</td>
      <td>0.825397</td>
      <td>0.908046</td>
      <td>0.829268</td>
      <td>0.75000</td>
      <td>0.906832</td>
      <td>0.815385</td>
      <td>0.828125</td>
      <td>0.872483</td>
      <td>0.969325</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.00000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.835
Average precision: 0.831
Average recall: 0.836
Average fscore: 0.830
</pre>
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    .dataframe thead tr:only-child th {
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    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.906250</td>
      <td>0.873016</td>
      <td>0.880952</td>
      <td>0.855072</td>
      <td>0.745455</td>
      <td>0.930233</td>
      <td>0.806452</td>
      <td>0.915663</td>
      <td>0.755814</td>
      <td>0.712329</td>
      <td>0.840909</td>
      <td>0.757576</td>
      <td>0.693333</td>
      <td>0.893939</td>
      <td>0.896552</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.925532</td>
      <td>0.932203</td>
      <td>0.973684</td>
      <td>0.670455</td>
      <td>0.561644</td>
      <td>0.941176</td>
      <td>0.806452</td>
      <td>0.808511</td>
      <td>0.833333</td>
      <td>0.675325</td>
      <td>0.925000</td>
      <td>0.806452</td>
      <td>0.912281</td>
      <td>0.797297</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.915789</td>
      <td>0.901639</td>
      <td>0.925000</td>
      <td>0.751592</td>
      <td>0.640625</td>
      <td>0.935673</td>
      <td>0.806452</td>
      <td>0.858757</td>
      <td>0.792683</td>
      <td>0.693333</td>
      <td>0.880952</td>
      <td>0.781250</td>
      <td>0.787879</td>
      <td>0.842857</td>
      <td>0.934132</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>C parameter: 10
Standard scaling
Accuracy: 0.857
Average precision: 0.856
Average recall: 0.860
Average fscore: 0.855
</pre>
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    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.904255</td>
      <td>0.901639</td>
      <td>0.872093</td>
      <td>0.871429</td>
      <td>0.649351</td>
      <td>0.963415</td>
      <td>0.822917</td>
      <td>0.974684</td>
      <td>0.777778</td>
      <td>0.746269</td>
      <td>0.947368</td>
      <td>0.808824</td>
      <td>0.750000</td>
      <td>0.898551</td>
      <td>0.951807</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.904255</td>
      <td>0.932203</td>
      <td>0.986842</td>
      <td>0.693182</td>
      <td>0.684932</td>
      <td>0.929412</td>
      <td>0.849462</td>
      <td>0.819149</td>
      <td>0.897436</td>
      <td>0.649351</td>
      <td>0.900000</td>
      <td>0.887097</td>
      <td>0.947368</td>
      <td>0.837838</td>
      <td>0.987500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.904255</td>
      <td>0.916667</td>
      <td>0.925926</td>
      <td>0.772152</td>
      <td>0.666667</td>
      <td>0.946108</td>
      <td>0.835979</td>
      <td>0.890173</td>
      <td>0.833333</td>
      <td>0.694444</td>
      <td>0.923077</td>
      <td>0.846154</td>
      <td>0.837209</td>
      <td>0.867133</td>
      <td>0.969325</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.868
Average precision: 0.865
Average recall: 0.870
Average fscore: 0.865
</pre>
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    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.955556</td>
      <td>0.919355</td>
      <td>0.894118</td>
      <td>0.833333</td>
      <td>0.810345</td>
      <td>0.964286</td>
      <td>0.814433</td>
      <td>0.963415</td>
      <td>0.772727</td>
      <td>0.794521</td>
      <td>0.913580</td>
      <td>0.782609</td>
      <td>0.729730</td>
      <td>0.875000</td>
      <td>0.951807</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.914894</td>
      <td>0.966102</td>
      <td>1.000000</td>
      <td>0.681818</td>
      <td>0.643836</td>
      <td>0.952941</td>
      <td>0.849462</td>
      <td>0.840426</td>
      <td>0.871795</td>
      <td>0.753247</td>
      <td>0.925000</td>
      <td>0.870968</td>
      <td>0.947368</td>
      <td>0.851351</td>
      <td>0.987500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.934783</td>
      <td>0.942149</td>
      <td>0.944099</td>
      <td>0.750000</td>
      <td>0.717557</td>
      <td>0.958580</td>
      <td>0.831579</td>
      <td>0.897727</td>
      <td>0.819277</td>
      <td>0.773333</td>
      <td>0.919255</td>
      <td>0.824427</td>
      <td>0.824427</td>
      <td>0.863014</td>
      <td>0.969325</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>C parameter: 100
Standard scaling
Accuracy: 0.824
Average precision: 0.822
Average recall: 0.823
Average fscore: 0.819
</pre>
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        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.854167</td>
      <td>0.901639</td>
      <td>0.870588</td>
      <td>0.848485</td>
      <td>0.693548</td>
      <td>0.961538</td>
      <td>0.754717</td>
      <td>0.948052</td>
      <td>0.776471</td>
      <td>0.776316</td>
      <td>0.921053</td>
      <td>0.641026</td>
      <td>0.655738</td>
      <td>0.820513</td>
      <td>0.905882</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.872340</td>
      <td>0.932203</td>
      <td>0.973684</td>
      <td>0.636364</td>
      <td>0.589041</td>
      <td>0.882353</td>
      <td>0.860215</td>
      <td>0.776596</td>
      <td>0.846154</td>
      <td>0.766234</td>
      <td>0.875000</td>
      <td>0.806452</td>
      <td>0.701754</td>
      <td>0.864865</td>
      <td>0.962500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.863158</td>
      <td>0.916667</td>
      <td>0.919255</td>
      <td>0.727273</td>
      <td>0.637037</td>
      <td>0.920245</td>
      <td>0.804020</td>
      <td>0.853801</td>
      <td>0.809816</td>
      <td>0.771242</td>
      <td>0.897436</td>
      <td>0.714286</td>
      <td>0.677966</td>
      <td>0.842105</td>
      <td>0.933333</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.844
Average precision: 0.854
Average recall: 0.848
Average fscore: 0.840
</pre>
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<style>
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    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.916667</td>
      <td>0.903226</td>
      <td>0.882353</td>
      <td>0.897959</td>
      <td>0.811321</td>
      <td>0.910112</td>
      <td>0.815217</td>
      <td>0.939759</td>
      <td>0.587302</td>
      <td>0.900000</td>
      <td>0.923077</td>
      <td>0.743243</td>
      <td>0.723684</td>
      <td>0.893939</td>
      <td>0.962963</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.936170</td>
      <td>0.949153</td>
      <td>0.986842</td>
      <td>0.500000</td>
      <td>0.589041</td>
      <td>0.952941</td>
      <td>0.806452</td>
      <td>0.829787</td>
      <td>0.948718</td>
      <td>0.701299</td>
      <td>0.900000</td>
      <td>0.887097</td>
      <td>0.964912</td>
      <td>0.797297</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.926316</td>
      <td>0.925620</td>
      <td>0.931677</td>
      <td>0.642336</td>
      <td>0.682540</td>
      <td>0.931034</td>
      <td>0.810811</td>
      <td>0.881356</td>
      <td>0.725490</td>
      <td>0.788321</td>
      <td>0.911392</td>
      <td>0.808824</td>
      <td>0.827068</td>
      <td>0.842857</td>
      <td>0.968944</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># svm</span>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="k">import</span> <span class="n">LinearSVC</span>

<span class="c1"># trying various C</span>
<span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="p">[</span><span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">100</span><span class="p">]:</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;C parameter: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">c</span><span class="p">))</span>
    <span class="n">svm</span> <span class="o">=</span> <span class="n">LinearSVC</span><span class="p">(</span><span class="n">C</span><span class="o">=</span><span class="n">c</span><span class="p">,</span> <span class="n">dual</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Standard scaling&quot;</span><span class="p">)</span>
    <span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_std</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_std</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">svm</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">()</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Minmax scaling&quot;</span><span class="p">)</span>
    <span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">svm</span><span class="p">)</span>
</pre></div>

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<pre>C parameter: 0.01
Standard scaling
Accuracy: 0.831
Average precision: 0.826
Average recall: 0.830
Average fscore: 0.822
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.887755</td>
      <td>0.811594</td>
      <td>0.894118</td>
      <td>0.893939</td>
      <td>0.782609</td>
      <td>0.891304</td>
      <td>0.793814</td>
      <td>0.937500</td>
      <td>0.779070</td>
      <td>0.724638</td>
      <td>0.822222</td>
      <td>0.771930</td>
      <td>0.712329</td>
      <td>0.857143</td>
      <td>0.836957</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.925532</td>
      <td>0.949153</td>
      <td>1.000000</td>
      <td>0.670455</td>
      <td>0.493151</td>
      <td>0.964706</td>
      <td>0.827957</td>
      <td>0.797872</td>
      <td>0.858974</td>
      <td>0.649351</td>
      <td>0.925000</td>
      <td>0.709677</td>
      <td>0.912281</td>
      <td>0.810811</td>
      <td>0.962500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.906250</td>
      <td>0.875000</td>
      <td>0.944099</td>
      <td>0.766234</td>
      <td>0.605042</td>
      <td>0.926554</td>
      <td>0.810526</td>
      <td>0.862069</td>
      <td>0.817073</td>
      <td>0.684932</td>
      <td>0.870588</td>
      <td>0.739496</td>
      <td>0.800000</td>
      <td>0.833333</td>
      <td>0.895349</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.725
Average precision: 0.726
Average recall: 0.720
Average fscore: 0.707
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.808081</td>
      <td>0.500000</td>
      <td>0.797872</td>
      <td>0.886792</td>
      <td>0.647059</td>
      <td>0.824742</td>
      <td>0.720000</td>
      <td>0.685714</td>
      <td>0.666667</td>
      <td>0.566038</td>
      <td>0.777778</td>
      <td>0.708333</td>
      <td>0.656250</td>
      <td>0.886792</td>
      <td>0.763441</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.851064</td>
      <td>0.898305</td>
      <td>0.986842</td>
      <td>0.534091</td>
      <td>0.301370</td>
      <td>0.941176</td>
      <td>0.774194</td>
      <td>0.765957</td>
      <td>0.769231</td>
      <td>0.389610</td>
      <td>0.787500</td>
      <td>0.548387</td>
      <td>0.736842</td>
      <td>0.635135</td>
      <td>0.887500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.829016</td>
      <td>0.642424</td>
      <td>0.882353</td>
      <td>0.666667</td>
      <td>0.411215</td>
      <td>0.879121</td>
      <td>0.746114</td>
      <td>0.723618</td>
      <td>0.714286</td>
      <td>0.461538</td>
      <td>0.782609</td>
      <td>0.618182</td>
      <td>0.694215</td>
      <td>0.740157</td>
      <td>0.820809</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>C parameter: 0.1
Standard scaling
Accuracy: 0.858
Average precision: 0.856
Average recall: 0.860
Average fscore: 0.854
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.934783</td>
      <td>0.903226</td>
      <td>0.883721</td>
      <td>0.873239</td>
      <td>0.818182</td>
      <td>0.941176</td>
      <td>0.802083</td>
      <td>0.975000</td>
      <td>0.779070</td>
      <td>0.767123</td>
      <td>0.872093</td>
      <td>0.812500</td>
      <td>0.706667</td>
      <td>0.875000</td>
      <td>0.896552</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.914894</td>
      <td>0.949153</td>
      <td>1.000000</td>
      <td>0.704545</td>
      <td>0.616438</td>
      <td>0.941176</td>
      <td>0.827957</td>
      <td>0.829787</td>
      <td>0.858974</td>
      <td>0.727273</td>
      <td>0.937500</td>
      <td>0.838710</td>
      <td>0.929825</td>
      <td>0.851351</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.924731</td>
      <td>0.925620</td>
      <td>0.938272</td>
      <td>0.779874</td>
      <td>0.703125</td>
      <td>0.941176</td>
      <td>0.814815</td>
      <td>0.896552</td>
      <td>0.817073</td>
      <td>0.746667</td>
      <td>0.903614</td>
      <td>0.825397</td>
      <td>0.803030</td>
      <td>0.863014</td>
      <td>0.934132</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.802
Average precision: 0.795
Average recall: 0.801
Average fscore: 0.791
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.826923</td>
      <td>0.753425</td>
      <td>0.862069</td>
      <td>0.890625</td>
      <td>0.681818</td>
      <td>0.921348</td>
      <td>0.784946</td>
      <td>0.852273</td>
      <td>0.747126</td>
      <td>0.647059</td>
      <td>0.791209</td>
      <td>0.769231</td>
      <td>0.675325</td>
      <td>0.873016</td>
      <td>0.855556</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.914894</td>
      <td>0.932203</td>
      <td>0.986842</td>
      <td>0.647727</td>
      <td>0.410959</td>
      <td>0.964706</td>
      <td>0.784946</td>
      <td>0.797872</td>
      <td>0.833333</td>
      <td>0.571429</td>
      <td>0.900000</td>
      <td>0.645161</td>
      <td>0.912281</td>
      <td>0.743243</td>
      <td>0.962500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.868687</td>
      <td>0.833333</td>
      <td>0.920245</td>
      <td>0.750000</td>
      <td>0.512821</td>
      <td>0.942529</td>
      <td>0.784946</td>
      <td>0.824176</td>
      <td>0.787879</td>
      <td>0.606897</td>
      <td>0.842105</td>
      <td>0.701754</td>
      <td>0.776119</td>
      <td>0.802920</td>
      <td>0.905882</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>C parameter: 1
Standard scaling
Accuracy: 0.863
Average precision: 0.860
Average recall: 0.866
Average fscore: 0.860
</pre>
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        vertical-align: top;
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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.915789</td>
      <td>0.918033</td>
      <td>0.883721</td>
      <td>0.857143</td>
      <td>0.770492</td>
      <td>0.963855</td>
      <td>0.810526</td>
      <td>0.987500</td>
      <td>0.790698</td>
      <td>0.76000</td>
      <td>0.901235</td>
      <td>0.779412</td>
      <td>0.746479</td>
      <td>0.866667</td>
      <td>0.951807</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.925532</td>
      <td>0.949153</td>
      <td>1.000000</td>
      <td>0.681818</td>
      <td>0.643836</td>
      <td>0.941176</td>
      <td>0.827957</td>
      <td>0.840426</td>
      <td>0.871795</td>
      <td>0.74026</td>
      <td>0.912500</td>
      <td>0.854839</td>
      <td>0.929825</td>
      <td>0.878378</td>
      <td>0.987500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.920635</td>
      <td>0.933333</td>
      <td>0.938272</td>
      <td>0.759494</td>
      <td>0.701493</td>
      <td>0.952381</td>
      <td>0.819149</td>
      <td>0.908046</td>
      <td>0.829268</td>
      <td>0.75000</td>
      <td>0.906832</td>
      <td>0.815385</td>
      <td>0.828125</td>
      <td>0.872483</td>
      <td>0.969325</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.00000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
</div>
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<pre>
Minmax scaling
Accuracy: 0.835
Average precision: 0.831
Average recall: 0.836
Average fscore: 0.830
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.906250</td>
      <td>0.873016</td>
      <td>0.880952</td>
      <td>0.855072</td>
      <td>0.745455</td>
      <td>0.930233</td>
      <td>0.806452</td>
      <td>0.915663</td>
      <td>0.755814</td>
      <td>0.712329</td>
      <td>0.840909</td>
      <td>0.757576</td>
      <td>0.693333</td>
      <td>0.893939</td>
      <td>0.896552</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.925532</td>
      <td>0.932203</td>
      <td>0.973684</td>
      <td>0.670455</td>
      <td>0.561644</td>
      <td>0.941176</td>
      <td>0.806452</td>
      <td>0.808511</td>
      <td>0.833333</td>
      <td>0.675325</td>
      <td>0.925000</td>
      <td>0.806452</td>
      <td>0.912281</td>
      <td>0.797297</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.915789</td>
      <td>0.901639</td>
      <td>0.925000</td>
      <td>0.751592</td>
      <td>0.640625</td>
      <td>0.935673</td>
      <td>0.806452</td>
      <td>0.858757</td>
      <td>0.792683</td>
      <td>0.693333</td>
      <td>0.880952</td>
      <td>0.781250</td>
      <td>0.787879</td>
      <td>0.842857</td>
      <td>0.934132</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>C parameter: 10
Standard scaling
Accuracy: 0.860
Average precision: 0.858
Average recall: 0.863
Average fscore: 0.858
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.914894</td>
      <td>0.933333</td>
      <td>0.873563</td>
      <td>0.845070</td>
      <td>0.742424</td>
      <td>0.962963</td>
      <td>0.800000</td>
      <td>0.974684</td>
      <td>0.800000</td>
      <td>0.776316</td>
      <td>0.935065</td>
      <td>0.782609</td>
      <td>0.736111</td>
      <td>0.851351</td>
      <td>0.940476</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.914894</td>
      <td>0.949153</td>
      <td>1.000000</td>
      <td>0.681818</td>
      <td>0.671233</td>
      <td>0.917647</td>
      <td>0.817204</td>
      <td>0.819149</td>
      <td>0.871795</td>
      <td>0.766234</td>
      <td>0.900000</td>
      <td>0.870968</td>
      <td>0.929825</td>
      <td>0.851351</td>
      <td>0.987500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.914894</td>
      <td>0.941176</td>
      <td>0.932515</td>
      <td>0.754717</td>
      <td>0.705036</td>
      <td>0.939759</td>
      <td>0.808511</td>
      <td>0.890173</td>
      <td>0.834356</td>
      <td>0.771242</td>
      <td>0.917197</td>
      <td>0.824427</td>
      <td>0.821705</td>
      <td>0.851351</td>
      <td>0.963415</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.863
Average precision: 0.860
Average recall: 0.866
Average fscore: 0.860
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.945055</td>
      <td>0.919355</td>
      <td>0.894118</td>
      <td>0.833333</td>
      <td>0.793103</td>
      <td>0.964286</td>
      <td>0.808511</td>
      <td>0.963415</td>
      <td>0.772727</td>
      <td>0.794521</td>
      <td>0.891566</td>
      <td>0.782609</td>
      <td>0.716216</td>
      <td>0.875000</td>
      <td>0.951807</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.914894</td>
      <td>0.966102</td>
      <td>1.000000</td>
      <td>0.681818</td>
      <td>0.630137</td>
      <td>0.952941</td>
      <td>0.817204</td>
      <td>0.840426</td>
      <td>0.871795</td>
      <td>0.753247</td>
      <td>0.925000</td>
      <td>0.870968</td>
      <td>0.929825</td>
      <td>0.851351</td>
      <td>0.987500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.929730</td>
      <td>0.942149</td>
      <td>0.944099</td>
      <td>0.750000</td>
      <td>0.702290</td>
      <td>0.958580</td>
      <td>0.812834</td>
      <td>0.897727</td>
      <td>0.819277</td>
      <td>0.773333</td>
      <td>0.907975</td>
      <td>0.824427</td>
      <td>0.809160</td>
      <td>0.863014</td>
      <td>0.969325</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>C parameter: 100
Standard scaling
Accuracy: 0.856
Average precision: 0.854
Average recall: 0.858
Average fscore: 0.854
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.915789</td>
      <td>0.932203</td>
      <td>0.873563</td>
      <td>0.859155</td>
      <td>0.738462</td>
      <td>0.939759</td>
      <td>0.800000</td>
      <td>0.950617</td>
      <td>0.785714</td>
      <td>0.766234</td>
      <td>0.935065</td>
      <td>0.782609</td>
      <td>0.736111</td>
      <td>0.873239</td>
      <td>0.916667</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.925532</td>
      <td>0.932203</td>
      <td>1.000000</td>
      <td>0.693182</td>
      <td>0.657534</td>
      <td>0.917647</td>
      <td>0.817204</td>
      <td>0.819149</td>
      <td>0.846154</td>
      <td>0.766234</td>
      <td>0.900000</td>
      <td>0.870968</td>
      <td>0.929825</td>
      <td>0.837838</td>
      <td>0.962500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.920635</td>
      <td>0.932203</td>
      <td>0.932515</td>
      <td>0.767296</td>
      <td>0.695652</td>
      <td>0.928571</td>
      <td>0.808511</td>
      <td>0.880000</td>
      <td>0.814815</td>
      <td>0.766234</td>
      <td>0.917197</td>
      <td>0.824427</td>
      <td>0.821705</td>
      <td>0.855172</td>
      <td>0.939024</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.866
Average precision: 0.863
Average recall: 0.869
Average fscore: 0.863
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.925532</td>
      <td>0.918033</td>
      <td>0.894118</td>
      <td>0.849315</td>
      <td>0.750000</td>
      <td>0.963855</td>
      <td>0.817204</td>
      <td>0.987500</td>
      <td>0.800000</td>
      <td>0.763158</td>
      <td>0.935065</td>
      <td>0.774648</td>
      <td>0.739726</td>
      <td>0.863014</td>
      <td>0.963415</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.925532</td>
      <td>0.949153</td>
      <td>1.000000</td>
      <td>0.704545</td>
      <td>0.657534</td>
      <td>0.941176</td>
      <td>0.817204</td>
      <td>0.840426</td>
      <td>0.871795</td>
      <td>0.753247</td>
      <td>0.900000</td>
      <td>0.887097</td>
      <td>0.947368</td>
      <td>0.851351</td>
      <td>0.987500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.925532</td>
      <td>0.933333</td>
      <td>0.944099</td>
      <td>0.770186</td>
      <td>0.700730</td>
      <td>0.952381</td>
      <td>0.817204</td>
      <td>0.908046</td>
      <td>0.834356</td>
      <td>0.758170</td>
      <td>0.917197</td>
      <td>0.827068</td>
      <td>0.830769</td>
      <td>0.857143</td>
      <td>0.975309</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<div class="cell border-box-sizing code_cell rendered">
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<div class="prompt input_prompt">In&nbsp;[155]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># neural network</span>
<span class="kn">from</span> <span class="nn">sklearn.neural_network</span> <span class="k">import</span> <span class="n">MLPClassifier</span>

<span class="c1"># def is relu activation, 1 hidden layer with 100 neurons</span>
<span class="n">nn</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">()</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Standard scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_std</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_std</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Minmax scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
</pre></div>

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<pre>Standard scaling
Accuracy: 0.949
Average precision: 0.949
Average recall: 0.951
Average fscore: 0.949
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.978723</td>
      <td>0.948276</td>
      <td>0.938272</td>
      <td>0.975000</td>
      <td>0.932432</td>
      <td>0.952941</td>
      <td>0.907216</td>
      <td>0.987952</td>
      <td>0.948718</td>
      <td>0.925000</td>
      <td>0.961538</td>
      <td>0.884058</td>
      <td>0.964912</td>
      <td>0.972973</td>
      <td>0.951220</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.978723</td>
      <td>0.932203</td>
      <td>1.000000</td>
      <td>0.886364</td>
      <td>0.945205</td>
      <td>0.952941</td>
      <td>0.946237</td>
      <td>0.872340</td>
      <td>0.948718</td>
      <td>0.961039</td>
      <td>0.937500</td>
      <td>0.983871</td>
      <td>0.964912</td>
      <td>0.972973</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.978723</td>
      <td>0.940171</td>
      <td>0.968153</td>
      <td>0.928571</td>
      <td>0.938776</td>
      <td>0.952941</td>
      <td>0.926316</td>
      <td>0.926554</td>
      <td>0.948718</td>
      <td>0.942675</td>
      <td>0.949367</td>
      <td>0.931298</td>
      <td>0.964912</td>
      <td>0.972973</td>
      <td>0.962963</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.879
Average precision: 0.878
Average recall: 0.881
Average fscore: 0.879
</pre>
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<pre>c:\users\jerem_000\appdata\local\programs\python\python36\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.957447</td>
      <td>0.904762</td>
      <td>0.937500</td>
      <td>0.817073</td>
      <td>0.823529</td>
      <td>0.976190</td>
      <td>0.815217</td>
      <td>0.921348</td>
      <td>0.795181</td>
      <td>0.772152</td>
      <td>0.9</td>
      <td>0.830769</td>
      <td>0.877193</td>
      <td>0.914286</td>
      <td>0.928571</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.957447</td>
      <td>0.966102</td>
      <td>0.986842</td>
      <td>0.761364</td>
      <td>0.767123</td>
      <td>0.964706</td>
      <td>0.806452</td>
      <td>0.872340</td>
      <td>0.846154</td>
      <td>0.792208</td>
      <td>0.9</td>
      <td>0.870968</td>
      <td>0.877193</td>
      <td>0.864865</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.957447</td>
      <td>0.934426</td>
      <td>0.961538</td>
      <td>0.788235</td>
      <td>0.794326</td>
      <td>0.970414</td>
      <td>0.810811</td>
      <td>0.896175</td>
      <td>0.819876</td>
      <td>0.782051</td>
      <td>0.9</td>
      <td>0.850394</td>
      <td>0.877193</td>
      <td>0.888889</td>
      <td>0.951220</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.0</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
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<div class="prompt input_prompt">In&nbsp;[156]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># try about 2 * hidden layers w/ 50 neurons</span>
<span class="n">nn</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span><span class="mi">50</span><span class="p">))</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Standard scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_std</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_std</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Minmax scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
</pre></div>

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<pre>Standard scaling
Accuracy: 0.947
Average precision: 0.947
Average recall: 0.949
Average fscore: 0.948
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.989247</td>
      <td>0.949153</td>
      <td>0.961538</td>
      <td>0.929412</td>
      <td>0.943662</td>
      <td>0.965116</td>
      <td>0.934066</td>
      <td>0.965517</td>
      <td>0.913580</td>
      <td>0.912500</td>
      <td>0.961538</td>
      <td>0.869565</td>
      <td>0.982759</td>
      <td>0.972222</td>
      <td>0.951220</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.978723</td>
      <td>0.949153</td>
      <td>0.986842</td>
      <td>0.897727</td>
      <td>0.917808</td>
      <td>0.976471</td>
      <td>0.913978</td>
      <td>0.893617</td>
      <td>0.948718</td>
      <td>0.948052</td>
      <td>0.937500</td>
      <td>0.967742</td>
      <td>1.000000</td>
      <td>0.945946</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.983957</td>
      <td>0.949153</td>
      <td>0.974026</td>
      <td>0.913295</td>
      <td>0.930556</td>
      <td>0.970760</td>
      <td>0.923913</td>
      <td>0.928177</td>
      <td>0.930818</td>
      <td>0.929936</td>
      <td>0.949367</td>
      <td>0.916031</td>
      <td>0.991304</td>
      <td>0.958904</td>
      <td>0.962963</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
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<pre>
Minmax scaling
Accuracy: 0.885
Average precision: 0.887
Average recall: 0.884
Average fscore: 0.884
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.937500</td>
      <td>0.890625</td>
      <td>0.914634</td>
      <td>0.793478</td>
      <td>0.929825</td>
      <td>0.987654</td>
      <td>0.847826</td>
      <td>0.932584</td>
      <td>0.827160</td>
      <td>0.765432</td>
      <td>0.842697</td>
      <td>0.820896</td>
      <td>0.88000</td>
      <td>0.970588</td>
      <td>0.962963</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.957447</td>
      <td>0.966102</td>
      <td>0.986842</td>
      <td>0.829545</td>
      <td>0.726027</td>
      <td>0.941176</td>
      <td>0.838710</td>
      <td>0.882979</td>
      <td>0.858974</td>
      <td>0.805195</td>
      <td>0.937500</td>
      <td>0.887097</td>
      <td>0.77193</td>
      <td>0.891892</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.947368</td>
      <td>0.926829</td>
      <td>0.949367</td>
      <td>0.811111</td>
      <td>0.815385</td>
      <td>0.963855</td>
      <td>0.843243</td>
      <td>0.907104</td>
      <td>0.842767</td>
      <td>0.784810</td>
      <td>0.887574</td>
      <td>0.852713</td>
      <td>0.82243</td>
      <td>0.929577</td>
      <td>0.968944</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.00000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<div class="cell border-box-sizing code_cell rendered">
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<div class="prompt input_prompt">In&nbsp;[159]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">alpha</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">11</span><span class="p">):</span>
    <span class="c1"># using the formula samples / (alpha * (input + output)), 1 hidden layer</span>
    <span class="n">nn</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">3510</span> <span class="o">//</span> <span class="p">(</span><span class="mi">65</span><span class="o">*</span><span class="n">alpha</span><span class="p">),))</span>
    
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Hidden layers: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="mi">3510</span> <span class="o">//</span> <span class="p">(</span><span class="mi">65</span><span class="o">*</span><span class="n">alpha</span><span class="p">)))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Standard scaling&quot;</span><span class="p">)</span>
    <span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_std</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_std</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">()</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Minmax scaling&quot;</span><span class="p">)</span>
    <span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
</pre></div>

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<pre>Hidden layers: 27
Standard scaling
Accuracy: 0.915
Average precision: 0.914
Average recall: 0.919
Average fscore: 0.915
</pre>
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<pre>c:\users\jerem_000\appdata\local\programs\python\python36\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
</pre>
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    .dataframe thead tr:only-child th {
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    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.965909</td>
      <td>0.949153</td>
      <td>0.892857</td>
      <td>0.878049</td>
      <td>0.90411</td>
      <td>0.976190</td>
      <td>0.869565</td>
      <td>0.951220</td>
      <td>0.935897</td>
      <td>0.884615</td>
      <td>0.974026</td>
      <td>0.824324</td>
      <td>0.820896</td>
      <td>0.929577</td>
      <td>0.950617</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.904255</td>
      <td>0.949153</td>
      <td>0.986842</td>
      <td>0.818182</td>
      <td>0.90411</td>
      <td>0.964706</td>
      <td>0.860215</td>
      <td>0.829787</td>
      <td>0.935897</td>
      <td>0.896104</td>
      <td>0.937500</td>
      <td>0.983871</td>
      <td>0.964912</td>
      <td>0.891892</td>
      <td>0.962500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.934066</td>
      <td>0.949153</td>
      <td>0.937500</td>
      <td>0.847059</td>
      <td>0.90411</td>
      <td>0.970414</td>
      <td>0.864865</td>
      <td>0.886364</td>
      <td>0.935897</td>
      <td>0.890323</td>
      <td>0.955414</td>
      <td>0.897059</td>
      <td>0.887097</td>
      <td>0.910345</td>
      <td>0.956522</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.00000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.809
Average precision: 0.801
Average recall: 0.808
Average fscore: 0.802
</pre>
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        text-align: left;
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        vertical-align: top;
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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.932584</td>
      <td>0.776119</td>
      <td>0.831461</td>
      <td>0.774648</td>
      <td>0.625000</td>
      <td>0.951807</td>
      <td>0.781250</td>
      <td>0.885057</td>
      <td>0.766667</td>
      <td>0.623377</td>
      <td>0.855422</td>
      <td>0.716418</td>
      <td>0.754098</td>
      <td>0.852941</td>
      <td>0.895349</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.882979</td>
      <td>0.881356</td>
      <td>0.973684</td>
      <td>0.625000</td>
      <td>0.479452</td>
      <td>0.929412</td>
      <td>0.806452</td>
      <td>0.819149</td>
      <td>0.884615</td>
      <td>0.623377</td>
      <td>0.887500</td>
      <td>0.774194</td>
      <td>0.807018</td>
      <td>0.783784</td>
      <td>0.962500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.907104</td>
      <td>0.825397</td>
      <td>0.896970</td>
      <td>0.691824</td>
      <td>0.542636</td>
      <td>0.940476</td>
      <td>0.793651</td>
      <td>0.850829</td>
      <td>0.821429</td>
      <td>0.623377</td>
      <td>0.871166</td>
      <td>0.744186</td>
      <td>0.779661</td>
      <td>0.816901</td>
      <td>0.927711</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>Hidden layers: 18
Standard scaling
Accuracy: 0.891
Average precision: 0.888
Average recall: 0.894
Average fscore: 0.889
</pre>
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        vertical-align: top;
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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.977528</td>
      <td>0.875000</td>
      <td>0.891566</td>
      <td>0.876712</td>
      <td>0.808219</td>
      <td>0.975309</td>
      <td>0.852632</td>
      <td>0.952381</td>
      <td>0.843373</td>
      <td>0.853333</td>
      <td>0.948052</td>
      <td>0.805556</td>
      <td>0.794118</td>
      <td>0.891892</td>
      <td>0.974684</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.925532</td>
      <td>0.949153</td>
      <td>0.973684</td>
      <td>0.727273</td>
      <td>0.808219</td>
      <td>0.929412</td>
      <td>0.870968</td>
      <td>0.851064</td>
      <td>0.897436</td>
      <td>0.831169</td>
      <td>0.912500</td>
      <td>0.935484</td>
      <td>0.947368</td>
      <td>0.891892</td>
      <td>0.962500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.950820</td>
      <td>0.910569</td>
      <td>0.930818</td>
      <td>0.795031</td>
      <td>0.808219</td>
      <td>0.951807</td>
      <td>0.861702</td>
      <td>0.898876</td>
      <td>0.869565</td>
      <td>0.842105</td>
      <td>0.929936</td>
      <td>0.865672</td>
      <td>0.864000</td>
      <td>0.891892</td>
      <td>0.968553</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.717
Average precision: 0.709
Average recall: 0.718
Average fscore: 0.707
</pre>
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    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.839080</td>
      <td>0.787879</td>
      <td>0.734043</td>
      <td>0.679245</td>
      <td>0.473684</td>
      <td>0.902439</td>
      <td>0.752809</td>
      <td>0.802326</td>
      <td>0.641509</td>
      <td>0.540541</td>
      <td>0.744186</td>
      <td>0.612903</td>
      <td>0.565789</td>
      <td>0.705882</td>
      <td>0.845238</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.776596</td>
      <td>0.881356</td>
      <td>0.907895</td>
      <td>0.409091</td>
      <td>0.369863</td>
      <td>0.870588</td>
      <td>0.720430</td>
      <td>0.734043</td>
      <td>0.871795</td>
      <td>0.519481</td>
      <td>0.800000</td>
      <td>0.612903</td>
      <td>0.754386</td>
      <td>0.648649</td>
      <td>0.887500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.806630</td>
      <td>0.832000</td>
      <td>0.811765</td>
      <td>0.510638</td>
      <td>0.415385</td>
      <td>0.886228</td>
      <td>0.736264</td>
      <td>0.766667</td>
      <td>0.739130</td>
      <td>0.529801</td>
      <td>0.771084</td>
      <td>0.612903</td>
      <td>0.646617</td>
      <td>0.676056</td>
      <td>0.865854</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>Hidden layers: 13
Standard scaling
Accuracy: 0.878
Average precision: 0.875
Average recall: 0.879
Average fscore: 0.876
</pre>
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    }

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    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.93617</td>
      <td>0.901639</td>
      <td>0.936709</td>
      <td>0.897436</td>
      <td>0.767123</td>
      <td>0.951807</td>
      <td>0.852632</td>
      <td>0.895349</td>
      <td>0.879518</td>
      <td>0.802632</td>
      <td>0.873418</td>
      <td>0.796875</td>
      <td>0.852459</td>
      <td>0.893333</td>
      <td>0.891566</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.93617</td>
      <td>0.932203</td>
      <td>0.973684</td>
      <td>0.795455</td>
      <td>0.767123</td>
      <td>0.929412</td>
      <td>0.870968</td>
      <td>0.819149</td>
      <td>0.935897</td>
      <td>0.792208</td>
      <td>0.862500</td>
      <td>0.822581</td>
      <td>0.912281</td>
      <td>0.905405</td>
      <td>0.925000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.93617</td>
      <td>0.916667</td>
      <td>0.954839</td>
      <td>0.843373</td>
      <td>0.767123</td>
      <td>0.940476</td>
      <td>0.861702</td>
      <td>0.855556</td>
      <td>0.906832</td>
      <td>0.797386</td>
      <td>0.867925</td>
      <td>0.809524</td>
      <td>0.881356</td>
      <td>0.899329</td>
      <td>0.907975</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.00000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.771
Average precision: 0.767
Average recall: 0.774
Average fscore: 0.766
</pre>
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    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.852632</td>
      <td>0.785714</td>
      <td>0.831461</td>
      <td>0.714286</td>
      <td>0.573770</td>
      <td>0.940476</td>
      <td>0.775000</td>
      <td>0.831461</td>
      <td>0.655914</td>
      <td>0.638889</td>
      <td>0.85</td>
      <td>0.681159</td>
      <td>0.594937</td>
      <td>0.931034</td>
      <td>0.851852</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.861702</td>
      <td>0.932203</td>
      <td>0.973684</td>
      <td>0.568182</td>
      <td>0.479452</td>
      <td>0.929412</td>
      <td>0.666667</td>
      <td>0.787234</td>
      <td>0.782051</td>
      <td>0.597403</td>
      <td>0.85</td>
      <td>0.758065</td>
      <td>0.824561</td>
      <td>0.729730</td>
      <td>0.862500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.857143</td>
      <td>0.852713</td>
      <td>0.896970</td>
      <td>0.632911</td>
      <td>0.522388</td>
      <td>0.934911</td>
      <td>0.716763</td>
      <td>0.808743</td>
      <td>0.713450</td>
      <td>0.617450</td>
      <td>0.85</td>
      <td>0.717557</td>
      <td>0.691176</td>
      <td>0.818182</td>
      <td>0.857143</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.00</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>Hidden layers: 10
Standard scaling
Accuracy: 0.840
Average precision: 0.838
Average recall: 0.843
Average fscore: 0.838
</pre>
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    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.924731</td>
      <td>0.777778</td>
      <td>0.833333</td>
      <td>0.797468</td>
      <td>0.830508</td>
      <td>0.950000</td>
      <td>0.848837</td>
      <td>0.898876</td>
      <td>0.775000</td>
      <td>0.649351</td>
      <td>0.886076</td>
      <td>0.756410</td>
      <td>0.833333</td>
      <td>0.882353</td>
      <td>0.925</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.914894</td>
      <td>0.949153</td>
      <td>0.986842</td>
      <td>0.715909</td>
      <td>0.671233</td>
      <td>0.894118</td>
      <td>0.784946</td>
      <td>0.851064</td>
      <td>0.794872</td>
      <td>0.649351</td>
      <td>0.875000</td>
      <td>0.951613</td>
      <td>0.877193</td>
      <td>0.810811</td>
      <td>0.925</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.919786</td>
      <td>0.854962</td>
      <td>0.903614</td>
      <td>0.754491</td>
      <td>0.742424</td>
      <td>0.921212</td>
      <td>0.815642</td>
      <td>0.874317</td>
      <td>0.784810</td>
      <td>0.649351</td>
      <td>0.880503</td>
      <td>0.842857</td>
      <td>0.854701</td>
      <td>0.845070</td>
      <td>0.925</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.667
Average precision: 0.668
Average recall: 0.667
Average fscore: 0.656
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.833333</td>
      <td>0.796875</td>
      <td>0.672727</td>
      <td>0.685714</td>
      <td>0.371795</td>
      <td>0.904762</td>
      <td>0.760563</td>
      <td>0.815217</td>
      <td>0.527473</td>
      <td>0.565217</td>
      <td>0.746479</td>
      <td>0.458824</td>
      <td>0.437500</td>
      <td>0.621622</td>
      <td>0.815789</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.797872</td>
      <td>0.864407</td>
      <td>0.973684</td>
      <td>0.272727</td>
      <td>0.397260</td>
      <td>0.894118</td>
      <td>0.580645</td>
      <td>0.797872</td>
      <td>0.615385</td>
      <td>0.506494</td>
      <td>0.662500</td>
      <td>0.629032</td>
      <td>0.614035</td>
      <td>0.621622</td>
      <td>0.775000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.815217</td>
      <td>0.829268</td>
      <td>0.795699</td>
      <td>0.390244</td>
      <td>0.384106</td>
      <td>0.899408</td>
      <td>0.658537</td>
      <td>0.806452</td>
      <td>0.568047</td>
      <td>0.534247</td>
      <td>0.701987</td>
      <td>0.530612</td>
      <td>0.510949</td>
      <td>0.621622</td>
      <td>0.794872</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>Hidden layers: 9
Standard scaling
Accuracy: 0.810
Average precision: 0.808
Average recall: 0.813
Average fscore: 0.808
</pre>
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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.87234</td>
      <td>0.805970</td>
      <td>0.888889</td>
      <td>0.768293</td>
      <td>0.706897</td>
      <td>0.937500</td>
      <td>0.790123</td>
      <td>0.860465</td>
      <td>0.809524</td>
      <td>0.586207</td>
      <td>0.909091</td>
      <td>0.690141</td>
      <td>0.690141</td>
      <td>0.923077</td>
      <td>0.883721</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.87234</td>
      <td>0.915254</td>
      <td>0.947368</td>
      <td>0.715909</td>
      <td>0.561644</td>
      <td>0.882353</td>
      <td>0.688172</td>
      <td>0.787234</td>
      <td>0.871795</td>
      <td>0.662338</td>
      <td>0.875000</td>
      <td>0.790323</td>
      <td>0.859649</td>
      <td>0.810811</td>
      <td>0.950000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.87234</td>
      <td>0.857143</td>
      <td>0.917197</td>
      <td>0.741176</td>
      <td>0.625954</td>
      <td>0.909091</td>
      <td>0.735632</td>
      <td>0.822222</td>
      <td>0.839506</td>
      <td>0.621951</td>
      <td>0.891720</td>
      <td>0.736842</td>
      <td>0.765625</td>
      <td>0.863309</td>
      <td>0.915663</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.00000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.743
Average precision: 0.737
Average recall: 0.746
Average fscore: 0.737
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.847826</td>
      <td>0.768116</td>
      <td>0.870588</td>
      <td>0.686567</td>
      <td>0.561404</td>
      <td>0.962963</td>
      <td>0.702381</td>
      <td>0.773810</td>
      <td>0.663158</td>
      <td>0.594595</td>
      <td>0.795181</td>
      <td>0.583333</td>
      <td>0.607595</td>
      <td>0.838710</td>
      <td>0.802326</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.829787</td>
      <td>0.898305</td>
      <td>0.973684</td>
      <td>0.522727</td>
      <td>0.438356</td>
      <td>0.917647</td>
      <td>0.634409</td>
      <td>0.691489</td>
      <td>0.807692</td>
      <td>0.571429</td>
      <td>0.825000</td>
      <td>0.677419</td>
      <td>0.842105</td>
      <td>0.702703</td>
      <td>0.862500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.838710</td>
      <td>0.828125</td>
      <td>0.919255</td>
      <td>0.593548</td>
      <td>0.492308</td>
      <td>0.939759</td>
      <td>0.666667</td>
      <td>0.730337</td>
      <td>0.728324</td>
      <td>0.582781</td>
      <td>0.809816</td>
      <td>0.626866</td>
      <td>0.705882</td>
      <td>0.764706</td>
      <td>0.831325</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>Hidden layers: 7
Standard scaling
Accuracy: 0.772
Average precision: 0.767
Average recall: 0.772
Average fscore: 0.767
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.838710</td>
      <td>0.768116</td>
      <td>0.825581</td>
      <td>0.776316</td>
      <td>0.711864</td>
      <td>0.902439</td>
      <td>0.775281</td>
      <td>0.829268</td>
      <td>0.750000</td>
      <td>0.646341</td>
      <td>0.738636</td>
      <td>0.691176</td>
      <td>0.661290</td>
      <td>0.803030</td>
      <td>0.793478</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.829787</td>
      <td>0.898305</td>
      <td>0.934211</td>
      <td>0.670455</td>
      <td>0.575342</td>
      <td>0.870588</td>
      <td>0.741935</td>
      <td>0.723404</td>
      <td>0.730769</td>
      <td>0.688312</td>
      <td>0.812500</td>
      <td>0.758065</td>
      <td>0.719298</td>
      <td>0.716216</td>
      <td>0.912500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.834225</td>
      <td>0.828125</td>
      <td>0.876543</td>
      <td>0.719512</td>
      <td>0.636364</td>
      <td>0.886228</td>
      <td>0.758242</td>
      <td>0.772727</td>
      <td>0.740260</td>
      <td>0.666667</td>
      <td>0.773810</td>
      <td>0.723077</td>
      <td>0.689076</td>
      <td>0.757143</td>
      <td>0.848837</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.616
Average precision: 0.612
Average recall: 0.620
Average fscore: 0.610
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.708861</td>
      <td>0.680556</td>
      <td>0.691489</td>
      <td>0.521739</td>
      <td>0.343750</td>
      <td>0.936709</td>
      <td>0.588235</td>
      <td>0.771739</td>
      <td>0.555556</td>
      <td>0.500000</td>
      <td>0.618421</td>
      <td>0.518519</td>
      <td>0.360000</td>
      <td>0.593750</td>
      <td>0.795181</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.595745</td>
      <td>0.830508</td>
      <td>0.855263</td>
      <td>0.409091</td>
      <td>0.301370</td>
      <td>0.870588</td>
      <td>0.430108</td>
      <td>0.755319</td>
      <td>0.576923</td>
      <td>0.441558</td>
      <td>0.587500</td>
      <td>0.677419</td>
      <td>0.631579</td>
      <td>0.513514</td>
      <td>0.825000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.647399</td>
      <td>0.748092</td>
      <td>0.764706</td>
      <td>0.458599</td>
      <td>0.321168</td>
      <td>0.902439</td>
      <td>0.496894</td>
      <td>0.763441</td>
      <td>0.566038</td>
      <td>0.468966</td>
      <td>0.602564</td>
      <td>0.587413</td>
      <td>0.458599</td>
      <td>0.550725</td>
      <td>0.809816</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>Hidden layers: 6
Standard scaling
Accuracy: 0.732
Average precision: 0.733
Average recall: 0.739
Average fscore: 0.729
</pre>
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    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.797872</td>
      <td>0.803030</td>
      <td>0.804348</td>
      <td>0.753623</td>
      <td>0.609756</td>
      <td>0.865854</td>
      <td>0.686047</td>
      <td>0.736842</td>
      <td>0.666667</td>
      <td>0.514286</td>
      <td>0.790123</td>
      <td>0.737705</td>
      <td>0.779412</td>
      <td>0.723077</td>
      <td>0.72</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.797872</td>
      <td>0.898305</td>
      <td>0.973684</td>
      <td>0.590909</td>
      <td>0.342466</td>
      <td>0.835294</td>
      <td>0.634409</td>
      <td>0.595745</td>
      <td>0.717949</td>
      <td>0.701299</td>
      <td>0.800000</td>
      <td>0.725806</td>
      <td>0.929825</td>
      <td>0.635135</td>
      <td>0.90</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.797872</td>
      <td>0.848000</td>
      <td>0.880952</td>
      <td>0.662420</td>
      <td>0.438596</td>
      <td>0.850299</td>
      <td>0.659218</td>
      <td>0.658824</td>
      <td>0.691358</td>
      <td>0.593407</td>
      <td>0.795031</td>
      <td>0.731707</td>
      <td>0.848000</td>
      <td>0.676259</td>
      <td>0.80</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.00</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.677
Average precision: 0.669
Average recall: 0.679
Average fscore: 0.669
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.837209</td>
      <td>0.746479</td>
      <td>0.784946</td>
      <td>0.671875</td>
      <td>0.403226</td>
      <td>0.883721</td>
      <td>0.704225</td>
      <td>0.717391</td>
      <td>0.635294</td>
      <td>0.458333</td>
      <td>0.77027</td>
      <td>0.547945</td>
      <td>0.430233</td>
      <td>0.643836</td>
      <td>0.804878</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.765957</td>
      <td>0.898305</td>
      <td>0.960526</td>
      <td>0.488636</td>
      <td>0.342466</td>
      <td>0.894118</td>
      <td>0.537634</td>
      <td>0.702128</td>
      <td>0.692308</td>
      <td>0.428571</td>
      <td>0.71250</td>
      <td>0.645161</td>
      <td>0.649123</td>
      <td>0.635135</td>
      <td>0.825000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.800000</td>
      <td>0.815385</td>
      <td>0.863905</td>
      <td>0.565789</td>
      <td>0.370370</td>
      <td>0.888889</td>
      <td>0.609756</td>
      <td>0.709677</td>
      <td>0.662577</td>
      <td>0.442953</td>
      <td>0.74026</td>
      <td>0.592593</td>
      <td>0.517483</td>
      <td>0.639456</td>
      <td>0.814815</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.00000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>Hidden layers: 6
Standard scaling
Accuracy: 0.735
Average precision: 0.734
Average recall: 0.740
Average fscore: 0.733
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.813725</td>
      <td>0.844828</td>
      <td>0.739130</td>
      <td>0.680000</td>
      <td>0.592105</td>
      <td>0.855422</td>
      <td>0.753425</td>
      <td>0.805556</td>
      <td>0.705128</td>
      <td>0.617284</td>
      <td>0.707317</td>
      <td>0.681818</td>
      <td>0.661972</td>
      <td>0.753623</td>
      <td>0.793478</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.882979</td>
      <td>0.830508</td>
      <td>0.894737</td>
      <td>0.579545</td>
      <td>0.616438</td>
      <td>0.835294</td>
      <td>0.591398</td>
      <td>0.617021</td>
      <td>0.705128</td>
      <td>0.649351</td>
      <td>0.725000</td>
      <td>0.725806</td>
      <td>0.824561</td>
      <td>0.702703</td>
      <td>0.912500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.846939</td>
      <td>0.837607</td>
      <td>0.809524</td>
      <td>0.625767</td>
      <td>0.604027</td>
      <td>0.845238</td>
      <td>0.662651</td>
      <td>0.698795</td>
      <td>0.705128</td>
      <td>0.632911</td>
      <td>0.716049</td>
      <td>0.703125</td>
      <td>0.734375</td>
      <td>0.727273</td>
      <td>0.848837</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
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<pre>
Minmax scaling
Accuracy: 0.458
Average precision: 0.458
Average recall: 0.461
Average fscore: 0.444
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.589147</td>
      <td>0.732394</td>
      <td>0.384615</td>
      <td>0.473684</td>
      <td>0.250000</td>
      <td>0.380000</td>
      <td>0.549020</td>
      <td>0.612500</td>
      <td>0.500000</td>
      <td>0.344828</td>
      <td>0.555556</td>
      <td>0.296296</td>
      <td>0.265060</td>
      <td>0.282828</td>
      <td>0.659091</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.808511</td>
      <td>0.881356</td>
      <td>0.657895</td>
      <td>0.204545</td>
      <td>0.260274</td>
      <td>0.223529</td>
      <td>0.301075</td>
      <td>0.521277</td>
      <td>0.294872</td>
      <td>0.259740</td>
      <td>0.625000</td>
      <td>0.387097</td>
      <td>0.385965</td>
      <td>0.378378</td>
      <td>0.725000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.681614</td>
      <td>0.800000</td>
      <td>0.485437</td>
      <td>0.285714</td>
      <td>0.255034</td>
      <td>0.281481</td>
      <td>0.388889</td>
      <td>0.563218</td>
      <td>0.370968</td>
      <td>0.296296</td>
      <td>0.588235</td>
      <td>0.335664</td>
      <td>0.314286</td>
      <td>0.323699</td>
      <td>0.690476</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
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<pre>Hidden layers: 5
Standard scaling
Accuracy: 0.692
Average precision: 0.685
Average recall: 0.696
Average fscore: 0.684
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.822917</td>
      <td>0.800000</td>
      <td>0.720430</td>
      <td>0.611940</td>
      <td>0.509091</td>
      <td>0.819149</td>
      <td>0.771084</td>
      <td>0.731343</td>
      <td>0.610169</td>
      <td>0.605634</td>
      <td>0.827160</td>
      <td>0.691176</td>
      <td>0.534247</td>
      <td>0.552083</td>
      <td>0.666667</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.840426</td>
      <td>0.881356</td>
      <td>0.881579</td>
      <td>0.465909</td>
      <td>0.383562</td>
      <td>0.905882</td>
      <td>0.688172</td>
      <td>0.521277</td>
      <td>0.461538</td>
      <td>0.558442</td>
      <td>0.837500</td>
      <td>0.758065</td>
      <td>0.684211</td>
      <td>0.716216</td>
      <td>0.850000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.831579</td>
      <td>0.838710</td>
      <td>0.792899</td>
      <td>0.529032</td>
      <td>0.437500</td>
      <td>0.860335</td>
      <td>0.727273</td>
      <td>0.608696</td>
      <td>0.525547</td>
      <td>0.581081</td>
      <td>0.832298</td>
      <td>0.723077</td>
      <td>0.600000</td>
      <td>0.623529</td>
      <td>0.747253</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
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<pre>
Minmax scaling
Accuracy: 0.626
Average precision: 0.613
Average recall: 0.627
Average fscore: 0.612
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.802326</td>
      <td>0.764706</td>
      <td>0.711538</td>
      <td>0.437500</td>
      <td>0.457627</td>
      <td>0.811765</td>
      <td>0.632911</td>
      <td>0.744681</td>
      <td>0.543478</td>
      <td>0.591549</td>
      <td>0.6375</td>
      <td>0.466667</td>
      <td>0.358025</td>
      <td>0.432099</td>
      <td>0.795181</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.734043</td>
      <td>0.881356</td>
      <td>0.973684</td>
      <td>0.159091</td>
      <td>0.369863</td>
      <td>0.811765</td>
      <td>0.537634</td>
      <td>0.744681</td>
      <td>0.641026</td>
      <td>0.545455</td>
      <td>0.6375</td>
      <td>0.564516</td>
      <td>0.508772</td>
      <td>0.472973</td>
      <td>0.825000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.766667</td>
      <td>0.818898</td>
      <td>0.822222</td>
      <td>0.233333</td>
      <td>0.409091</td>
      <td>0.811765</td>
      <td>0.581395</td>
      <td>0.744681</td>
      <td>0.588235</td>
      <td>0.567568</td>
      <td>0.6375</td>
      <td>0.510949</td>
      <td>0.420290</td>
      <td>0.451613</td>
      <td>0.809816</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.0000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<div class="cell border-box-sizing code_cell rendered">
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<div class="prompt input_prompt">In&nbsp;[161]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># try about 2 * hidden layers w/ 40, 30 neurons</span>
<span class="n">nn</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">40</span><span class="p">,</span><span class="mi">30</span><span class="p">),</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Standard scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_std</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_std</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Minmax scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
</pre></div>

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<pre>Standard scaling
Accuracy: 0.922
Average precision: 0.921
Average recall: 0.924
Average fscore: 0.922
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.946809</td>
      <td>0.947368</td>
      <td>0.938272</td>
      <td>0.937500</td>
      <td>0.90411</td>
      <td>0.952941</td>
      <td>0.904255</td>
      <td>0.962963</td>
      <td>0.935065</td>
      <td>0.833333</td>
      <td>0.936709</td>
      <td>0.845070</td>
      <td>0.881356</td>
      <td>0.945205</td>
      <td>0.951220</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.946809</td>
      <td>0.915254</td>
      <td>1.000000</td>
      <td>0.852273</td>
      <td>0.90411</td>
      <td>0.952941</td>
      <td>0.913978</td>
      <td>0.829787</td>
      <td>0.923077</td>
      <td>0.909091</td>
      <td>0.925000</td>
      <td>0.967742</td>
      <td>0.912281</td>
      <td>0.932432</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.946809</td>
      <td>0.931034</td>
      <td>0.968153</td>
      <td>0.892857</td>
      <td>0.90411</td>
      <td>0.952941</td>
      <td>0.909091</td>
      <td>0.891429</td>
      <td>0.929032</td>
      <td>0.869565</td>
      <td>0.930818</td>
      <td>0.902256</td>
      <td>0.896552</td>
      <td>0.938776</td>
      <td>0.962963</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.00000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.852
Average precision: 0.850
Average recall: 0.855
Average fscore: 0.851
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.955056</td>
      <td>0.826087</td>
      <td>0.890244</td>
      <td>0.820513</td>
      <td>0.768116</td>
      <td>0.975904</td>
      <td>0.831461</td>
      <td>0.927711</td>
      <td>0.764045</td>
      <td>0.696203</td>
      <td>0.846154</td>
      <td>0.791045</td>
      <td>0.793651</td>
      <td>0.955224</td>
      <td>0.905882</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.904255</td>
      <td>0.966102</td>
      <td>0.960526</td>
      <td>0.727273</td>
      <td>0.726027</td>
      <td>0.952941</td>
      <td>0.795699</td>
      <td>0.819149</td>
      <td>0.871795</td>
      <td>0.714286</td>
      <td>0.825000</td>
      <td>0.854839</td>
      <td>0.877193</td>
      <td>0.864865</td>
      <td>0.962500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.928962</td>
      <td>0.890625</td>
      <td>0.924051</td>
      <td>0.771084</td>
      <td>0.746479</td>
      <td>0.964286</td>
      <td>0.813187</td>
      <td>0.870056</td>
      <td>0.814371</td>
      <td>0.705128</td>
      <td>0.835443</td>
      <td>0.821705</td>
      <td>0.833333</td>
      <td>0.907801</td>
      <td>0.933333</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<div class="cell border-box-sizing code_cell rendered">
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<div class="prompt input_prompt">In&nbsp;[163]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># try about 2 * hidden layers w/ 45 neurons, logistic activation</span>
<span class="n">nn</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">45</span><span class="p">,</span><span class="mi">45</span><span class="p">),</span><span class="n">activation</span><span class="o">=</span><span class="s1">&#39;logistic&#39;</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Standard scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_std</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_std</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Minmax scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
</pre></div>

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<pre>Standard scaling
Accuracy: 0.934
Average precision: 0.934
Average recall: 0.937
Average fscore: 0.935
</pre>
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        text-align: left;
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    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.928571</td>
      <td>0.965517</td>
      <td>0.937500</td>
      <td>0.939024</td>
      <td>0.868421</td>
      <td>0.953488</td>
      <td>0.890110</td>
      <td>0.976744</td>
      <td>0.935897</td>
      <td>0.897436</td>
      <td>0.948052</td>
      <td>0.897059</td>
      <td>0.932203</td>
      <td>0.945946</td>
      <td>1.000000</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.968085</td>
      <td>0.949153</td>
      <td>0.986842</td>
      <td>0.875000</td>
      <td>0.904110</td>
      <td>0.964706</td>
      <td>0.870968</td>
      <td>0.893617</td>
      <td>0.935897</td>
      <td>0.909091</td>
      <td>0.912500</td>
      <td>0.983871</td>
      <td>0.964912</td>
      <td>0.945946</td>
      <td>0.987500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.947917</td>
      <td>0.957265</td>
      <td>0.961538</td>
      <td>0.905882</td>
      <td>0.885906</td>
      <td>0.959064</td>
      <td>0.880435</td>
      <td>0.933333</td>
      <td>0.935897</td>
      <td>0.903226</td>
      <td>0.929936</td>
      <td>0.938462</td>
      <td>0.948276</td>
      <td>0.945946</td>
      <td>0.993711</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.862
Average precision: 0.859
Average recall: 0.862
Average fscore: 0.860
</pre>
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<pre>c:\users\jerem_000\appdata\local\programs\python\python36\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
</pre>
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        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.925532</td>
      <td>0.865672</td>
      <td>0.902439</td>
      <td>0.839506</td>
      <td>0.750000</td>
      <td>0.963415</td>
      <td>0.844444</td>
      <td>0.838710</td>
      <td>0.862500</td>
      <td>0.791667</td>
      <td>0.876543</td>
      <td>0.787879</td>
      <td>0.839286</td>
      <td>0.876712</td>
      <td>0.913580</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.925532</td>
      <td>0.983051</td>
      <td>0.973684</td>
      <td>0.772727</td>
      <td>0.739726</td>
      <td>0.929412</td>
      <td>0.817204</td>
      <td>0.829787</td>
      <td>0.884615</td>
      <td>0.740260</td>
      <td>0.887500</td>
      <td>0.838710</td>
      <td>0.824561</td>
      <td>0.864865</td>
      <td>0.925000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.925532</td>
      <td>0.920635</td>
      <td>0.936709</td>
      <td>0.804734</td>
      <td>0.744828</td>
      <td>0.946108</td>
      <td>0.830601</td>
      <td>0.834225</td>
      <td>0.873418</td>
      <td>0.765101</td>
      <td>0.881988</td>
      <td>0.812500</td>
      <td>0.831858</td>
      <td>0.870748</td>
      <td>0.919255</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
</div>
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<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[164]:</div>
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    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># try about 2 * hidden layers w/ 45 neurons, relu activation</span>
<span class="n">nn</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">45</span><span class="p">,</span><span class="mi">45</span><span class="p">),</span><span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Standard scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_std</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_std</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Minmax scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
</pre></div>

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<pre>Standard scaling
Accuracy: 0.921
Average precision: 0.920
Average recall: 0.925
Average fscore: 0.921
</pre>
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        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.967742</td>
      <td>0.931034</td>
      <td>0.925000</td>
      <td>0.876543</td>
      <td>0.905405</td>
      <td>0.975904</td>
      <td>0.901099</td>
      <td>0.965517</td>
      <td>0.862500</td>
      <td>0.896104</td>
      <td>0.936709</td>
      <td>0.885714</td>
      <td>0.861538</td>
      <td>0.957143</td>
      <td>0.951220</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.957447</td>
      <td>0.915254</td>
      <td>0.973684</td>
      <td>0.806818</td>
      <td>0.917808</td>
      <td>0.952941</td>
      <td>0.881720</td>
      <td>0.893617</td>
      <td>0.884615</td>
      <td>0.896104</td>
      <td>0.925000</td>
      <td>1.000000</td>
      <td>0.982456</td>
      <td>0.905405</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.962567</td>
      <td>0.923077</td>
      <td>0.948718</td>
      <td>0.840237</td>
      <td>0.911565</td>
      <td>0.964286</td>
      <td>0.891304</td>
      <td>0.928177</td>
      <td>0.873418</td>
      <td>0.896104</td>
      <td>0.930818</td>
      <td>0.939394</td>
      <td>0.918033</td>
      <td>0.930556</td>
      <td>0.962963</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.874
Average precision: 0.874
Average recall: 0.876
Average fscore: 0.872
</pre>
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<div class="output_html rendered_html output_subarea ">
<div>
<style>
    .dataframe thead tr:only-child th {
        text-align: right;
    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.957447</td>
      <td>0.873016</td>
      <td>0.901235</td>
      <td>0.848485</td>
      <td>0.857143</td>
      <td>0.976190</td>
      <td>0.847826</td>
      <td>0.930233</td>
      <td>0.696078</td>
      <td>0.786667</td>
      <td>0.901235</td>
      <td>0.788732</td>
      <td>0.825397</td>
      <td>0.956522</td>
      <td>0.9625</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.957447</td>
      <td>0.932203</td>
      <td>0.960526</td>
      <td>0.636364</td>
      <td>0.739726</td>
      <td>0.964706</td>
      <td>0.838710</td>
      <td>0.851064</td>
      <td>0.910256</td>
      <td>0.766234</td>
      <td>0.912500</td>
      <td>0.903226</td>
      <td>0.912281</td>
      <td>0.891892</td>
      <td>0.9625</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.957447</td>
      <td>0.901639</td>
      <td>0.929936</td>
      <td>0.727273</td>
      <td>0.794118</td>
      <td>0.970414</td>
      <td>0.843243</td>
      <td>0.888889</td>
      <td>0.788889</td>
      <td>0.776316</td>
      <td>0.906832</td>
      <td>0.842105</td>
      <td>0.866667</td>
      <td>0.923077</td>
      <td>0.9625</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.0000</td>
    </tr>
  </tbody>
</table>
</div>
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<div class="prompt input_prompt">In&nbsp;[165]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># try about 2 * hidden layers w/ 45 neurons, tanh activation</span>
<span class="n">nn</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">45</span><span class="p">,</span><span class="mi">45</span><span class="p">),</span><span class="n">activation</span><span class="o">=</span><span class="s1">&#39;tanh&#39;</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Standard scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_std</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_std</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Minmax scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
</pre></div>

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<pre>Standard scaling
Accuracy: 0.930
Average precision: 0.929
Average recall: 0.932
Average fscore: 0.929
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.978261</td>
      <td>0.964286</td>
      <td>0.949367</td>
      <td>0.951220</td>
      <td>0.862500</td>
      <td>0.964286</td>
      <td>0.914894</td>
      <td>0.963415</td>
      <td>0.947368</td>
      <td>0.873418</td>
      <td>0.973684</td>
      <td>0.833333</td>
      <td>0.885246</td>
      <td>0.945205</td>
      <td>0.928571</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.957447</td>
      <td>0.915254</td>
      <td>0.986842</td>
      <td>0.886364</td>
      <td>0.945205</td>
      <td>0.952941</td>
      <td>0.924731</td>
      <td>0.840426</td>
      <td>0.923077</td>
      <td>0.896104</td>
      <td>0.925000</td>
      <td>0.967742</td>
      <td>0.947368</td>
      <td>0.932432</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.967742</td>
      <td>0.939130</td>
      <td>0.967742</td>
      <td>0.917647</td>
      <td>0.901961</td>
      <td>0.958580</td>
      <td>0.919786</td>
      <td>0.897727</td>
      <td>0.935065</td>
      <td>0.884615</td>
      <td>0.948718</td>
      <td>0.895522</td>
      <td>0.915254</td>
      <td>0.938776</td>
      <td>0.951220</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.902
Average precision: 0.902
Average recall: 0.904
Average fscore: 0.902
</pre>
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    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.937500</td>
      <td>0.933333</td>
      <td>0.902439</td>
      <td>0.883117</td>
      <td>0.880597</td>
      <td>0.975904</td>
      <td>0.828283</td>
      <td>0.952941</td>
      <td>0.823529</td>
      <td>0.868421</td>
      <td>0.890244</td>
      <td>0.869565</td>
      <td>0.881356</td>
      <td>0.970588</td>
      <td>0.939024</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.957447</td>
      <td>0.949153</td>
      <td>0.973684</td>
      <td>0.772727</td>
      <td>0.808219</td>
      <td>0.952941</td>
      <td>0.881720</td>
      <td>0.861702</td>
      <td>0.897436</td>
      <td>0.857143</td>
      <td>0.912500</td>
      <td>0.967742</td>
      <td>0.912281</td>
      <td>0.891892</td>
      <td>0.962500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.947368</td>
      <td>0.941176</td>
      <td>0.936709</td>
      <td>0.824242</td>
      <td>0.842857</td>
      <td>0.964286</td>
      <td>0.854167</td>
      <td>0.905028</td>
      <td>0.858896</td>
      <td>0.862745</td>
      <td>0.901235</td>
      <td>0.916031</td>
      <td>0.896552</td>
      <td>0.929577</td>
      <td>0.950617</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
</div>
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<div class="cell border-box-sizing code_cell rendered">
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<div class="prompt input_prompt">In&nbsp;[166]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># try about 3 * hidden layers w/ 40 neurons, relu activation</span>
<span class="n">nn</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">40</span><span class="p">,</span><span class="mi">40</span><span class="p">,</span><span class="mi">40</span><span class="p">),</span><span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Standard scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_std</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_std</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Minmax scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
</pre></div>

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<pre>Standard scaling
Accuracy: 0.932
Average precision: 0.930
Average recall: 0.935
Average fscore: 0.932
</pre>
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        text-align: left;
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        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.988636</td>
      <td>0.932203</td>
      <td>0.925926</td>
      <td>0.962025</td>
      <td>0.888889</td>
      <td>0.987952</td>
      <td>0.913043</td>
      <td>0.988636</td>
      <td>0.891566</td>
      <td>0.848101</td>
      <td>0.974026</td>
      <td>0.873239</td>
      <td>0.861538</td>
      <td>0.932432</td>
      <td>0.987342</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.925532</td>
      <td>0.932203</td>
      <td>0.986842</td>
      <td>0.863636</td>
      <td>0.876712</td>
      <td>0.964706</td>
      <td>0.903226</td>
      <td>0.925532</td>
      <td>0.948718</td>
      <td>0.870130</td>
      <td>0.937500</td>
      <td>1.000000</td>
      <td>0.982456</td>
      <td>0.932432</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.956044</td>
      <td>0.932203</td>
      <td>0.955414</td>
      <td>0.910180</td>
      <td>0.882759</td>
      <td>0.976190</td>
      <td>0.908108</td>
      <td>0.956044</td>
      <td>0.919255</td>
      <td>0.858974</td>
      <td>0.955414</td>
      <td>0.932331</td>
      <td>0.918033</td>
      <td>0.932432</td>
      <td>0.981132</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.870
Average precision: 0.870
Average recall: 0.871
Average fscore: 0.870
</pre>
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<div class="output_html rendered_html output_subarea ">
<div>
<style>
    .dataframe thead tr:only-child th {
        text-align: right;
    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.918367</td>
      <td>0.933333</td>
      <td>0.880952</td>
      <td>0.812500</td>
      <td>0.803030</td>
      <td>0.922222</td>
      <td>0.865169</td>
      <td>0.939024</td>
      <td>0.802469</td>
      <td>0.764706</td>
      <td>0.851852</td>
      <td>0.793651</td>
      <td>0.928571</td>
      <td>0.955224</td>
      <td>0.886364</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.957447</td>
      <td>0.949153</td>
      <td>0.973684</td>
      <td>0.738636</td>
      <td>0.726027</td>
      <td>0.976471</td>
      <td>0.827957</td>
      <td>0.819149</td>
      <td>0.833333</td>
      <td>0.844156</td>
      <td>0.862500</td>
      <td>0.806452</td>
      <td>0.912281</td>
      <td>0.864865</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.937500</td>
      <td>0.941176</td>
      <td>0.925000</td>
      <td>0.773810</td>
      <td>0.762590</td>
      <td>0.948571</td>
      <td>0.846154</td>
      <td>0.875000</td>
      <td>0.817610</td>
      <td>0.802469</td>
      <td>0.857143</td>
      <td>0.800000</td>
      <td>0.920354</td>
      <td>0.907801</td>
      <td>0.928571</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
</div>
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<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[167]:</div>
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    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># try about 3 * hidden layers w/ 75 neurons, relu activation</span>
<span class="n">nn</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">75</span><span class="p">,</span><span class="mi">75</span><span class="p">,</span><span class="mi">75</span><span class="p">),</span><span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Standard scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_std</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_std</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Minmax scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
</pre></div>

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<pre>Standard scaling
Accuracy: 0.944
Average precision: 0.944
Average recall: 0.946
Average fscore: 0.945
</pre>
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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.957447</td>
      <td>0.933333</td>
      <td>0.962025</td>
      <td>0.963415</td>
      <td>0.956522</td>
      <td>0.965517</td>
      <td>0.895833</td>
      <td>0.988235</td>
      <td>0.921053</td>
      <td>0.888889</td>
      <td>0.961538</td>
      <td>0.884058</td>
      <td>0.948276</td>
      <td>0.960526</td>
      <td>0.975</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.957447</td>
      <td>0.949153</td>
      <td>1.000000</td>
      <td>0.897727</td>
      <td>0.904110</td>
      <td>0.988235</td>
      <td>0.924731</td>
      <td>0.893617</td>
      <td>0.897436</td>
      <td>0.935065</td>
      <td>0.937500</td>
      <td>0.983871</td>
      <td>0.964912</td>
      <td>0.986486</td>
      <td>0.975</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.957447</td>
      <td>0.941176</td>
      <td>0.980645</td>
      <td>0.929412</td>
      <td>0.929577</td>
      <td>0.976744</td>
      <td>0.910053</td>
      <td>0.938547</td>
      <td>0.909091</td>
      <td>0.911392</td>
      <td>0.949367</td>
      <td>0.931298</td>
      <td>0.956522</td>
      <td>0.973333</td>
      <td>0.975</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.891
Average precision: 0.892
Average recall: 0.893
Average fscore: 0.892
</pre>
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<div class="output_html rendered_html output_subarea ">
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<style>
    .dataframe thead tr:only-child th {
        text-align: right;
    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.892157</td>
      <td>0.929825</td>
      <td>0.936709</td>
      <td>0.853659</td>
      <td>0.865672</td>
      <td>0.987654</td>
      <td>0.846154</td>
      <td>0.910112</td>
      <td>0.804598</td>
      <td>0.825000</td>
      <td>0.911392</td>
      <td>0.852941</td>
      <td>0.852459</td>
      <td>0.914286</td>
      <td>1.000000</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.968085</td>
      <td>0.898305</td>
      <td>0.973684</td>
      <td>0.795455</td>
      <td>0.794521</td>
      <td>0.941176</td>
      <td>0.827957</td>
      <td>0.861702</td>
      <td>0.897436</td>
      <td>0.857143</td>
      <td>0.900000</td>
      <td>0.935484</td>
      <td>0.912281</td>
      <td>0.864865</td>
      <td>0.962500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.928571</td>
      <td>0.913793</td>
      <td>0.954839</td>
      <td>0.823529</td>
      <td>0.828571</td>
      <td>0.963855</td>
      <td>0.836957</td>
      <td>0.885246</td>
      <td>0.848485</td>
      <td>0.840764</td>
      <td>0.905660</td>
      <td>0.892308</td>
      <td>0.881356</td>
      <td>0.888889</td>
      <td>0.980892</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
</div>
</div>

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<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[168]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># try about 3 * hidden layers w/ 75 neurons, tanh activation</span>
<span class="n">nn</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">75</span><span class="p">,</span><span class="mi">75</span><span class="p">,</span><span class="mi">75</span><span class="p">),</span><span class="n">activation</span><span class="o">=</span><span class="s1">&#39;tanh&#39;</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Standard scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_std</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_std</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Minmax scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
</pre></div>

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<pre>Standard scaling
Accuracy: 0.941
Average precision: 0.941
Average recall: 0.942
Average fscore: 0.941
</pre>
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<style>
    .dataframe thead tr:only-child th {
        text-align: right;
    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.978022</td>
      <td>0.982143</td>
      <td>0.950000</td>
      <td>0.941860</td>
      <td>0.910256</td>
      <td>0.976471</td>
      <td>0.886598</td>
      <td>0.953488</td>
      <td>0.912500</td>
      <td>0.922078</td>
      <td>0.974026</td>
      <td>0.910448</td>
      <td>0.898305</td>
      <td>0.957746</td>
      <td>0.9625</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.946809</td>
      <td>0.932203</td>
      <td>1.000000</td>
      <td>0.920455</td>
      <td>0.972603</td>
      <td>0.976471</td>
      <td>0.924731</td>
      <td>0.872340</td>
      <td>0.935897</td>
      <td>0.922078</td>
      <td>0.937500</td>
      <td>0.983871</td>
      <td>0.929825</td>
      <td>0.918919</td>
      <td>0.9625</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.962162</td>
      <td>0.956522</td>
      <td>0.974359</td>
      <td>0.931034</td>
      <td>0.940397</td>
      <td>0.976471</td>
      <td>0.905263</td>
      <td>0.911111</td>
      <td>0.924051</td>
      <td>0.922078</td>
      <td>0.955414</td>
      <td>0.945736</td>
      <td>0.913793</td>
      <td>0.937931</td>
      <td>0.9625</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.0000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.925
Average precision: 0.926
Average recall: 0.928
Average fscore: 0.926
</pre>
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<div class="output_html rendered_html output_subarea ">
<div>
<style>
    .dataframe thead tr:only-child th {
        text-align: right;
    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.956522</td>
      <td>0.949153</td>
      <td>0.914634</td>
      <td>0.863636</td>
      <td>0.853659</td>
      <td>0.987805</td>
      <td>0.884211</td>
      <td>0.975904</td>
      <td>0.931507</td>
      <td>0.881579</td>
      <td>0.924051</td>
      <td>0.869565</td>
      <td>0.947368</td>
      <td>0.985915</td>
      <td>0.963415</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.936170</td>
      <td>0.949153</td>
      <td>0.986842</td>
      <td>0.863636</td>
      <td>0.958904</td>
      <td>0.952941</td>
      <td>0.903226</td>
      <td>0.861702</td>
      <td>0.871795</td>
      <td>0.870130</td>
      <td>0.912500</td>
      <td>0.967742</td>
      <td>0.947368</td>
      <td>0.945946</td>
      <td>0.987500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.946237</td>
      <td>0.949153</td>
      <td>0.949367</td>
      <td>0.863636</td>
      <td>0.903226</td>
      <td>0.970060</td>
      <td>0.893617</td>
      <td>0.915254</td>
      <td>0.900662</td>
      <td>0.875817</td>
      <td>0.918239</td>
      <td>0.916031</td>
      <td>0.947368</td>
      <td>0.965517</td>
      <td>0.975309</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[169]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># try about 2 * hidden layers w/ 100 neurons, relu activation</span>
<span class="n">nn</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span><span class="mi">100</span><span class="p">),</span><span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Standard scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_std</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_std</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Minmax scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
</pre></div>

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<pre>Standard scaling
Accuracy: 0.948
Average precision: 0.948
Average recall: 0.950
Average fscore: 0.949
</pre>
</div>
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<div class="prompt"></div>


<div class="output_html rendered_html output_subarea ">
<div>
<style>
    .dataframe thead tr:only-child th {
        text-align: right;
    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.957895</td>
      <td>0.950000</td>
      <td>0.950000</td>
      <td>0.975309</td>
      <td>0.971831</td>
      <td>0.953488</td>
      <td>0.915789</td>
      <td>0.976190</td>
      <td>0.912500</td>
      <td>0.923077</td>
      <td>0.962025</td>
      <td>0.895522</td>
      <td>0.965517</td>
      <td>0.945946</td>
      <td>0.963415</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.968085</td>
      <td>0.966102</td>
      <td>1.000000</td>
      <td>0.897727</td>
      <td>0.945205</td>
      <td>0.964706</td>
      <td>0.935484</td>
      <td>0.872340</td>
      <td>0.935897</td>
      <td>0.935065</td>
      <td>0.950000</td>
      <td>0.967742</td>
      <td>0.982456</td>
      <td>0.945946</td>
      <td>0.987500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.962963</td>
      <td>0.957983</td>
      <td>0.974359</td>
      <td>0.934911</td>
      <td>0.958333</td>
      <td>0.959064</td>
      <td>0.925532</td>
      <td>0.921348</td>
      <td>0.924051</td>
      <td>0.929032</td>
      <td>0.955975</td>
      <td>0.930233</td>
      <td>0.973913</td>
      <td>0.945946</td>
      <td>0.975309</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.902
Average precision: 0.900
Average recall: 0.904
Average fscore: 0.900
</pre>
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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.958763</td>
      <td>0.931034</td>
      <td>0.904762</td>
      <td>0.873418</td>
      <td>0.850746</td>
      <td>0.987952</td>
      <td>0.846154</td>
      <td>0.975904</td>
      <td>0.907895</td>
      <td>0.835443</td>
      <td>0.910256</td>
      <td>0.786667</td>
      <td>0.857143</td>
      <td>0.920000</td>
      <td>0.951220</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.989362</td>
      <td>0.915254</td>
      <td>1.000000</td>
      <td>0.784091</td>
      <td>0.780822</td>
      <td>0.964706</td>
      <td>0.827957</td>
      <td>0.861702</td>
      <td>0.884615</td>
      <td>0.857143</td>
      <td>0.887500</td>
      <td>0.951613</td>
      <td>0.947368</td>
      <td>0.932432</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.973822</td>
      <td>0.923077</td>
      <td>0.950000</td>
      <td>0.826347</td>
      <td>0.814286</td>
      <td>0.976190</td>
      <td>0.836957</td>
      <td>0.915254</td>
      <td>0.896104</td>
      <td>0.846154</td>
      <td>0.898734</td>
      <td>0.861314</td>
      <td>0.900000</td>
      <td>0.926174</td>
      <td>0.962963</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<div class="cell border-box-sizing code_cell rendered">
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<div class="prompt input_prompt">In&nbsp;[170]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># try about 2 * hidden layers w/ 150 neurons, relu activation</span>
<span class="n">nn</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span><span class="mi">150</span><span class="p">),</span><span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Standard scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_std</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_std</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Minmax scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
</pre></div>

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<pre>Standard scaling
Accuracy: 0.945
Average precision: 0.946
Average recall: 0.946
Average fscore: 0.945
</pre>
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    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.958763</td>
      <td>0.964912</td>
      <td>0.950000</td>
      <td>0.939759</td>
      <td>0.958333</td>
      <td>0.976190</td>
      <td>0.906250</td>
      <td>0.988235</td>
      <td>0.912500</td>
      <td>0.869048</td>
      <td>0.961538</td>
      <td>0.909091</td>
      <td>0.946429</td>
      <td>0.985915</td>
      <td>0.962963</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.989362</td>
      <td>0.932203</td>
      <td>1.000000</td>
      <td>0.886364</td>
      <td>0.945205</td>
      <td>0.964706</td>
      <td>0.935484</td>
      <td>0.893617</td>
      <td>0.935897</td>
      <td>0.948052</td>
      <td>0.937500</td>
      <td>0.967742</td>
      <td>0.929825</td>
      <td>0.945946</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.973822</td>
      <td>0.948276</td>
      <td>0.974359</td>
      <td>0.912281</td>
      <td>0.951724</td>
      <td>0.970414</td>
      <td>0.920635</td>
      <td>0.938547</td>
      <td>0.924051</td>
      <td>0.906832</td>
      <td>0.949367</td>
      <td>0.937500</td>
      <td>0.938053</td>
      <td>0.965517</td>
      <td>0.968944</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.895
Average precision: 0.893
Average recall: 0.898
Average fscore: 0.894
</pre>
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    .dataframe thead tr:only-child th {
        text-align: right;
    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.927835</td>
      <td>0.916667</td>
      <td>0.903614</td>
      <td>0.833333</td>
      <td>0.845070</td>
      <td>0.964286</td>
      <td>0.946667</td>
      <td>0.965909</td>
      <td>0.855263</td>
      <td>0.840000</td>
      <td>0.901235</td>
      <td>0.808219</td>
      <td>0.797101</td>
      <td>0.943662</td>
      <td>0.939759</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.957447</td>
      <td>0.932203</td>
      <td>0.986842</td>
      <td>0.795455</td>
      <td>0.821918</td>
      <td>0.952941</td>
      <td>0.763441</td>
      <td>0.904255</td>
      <td>0.833333</td>
      <td>0.818182</td>
      <td>0.912500</td>
      <td>0.951613</td>
      <td>0.964912</td>
      <td>0.905405</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.942408</td>
      <td>0.924370</td>
      <td>0.943396</td>
      <td>0.813953</td>
      <td>0.833333</td>
      <td>0.958580</td>
      <td>0.845238</td>
      <td>0.934066</td>
      <td>0.844156</td>
      <td>0.828947</td>
      <td>0.906832</td>
      <td>0.874074</td>
      <td>0.873016</td>
      <td>0.924138</td>
      <td>0.957055</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
</div>
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<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[172]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># try about 2 * hidden layers w/ 60 neurons, relu activation</span>
<span class="n">nn</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">60</span><span class="p">,</span><span class="mi">60</span><span class="p">),</span><span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;No scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Standard scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_std</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_std</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Minmax scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
</pre></div>

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<pre>No scaling
Accuracy: 0.588
Average precision: 0.623
Average recall: 0.580
Average fscore: 0.570
</pre>
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<style>
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        text-align: right;
    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.689655</td>
      <td>0.739130</td>
      <td>0.850000</td>
      <td>0.636364</td>
      <td>0.345865</td>
      <td>0.593750</td>
      <td>0.560345</td>
      <td>0.581395</td>
      <td>0.436975</td>
      <td>0.634146</td>
      <td>0.683333</td>
      <td>0.812500</td>
      <td>0.500000</td>
      <td>0.529412</td>
      <td>0.750000</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.638298</td>
      <td>0.864407</td>
      <td>0.671053</td>
      <td>0.318182</td>
      <td>0.630137</td>
      <td>0.894118</td>
      <td>0.698925</td>
      <td>0.797872</td>
      <td>0.666667</td>
      <td>0.337662</td>
      <td>0.512500</td>
      <td>0.209677</td>
      <td>0.508772</td>
      <td>0.243243</td>
      <td>0.712500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.662983</td>
      <td>0.796875</td>
      <td>0.750000</td>
      <td>0.424242</td>
      <td>0.446602</td>
      <td>0.713615</td>
      <td>0.622010</td>
      <td>0.672646</td>
      <td>0.527919</td>
      <td>0.440678</td>
      <td>0.585714</td>
      <td>0.333333</td>
      <td>0.504348</td>
      <td>0.333333</td>
      <td>0.730769</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Standard scaling
Accuracy: 0.930
Average precision: 0.929
Average recall: 0.933
Average fscore: 0.930
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.956522</td>
      <td>0.964912</td>
      <td>0.891566</td>
      <td>0.913580</td>
      <td>0.931507</td>
      <td>0.965116</td>
      <td>0.911111</td>
      <td>0.988235</td>
      <td>0.890244</td>
      <td>0.876543</td>
      <td>0.974026</td>
      <td>0.910448</td>
      <td>0.846154</td>
      <td>0.956522</td>
      <td>0.963415</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.936170</td>
      <td>0.932203</td>
      <td>0.973684</td>
      <td>0.840909</td>
      <td>0.931507</td>
      <td>0.976471</td>
      <td>0.881720</td>
      <td>0.893617</td>
      <td>0.935897</td>
      <td>0.922078</td>
      <td>0.937500</td>
      <td>0.983871</td>
      <td>0.964912</td>
      <td>0.891892</td>
      <td>0.987500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.946237</td>
      <td>0.948276</td>
      <td>0.930818</td>
      <td>0.875740</td>
      <td>0.931507</td>
      <td>0.970760</td>
      <td>0.896175</td>
      <td>0.938547</td>
      <td>0.912500</td>
      <td>0.898734</td>
      <td>0.955414</td>
      <td>0.945736</td>
      <td>0.901639</td>
      <td>0.923077</td>
      <td>0.975309</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
</div>
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</div>

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<pre>
Minmax scaling
Accuracy: 0.884
Average precision: 0.883
Average recall: 0.885
Average fscore: 0.883
</pre>
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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.967033</td>
      <td>0.875000</td>
      <td>0.892857</td>
      <td>0.850000</td>
      <td>0.797101</td>
      <td>0.976190</td>
      <td>0.797980</td>
      <td>1.000000</td>
      <td>0.804598</td>
      <td>0.816901</td>
      <td>0.869048</td>
      <td>0.838235</td>
      <td>0.864407</td>
      <td>0.984375</td>
      <td>0.918605</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.936170</td>
      <td>0.949153</td>
      <td>0.986842</td>
      <td>0.772727</td>
      <td>0.753425</td>
      <td>0.964706</td>
      <td>0.849462</td>
      <td>0.851064</td>
      <td>0.897436</td>
      <td>0.753247</td>
      <td>0.912500</td>
      <td>0.919355</td>
      <td>0.894737</td>
      <td>0.851351</td>
      <td>0.987500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.951351</td>
      <td>0.910569</td>
      <td>0.937500</td>
      <td>0.809524</td>
      <td>0.774648</td>
      <td>0.970414</td>
      <td>0.822917</td>
      <td>0.919540</td>
      <td>0.848485</td>
      <td>0.783784</td>
      <td>0.890244</td>
      <td>0.876923</td>
      <td>0.879310</td>
      <td>0.913043</td>
      <td>0.951807</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

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<div class="cell border-box-sizing code_cell rendered">
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<div class="prompt input_prompt">In&nbsp;[173]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># try about 1 * hidden layers w/ 100 neurons, relu activation</span>
<span class="n">nn</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">100</span><span class="p">,),</span><span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;No scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Standard scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_std</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_std</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Minmax scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
</pre></div>

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<pre>No scaling
Accuracy: 0.560
Average precision: 0.629
Average recall: 0.556
Average fscore: 0.532
</pre>
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    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.679012</td>
      <td>0.705882</td>
      <td>0.673913</td>
      <td>0.555556</td>
      <td>0.559322</td>
      <td>0.609091</td>
      <td>0.552632</td>
      <td>0.705882</td>
      <td>0.284615</td>
      <td>0.494118</td>
      <td>0.800000</td>
      <td>0.692308</td>
      <td>0.472222</td>
      <td>1.000000</td>
      <td>0.654545</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.585106</td>
      <td>0.813559</td>
      <td>0.815789</td>
      <td>0.056818</td>
      <td>0.452055</td>
      <td>0.788235</td>
      <td>0.451613</td>
      <td>0.638298</td>
      <td>0.948718</td>
      <td>0.545455</td>
      <td>0.650000</td>
      <td>0.290323</td>
      <td>0.298246</td>
      <td>0.108108</td>
      <td>0.900000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.628571</td>
      <td>0.755906</td>
      <td>0.738095</td>
      <td>0.103093</td>
      <td>0.500000</td>
      <td>0.687179</td>
      <td>0.497041</td>
      <td>0.670391</td>
      <td>0.437870</td>
      <td>0.518519</td>
      <td>0.717241</td>
      <td>0.409091</td>
      <td>0.365591</td>
      <td>0.195122</td>
      <td>0.757895</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Standard scaling
Accuracy: 0.941
Average precision: 0.939
Average recall: 0.943
Average fscore: 0.940
</pre>
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    .dataframe thead tr:only-child th {
        text-align: right;
    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.978495</td>
      <td>0.931034</td>
      <td>0.938272</td>
      <td>0.950000</td>
      <td>0.920000</td>
      <td>0.988095</td>
      <td>0.935484</td>
      <td>0.988095</td>
      <td>0.911392</td>
      <td>0.909091</td>
      <td>0.961039</td>
      <td>0.897059</td>
      <td>0.888889</td>
      <td>0.934211</td>
      <td>0.951220</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.968085</td>
      <td>0.915254</td>
      <td>1.000000</td>
      <td>0.863636</td>
      <td>0.945205</td>
      <td>0.976471</td>
      <td>0.935484</td>
      <td>0.882979</td>
      <td>0.923077</td>
      <td>0.909091</td>
      <td>0.925000</td>
      <td>0.983871</td>
      <td>0.982456</td>
      <td>0.959459</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.973262</td>
      <td>0.923077</td>
      <td>0.968153</td>
      <td>0.904762</td>
      <td>0.932432</td>
      <td>0.982249</td>
      <td>0.935484</td>
      <td>0.932584</td>
      <td>0.917197</td>
      <td>0.909091</td>
      <td>0.942675</td>
      <td>0.938462</td>
      <td>0.933333</td>
      <td>0.946667</td>
      <td>0.962963</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.880
Average precision: 0.879
Average recall: 0.883
Average fscore: 0.879
</pre>
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<div class="output_html rendered_html output_subarea ">
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<style>
    .dataframe thead tr:only-child th {
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    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.978022</td>
      <td>0.876923</td>
      <td>0.902439</td>
      <td>0.818182</td>
      <td>0.890625</td>
      <td>0.976190</td>
      <td>0.808511</td>
      <td>0.964706</td>
      <td>0.786517</td>
      <td>0.753247</td>
      <td>0.9125</td>
      <td>0.828125</td>
      <td>0.815385</td>
      <td>0.942029</td>
      <td>0.928571</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.946809</td>
      <td>0.966102</td>
      <td>0.973684</td>
      <td>0.715909</td>
      <td>0.780822</td>
      <td>0.964706</td>
      <td>0.817204</td>
      <td>0.872340</td>
      <td>0.897436</td>
      <td>0.753247</td>
      <td>0.9125</td>
      <td>0.854839</td>
      <td>0.929825</td>
      <td>0.878378</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.962162</td>
      <td>0.919355</td>
      <td>0.936709</td>
      <td>0.763636</td>
      <td>0.832117</td>
      <td>0.970414</td>
      <td>0.812834</td>
      <td>0.916201</td>
      <td>0.838323</td>
      <td>0.753247</td>
      <td>0.9125</td>
      <td>0.841270</td>
      <td>0.868852</td>
      <td>0.909091</td>
      <td>0.951220</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.0000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
</div>
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<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[174]:</div>
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    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># try about 2 * hidden layers w/ 30 neurons, relu activation</span>
<span class="n">nn</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span><span class="mi">30</span><span class="p">),</span><span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;No scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Standard scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_std</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_std</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Minmax scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
</pre></div>

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<pre>No scaling
Accuracy: 0.509
Average precision: 0.585
Average recall: 0.528
Average fscore: 0.513
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.666667</td>
      <td>0.733333</td>
      <td>0.539823</td>
      <td>0.500</td>
      <td>0.338583</td>
      <td>0.894737</td>
      <td>0.511111</td>
      <td>0.857143</td>
      <td>0.236486</td>
      <td>0.755556</td>
      <td>0.630137</td>
      <td>0.240741</td>
      <td>0.478261</td>
      <td>0.852941</td>
      <td>0.541667</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.255319</td>
      <td>0.932203</td>
      <td>0.802632</td>
      <td>0.125</td>
      <td>0.589041</td>
      <td>0.800000</td>
      <td>0.247312</td>
      <td>0.446809</td>
      <td>0.448718</td>
      <td>0.441558</td>
      <td>0.575000</td>
      <td>0.629032</td>
      <td>0.578947</td>
      <td>0.391892</td>
      <td>0.650000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.369231</td>
      <td>0.820896</td>
      <td>0.645503</td>
      <td>0.200</td>
      <td>0.430000</td>
      <td>0.844720</td>
      <td>0.333333</td>
      <td>0.587413</td>
      <td>0.309735</td>
      <td>0.557377</td>
      <td>0.601307</td>
      <td>0.348214</td>
      <td>0.523810</td>
      <td>0.537037</td>
      <td>0.590909</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Standard scaling
Accuracy: 0.932
Average precision: 0.932
Average recall: 0.934
Average fscore: 0.932
</pre>
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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.967391</td>
      <td>0.949153</td>
      <td>0.961538</td>
      <td>0.927711</td>
      <td>0.957143</td>
      <td>0.987952</td>
      <td>0.885417</td>
      <td>0.953488</td>
      <td>0.910256</td>
      <td>0.864198</td>
      <td>0.961538</td>
      <td>0.857143</td>
      <td>0.929825</td>
      <td>0.911392</td>
      <td>0.95</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.946809</td>
      <td>0.949153</td>
      <td>0.986842</td>
      <td>0.875000</td>
      <td>0.917808</td>
      <td>0.964706</td>
      <td>0.913978</td>
      <td>0.872340</td>
      <td>0.910256</td>
      <td>0.909091</td>
      <td>0.937500</td>
      <td>0.967742</td>
      <td>0.929825</td>
      <td>0.972973</td>
      <td>0.95</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.956989</td>
      <td>0.949153</td>
      <td>0.974026</td>
      <td>0.900585</td>
      <td>0.937063</td>
      <td>0.976190</td>
      <td>0.899471</td>
      <td>0.911111</td>
      <td>0.910256</td>
      <td>0.886076</td>
      <td>0.949367</td>
      <td>0.909091</td>
      <td>0.929825</td>
      <td>0.941176</td>
      <td>0.95</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.00</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.828
Average precision: 0.826
Average recall: 0.828
Average fscore: 0.825
</pre>
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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.911111</td>
      <td>0.870968</td>
      <td>0.860465</td>
      <td>0.779070</td>
      <td>0.796610</td>
      <td>0.952941</td>
      <td>0.802198</td>
      <td>0.873563</td>
      <td>0.776471</td>
      <td>0.691358</td>
      <td>0.870130</td>
      <td>0.775862</td>
      <td>0.694444</td>
      <td>0.876923</td>
      <td>0.860465</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.872340</td>
      <td>0.915254</td>
      <td>0.973684</td>
      <td>0.761364</td>
      <td>0.643836</td>
      <td>0.952941</td>
      <td>0.784946</td>
      <td>0.808511</td>
      <td>0.846154</td>
      <td>0.727273</td>
      <td>0.837500</td>
      <td>0.725806</td>
      <td>0.877193</td>
      <td>0.770270</td>
      <td>0.925000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.891304</td>
      <td>0.892562</td>
      <td>0.913580</td>
      <td>0.770115</td>
      <td>0.712121</td>
      <td>0.952941</td>
      <td>0.793478</td>
      <td>0.839779</td>
      <td>0.809816</td>
      <td>0.708861</td>
      <td>0.853503</td>
      <td>0.750000</td>
      <td>0.775194</td>
      <td>0.820144</td>
      <td>0.891566</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[175]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">hn</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">35</span><span class="p">,</span> <span class="mi">66</span><span class="p">,</span> <span class="mi">5</span><span class="p">):</span>
    <span class="n">nn</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="n">hn</span><span class="p">,</span><span class="n">hn</span><span class="p">),</span><span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>
    
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Hidden neurons: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">hn</span><span class="p">))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Standard scaling&quot;</span><span class="p">)</span>
    <span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_std</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_std</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">()</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Minmax scaling&quot;</span><span class="p">)</span>
    <span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">()</span>
</pre></div>

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<pre>Hidden neurons: 35
Standard scaling
Accuracy: 0.928
Average precision: 0.927
Average recall: 0.931
Average fscore: 0.928
</pre>
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        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.93617</td>
      <td>0.903226</td>
      <td>0.903614</td>
      <td>0.917647</td>
      <td>0.942029</td>
      <td>0.987952</td>
      <td>0.909091</td>
      <td>0.976744</td>
      <td>0.933333</td>
      <td>0.896104</td>
      <td>0.9375</td>
      <td>0.835616</td>
      <td>0.931034</td>
      <td>0.934211</td>
      <td>0.962963</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.93617</td>
      <td>0.949153</td>
      <td>0.986842</td>
      <td>0.886364</td>
      <td>0.890411</td>
      <td>0.964706</td>
      <td>0.860215</td>
      <td>0.893617</td>
      <td>0.897436</td>
      <td>0.896104</td>
      <td>0.9375</td>
      <td>0.983871</td>
      <td>0.947368</td>
      <td>0.959459</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.93617</td>
      <td>0.925620</td>
      <td>0.943396</td>
      <td>0.901734</td>
      <td>0.915493</td>
      <td>0.976190</td>
      <td>0.883978</td>
      <td>0.933333</td>
      <td>0.915033</td>
      <td>0.896104</td>
      <td>0.9375</td>
      <td>0.903704</td>
      <td>0.939130</td>
      <td>0.946667</td>
      <td>0.968944</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.00000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.0000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.838
Average precision: 0.844
Average recall: 0.841
Average fscore: 0.838
</pre>
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    .dataframe thead tr:only-child th {
        text-align: right;
    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.913978</td>
      <td>0.861538</td>
      <td>0.878049</td>
      <td>0.763158</td>
      <td>0.870370</td>
      <td>0.987179</td>
      <td>0.820225</td>
      <td>0.892857</td>
      <td>0.672897</td>
      <td>0.717647</td>
      <td>0.829545</td>
      <td>0.772727</td>
      <td>0.844828</td>
      <td>0.948276</td>
      <td>0.885057</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.904255</td>
      <td>0.949153</td>
      <td>0.947368</td>
      <td>0.659091</td>
      <td>0.643836</td>
      <td>0.905882</td>
      <td>0.784946</td>
      <td>0.797872</td>
      <td>0.923077</td>
      <td>0.792208</td>
      <td>0.912500</td>
      <td>0.822581</td>
      <td>0.859649</td>
      <td>0.743243</td>
      <td>0.962500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.909091</td>
      <td>0.903226</td>
      <td>0.911392</td>
      <td>0.707317</td>
      <td>0.740157</td>
      <td>0.944785</td>
      <td>0.802198</td>
      <td>0.842697</td>
      <td>0.778378</td>
      <td>0.753086</td>
      <td>0.869048</td>
      <td>0.796875</td>
      <td>0.852174</td>
      <td>0.833333</td>
      <td>0.922156</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
</div>
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<pre>
Hidden neurons: 40
Standard scaling
Accuracy: 0.932
Average precision: 0.930
Average recall: 0.933
Average fscore: 0.931
</pre>
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<style>
    .dataframe thead tr:only-child th {
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        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.989130</td>
      <td>0.934426</td>
      <td>0.904762</td>
      <td>0.934211</td>
      <td>0.918919</td>
      <td>0.987952</td>
      <td>0.88172</td>
      <td>0.955556</td>
      <td>0.894118</td>
      <td>0.909091</td>
      <td>0.974026</td>
      <td>0.859155</td>
      <td>0.894737</td>
      <td>0.942857</td>
      <td>0.975</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.968085</td>
      <td>0.966102</td>
      <td>1.000000</td>
      <td>0.806818</td>
      <td>0.931507</td>
      <td>0.964706</td>
      <td>0.88172</td>
      <td>0.914894</td>
      <td>0.974359</td>
      <td>0.909091</td>
      <td>0.937500</td>
      <td>0.983871</td>
      <td>0.894737</td>
      <td>0.891892</td>
      <td>0.975</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.978495</td>
      <td>0.950000</td>
      <td>0.950000</td>
      <td>0.865854</td>
      <td>0.925170</td>
      <td>0.976190</td>
      <td>0.88172</td>
      <td>0.934783</td>
      <td>0.932515</td>
      <td>0.909091</td>
      <td>0.955414</td>
      <td>0.917293</td>
      <td>0.894737</td>
      <td>0.916667</td>
      <td>0.975</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.00000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.881
Average precision: 0.881
Average recall: 0.884
Average fscore: 0.881
</pre>
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<style>
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        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.948454</td>
      <td>0.932203</td>
      <td>0.880952</td>
      <td>0.827160</td>
      <td>0.783784</td>
      <td>0.964286</td>
      <td>0.863636</td>
      <td>0.951220</td>
      <td>0.775281</td>
      <td>0.831169</td>
      <td>0.894737</td>
      <td>0.850746</td>
      <td>0.830769</td>
      <td>0.967742</td>
      <td>0.917647</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.978723</td>
      <td>0.932203</td>
      <td>0.973684</td>
      <td>0.761364</td>
      <td>0.794521</td>
      <td>0.952941</td>
      <td>0.817204</td>
      <td>0.829787</td>
      <td>0.884615</td>
      <td>0.831169</td>
      <td>0.850000</td>
      <td>0.919355</td>
      <td>0.947368</td>
      <td>0.810811</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.963351</td>
      <td>0.932203</td>
      <td>0.925000</td>
      <td>0.792899</td>
      <td>0.789116</td>
      <td>0.958580</td>
      <td>0.839779</td>
      <td>0.886364</td>
      <td>0.826347</td>
      <td>0.831169</td>
      <td>0.871795</td>
      <td>0.883721</td>
      <td>0.885246</td>
      <td>0.882353</td>
      <td>0.945455</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Hidden neurons: 45
Standard scaling
Accuracy: 0.927
Average precision: 0.928
Average recall: 0.928
Average fscore: 0.927
</pre>
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        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.968421</td>
      <td>0.946429</td>
      <td>0.936709</td>
      <td>0.890244</td>
      <td>0.929577</td>
      <td>0.941860</td>
      <td>0.888889</td>
      <td>0.975904</td>
      <td>0.887500</td>
      <td>0.857143</td>
      <td>0.961039</td>
      <td>0.882353</td>
      <td>0.962963</td>
      <td>0.932432</td>
      <td>0.963415</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.978723</td>
      <td>0.898305</td>
      <td>0.973684</td>
      <td>0.829545</td>
      <td>0.904110</td>
      <td>0.952941</td>
      <td>0.946237</td>
      <td>0.861702</td>
      <td>0.910256</td>
      <td>0.935065</td>
      <td>0.925000</td>
      <td>0.967742</td>
      <td>0.912281</td>
      <td>0.932432</td>
      <td>0.987500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.973545</td>
      <td>0.921739</td>
      <td>0.954839</td>
      <td>0.858824</td>
      <td>0.916667</td>
      <td>0.947368</td>
      <td>0.916667</td>
      <td>0.915254</td>
      <td>0.898734</td>
      <td>0.894410</td>
      <td>0.942675</td>
      <td>0.923077</td>
      <td>0.936937</td>
      <td>0.932432</td>
      <td>0.975309</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.890
Average precision: 0.889
Average recall: 0.893
Average fscore: 0.889
</pre>
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        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.947917</td>
      <td>0.915254</td>
      <td>0.949367</td>
      <td>0.846154</td>
      <td>0.904762</td>
      <td>0.975309</td>
      <td>0.896552</td>
      <td>0.964286</td>
      <td>0.775281</td>
      <td>0.797297</td>
      <td>0.858824</td>
      <td>0.819444</td>
      <td>0.820896</td>
      <td>0.932432</td>
      <td>0.926829</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.968085</td>
      <td>0.915254</td>
      <td>0.986842</td>
      <td>0.750000</td>
      <td>0.780822</td>
      <td>0.929412</td>
      <td>0.838710</td>
      <td>0.861702</td>
      <td>0.884615</td>
      <td>0.766234</td>
      <td>0.912500</td>
      <td>0.951613</td>
      <td>0.964912</td>
      <td>0.932432</td>
      <td>0.950000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.957895</td>
      <td>0.915254</td>
      <td>0.967742</td>
      <td>0.795181</td>
      <td>0.838235</td>
      <td>0.951807</td>
      <td>0.866667</td>
      <td>0.910112</td>
      <td>0.826347</td>
      <td>0.781457</td>
      <td>0.884848</td>
      <td>0.880597</td>
      <td>0.887097</td>
      <td>0.932432</td>
      <td>0.938272</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
</div>
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<pre>
Hidden neurons: 50
Standard scaling
Accuracy: 0.936
Average precision: 0.934
Average recall: 0.938
Average fscore: 0.936
</pre>
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    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.968750</td>
      <td>0.934426</td>
      <td>0.914634</td>
      <td>0.912500</td>
      <td>0.896104</td>
      <td>0.975904</td>
      <td>0.913978</td>
      <td>0.977011</td>
      <td>0.911392</td>
      <td>0.923077</td>
      <td>0.961039</td>
      <td>0.895522</td>
      <td>0.885246</td>
      <td>0.985714</td>
      <td>0.962025</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.989362</td>
      <td>0.966102</td>
      <td>0.986842</td>
      <td>0.829545</td>
      <td>0.945205</td>
      <td>0.952941</td>
      <td>0.913978</td>
      <td>0.904255</td>
      <td>0.923077</td>
      <td>0.935065</td>
      <td>0.925000</td>
      <td>0.967742</td>
      <td>0.947368</td>
      <td>0.932432</td>
      <td>0.950000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.978947</td>
      <td>0.950000</td>
      <td>0.949367</td>
      <td>0.869048</td>
      <td>0.920000</td>
      <td>0.964286</td>
      <td>0.913978</td>
      <td>0.939227</td>
      <td>0.917197</td>
      <td>0.929032</td>
      <td>0.942675</td>
      <td>0.930233</td>
      <td>0.915254</td>
      <td>0.958333</td>
      <td>0.955975</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
</div>
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<pre>
Minmax scaling
Accuracy: 0.900
Average precision: 0.901
Average recall: 0.903
Average fscore: 0.900
</pre>
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<div class="output_html rendered_html output_subarea ">
<div>
<style>
    .dataframe thead tr:only-child th {
        text-align: right;
    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.966292</td>
      <td>0.934426</td>
      <td>0.891566</td>
      <td>0.826087</td>
      <td>0.787500</td>
      <td>0.975610</td>
      <td>0.857143</td>
      <td>0.913043</td>
      <td>0.943662</td>
      <td>0.882353</td>
      <td>0.891566</td>
      <td>0.838235</td>
      <td>0.857143</td>
      <td>0.970149</td>
      <td>0.975</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.914894</td>
      <td>0.966102</td>
      <td>0.973684</td>
      <td>0.863636</td>
      <td>0.863014</td>
      <td>0.941176</td>
      <td>0.838710</td>
      <td>0.893617</td>
      <td>0.858974</td>
      <td>0.779221</td>
      <td>0.925000</td>
      <td>0.919355</td>
      <td>0.947368</td>
      <td>0.878378</td>
      <td>0.975</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.939891</td>
      <td>0.950000</td>
      <td>0.930818</td>
      <td>0.844444</td>
      <td>0.823529</td>
      <td>0.958084</td>
      <td>0.847826</td>
      <td>0.903226</td>
      <td>0.899329</td>
      <td>0.827586</td>
      <td>0.907975</td>
      <td>0.876923</td>
      <td>0.900000</td>
      <td>0.921986</td>
      <td>0.975</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

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<pre>
Hidden neurons: 55
Standard scaling
Accuracy: 0.940
Average precision: 0.938
Average recall: 0.943
Average fscore: 0.940
</pre>
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<div class="output_html rendered_html output_subarea ">
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<style>
    .dataframe thead tr:only-child th {
        text-align: right;
    }

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        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.978947</td>
      <td>0.934426</td>
      <td>0.962025</td>
      <td>0.917647</td>
      <td>0.944444</td>
      <td>0.964706</td>
      <td>0.933333</td>
      <td>0.964706</td>
      <td>0.909091</td>
      <td>0.910256</td>
      <td>0.961538</td>
      <td>0.871429</td>
      <td>0.903226</td>
      <td>0.958904</td>
      <td>0.9625</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.989362</td>
      <td>0.966102</td>
      <td>1.000000</td>
      <td>0.886364</td>
      <td>0.931507</td>
      <td>0.964706</td>
      <td>0.903226</td>
      <td>0.872340</td>
      <td>0.897436</td>
      <td>0.922078</td>
      <td>0.937500</td>
      <td>0.983871</td>
      <td>0.982456</td>
      <td>0.945946</td>
      <td>0.9625</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.984127</td>
      <td>0.950000</td>
      <td>0.980645</td>
      <td>0.901734</td>
      <td>0.937931</td>
      <td>0.964706</td>
      <td>0.918033</td>
      <td>0.916201</td>
      <td>0.903226</td>
      <td>0.916129</td>
      <td>0.949367</td>
      <td>0.924242</td>
      <td>0.941176</td>
      <td>0.952381</td>
      <td>0.9625</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.0000</td>
    </tr>
  </tbody>
</table>
</div>
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</div>

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<pre>
Minmax scaling
Accuracy: 0.879
Average precision: 0.880
Average recall: 0.880
Average fscore: 0.879
</pre>
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<div class="output_html rendered_html output_subarea ">
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<style>
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        text-align: right;
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    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.919192</td>
      <td>0.916667</td>
      <td>0.833333</td>
      <td>0.811765</td>
      <td>0.726190</td>
      <td>0.953488</td>
      <td>0.853933</td>
      <td>0.891304</td>
      <td>0.871429</td>
      <td>0.845070</td>
      <td>0.918919</td>
      <td>0.811594</td>
      <td>0.910714</td>
      <td>0.955224</td>
      <td>0.987179</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.968085</td>
      <td>0.932203</td>
      <td>0.986842</td>
      <td>0.784091</td>
      <td>0.835616</td>
      <td>0.964706</td>
      <td>0.817204</td>
      <td>0.872340</td>
      <td>0.782051</td>
      <td>0.779221</td>
      <td>0.850000</td>
      <td>0.903226</td>
      <td>0.894737</td>
      <td>0.864865</td>
      <td>0.962500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.943005</td>
      <td>0.924370</td>
      <td>0.903614</td>
      <td>0.797688</td>
      <td>0.777070</td>
      <td>0.959064</td>
      <td>0.835165</td>
      <td>0.881720</td>
      <td>0.824324</td>
      <td>0.810811</td>
      <td>0.883117</td>
      <td>0.854962</td>
      <td>0.902655</td>
      <td>0.907801</td>
      <td>0.974684</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
</div>
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<pre>
Hidden neurons: 60
Standard scaling
Accuracy: 0.944
Average precision: 0.942
Average recall: 0.946
Average fscore: 0.943
</pre>
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<div class="output_html rendered_html output_subarea ">
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<style>
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        text-align: right;
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    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.978261</td>
      <td>0.919355</td>
      <td>0.962025</td>
      <td>0.963415</td>
      <td>0.930556</td>
      <td>0.976471</td>
      <td>0.914894</td>
      <td>0.976471</td>
      <td>0.961039</td>
      <td>0.886076</td>
      <td>0.961538</td>
      <td>0.869565</td>
      <td>0.918033</td>
      <td>0.958904</td>
      <td>0.951220</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.957447</td>
      <td>0.966102</td>
      <td>1.000000</td>
      <td>0.897727</td>
      <td>0.917808</td>
      <td>0.976471</td>
      <td>0.924731</td>
      <td>0.882979</td>
      <td>0.948718</td>
      <td>0.909091</td>
      <td>0.937500</td>
      <td>0.967742</td>
      <td>0.982456</td>
      <td>0.945946</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.967742</td>
      <td>0.942149</td>
      <td>0.980645</td>
      <td>0.929412</td>
      <td>0.924138</td>
      <td>0.976471</td>
      <td>0.919786</td>
      <td>0.927374</td>
      <td>0.954839</td>
      <td>0.897436</td>
      <td>0.949367</td>
      <td>0.916031</td>
      <td>0.949153</td>
      <td>0.952381</td>
      <td>0.962963</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
</div>
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<pre>
Minmax scaling
Accuracy: 0.865
Average precision: 0.865
Average recall: 0.864
Average fscore: 0.863
</pre>
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    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.946809</td>
      <td>0.814286</td>
      <td>0.935065</td>
      <td>0.800000</td>
      <td>0.818182</td>
      <td>0.953488</td>
      <td>0.810526</td>
      <td>0.861702</td>
      <td>0.781609</td>
      <td>0.743902</td>
      <td>0.920000</td>
      <td>0.830508</td>
      <td>0.886792</td>
      <td>0.902778</td>
      <td>0.9625</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.946809</td>
      <td>0.966102</td>
      <td>0.947368</td>
      <td>0.727273</td>
      <td>0.739726</td>
      <td>0.964706</td>
      <td>0.827957</td>
      <td>0.861702</td>
      <td>0.871795</td>
      <td>0.792208</td>
      <td>0.862500</td>
      <td>0.790323</td>
      <td>0.824561</td>
      <td>0.878378</td>
      <td>0.9625</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.946809</td>
      <td>0.883721</td>
      <td>0.941176</td>
      <td>0.761905</td>
      <td>0.776978</td>
      <td>0.959064</td>
      <td>0.819149</td>
      <td>0.861702</td>
      <td>0.824242</td>
      <td>0.767296</td>
      <td>0.890323</td>
      <td>0.809917</td>
      <td>0.854545</td>
      <td>0.890411</td>
      <td>0.9625</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.0000</td>
    </tr>
  </tbody>
</table>
</div>
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<pre>
Hidden neurons: 65
Standard scaling
Accuracy: 0.945
Average precision: 0.944
Average recall: 0.947
Average fscore: 0.945
</pre>
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<style>
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    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.968421</td>
      <td>0.918033</td>
      <td>0.938272</td>
      <td>0.928571</td>
      <td>0.958333</td>
      <td>0.987654</td>
      <td>0.925532</td>
      <td>0.988372</td>
      <td>0.933333</td>
      <td>0.890244</td>
      <td>0.986842</td>
      <td>0.910448</td>
      <td>0.916667</td>
      <td>0.934211</td>
      <td>0.975</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.978723</td>
      <td>0.949153</td>
      <td>1.000000</td>
      <td>0.886364</td>
      <td>0.945205</td>
      <td>0.941176</td>
      <td>0.935484</td>
      <td>0.904255</td>
      <td>0.897436</td>
      <td>0.948052</td>
      <td>0.937500</td>
      <td>0.983871</td>
      <td>0.964912</td>
      <td>0.959459</td>
      <td>0.975</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.973545</td>
      <td>0.933333</td>
      <td>0.968153</td>
      <td>0.906977</td>
      <td>0.951724</td>
      <td>0.963855</td>
      <td>0.930481</td>
      <td>0.944444</td>
      <td>0.915033</td>
      <td>0.918239</td>
      <td>0.961538</td>
      <td>0.945736</td>
      <td>0.940171</td>
      <td>0.946667</td>
      <td>0.975</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000</td>
    </tr>
  </tbody>
</table>
</div>
</div>

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<div class="output_subarea output_stream output_stdout output_text">
<pre>
Minmax scaling
Accuracy: 0.885
Average precision: 0.884
Average recall: 0.889
Average fscore: 0.884
</pre>
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<div class="output_html rendered_html output_subarea ">
<div>
<style>
    .dataframe thead tr:only-child th {
        text-align: right;
    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.937500</td>
      <td>0.850746</td>
      <td>0.925926</td>
      <td>0.901408</td>
      <td>0.888889</td>
      <td>0.975904</td>
      <td>0.844444</td>
      <td>0.940476</td>
      <td>0.802198</td>
      <td>0.791667</td>
      <td>0.9</td>
      <td>0.791667</td>
      <td>0.794118</td>
      <td>0.958333</td>
      <td>0.95</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.957447</td>
      <td>0.966102</td>
      <td>0.986842</td>
      <td>0.727273</td>
      <td>0.767123</td>
      <td>0.952941</td>
      <td>0.817204</td>
      <td>0.840426</td>
      <td>0.935897</td>
      <td>0.740260</td>
      <td>0.9</td>
      <td>0.919355</td>
      <td>0.947368</td>
      <td>0.932432</td>
      <td>0.95</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.947368</td>
      <td>0.904762</td>
      <td>0.955414</td>
      <td>0.805031</td>
      <td>0.823529</td>
      <td>0.964286</td>
      <td>0.830601</td>
      <td>0.887640</td>
      <td>0.863905</td>
      <td>0.765101</td>
      <td>0.9</td>
      <td>0.850746</td>
      <td>0.864000</td>
      <td>0.945205</td>
      <td>0.95</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.0</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.00</td>
    </tr>
  </tbody>
</table>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># try about 2 * hidden layers w/ 120 neurons, relu activation</span>
<span class="n">nn</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">120</span><span class="p">,</span><span class="mi">120</span><span class="p">),</span><span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;No scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Standard scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_std</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_std</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Minmax scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
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<pre>No scaling
Accuracy: 0.478
Average precision: 0.542
Average recall: 0.474
Average fscore: 0.438
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<pre>c:\users\jerem_000\appdata\local\programs\python\python36\lib\site-packages\sklearn\metrics\classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.
  &#39;precision&#39;, &#39;predicted&#39;, average, warn_for)
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.401015</td>
      <td>0.781818</td>
      <td>0.657895</td>
      <td>0.517241</td>
      <td>0.328947</td>
      <td>0.933333</td>
      <td>0.736842</td>
      <td>0.533898</td>
      <td>0.461538</td>
      <td>0.321053</td>
      <td>0.600000</td>
      <td>0.578947</td>
      <td>0.0</td>
      <td>0.340541</td>
      <td>0.937500</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.840426</td>
      <td>0.728814</td>
      <td>0.657895</td>
      <td>0.170455</td>
      <td>0.342466</td>
      <td>0.329412</td>
      <td>0.150538</td>
      <td>0.670213</td>
      <td>0.615385</td>
      <td>0.792208</td>
      <td>0.037500</td>
      <td>0.354839</td>
      <td>0.0</td>
      <td>0.851351</td>
      <td>0.562500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.542955</td>
      <td>0.754386</td>
      <td>0.657895</td>
      <td>0.256410</td>
      <td>0.335570</td>
      <td>0.486957</td>
      <td>0.250000</td>
      <td>0.594340</td>
      <td>0.527473</td>
      <td>0.456929</td>
      <td>0.070588</td>
      <td>0.440000</td>
      <td>0.0</td>
      <td>0.486486</td>
      <td>0.703125</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.0</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
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<pre>
Standard scaling
Accuracy: 0.951
Average precision: 0.951
Average recall: 0.954
Average fscore: 0.952
</pre>
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    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.957895</td>
      <td>0.965517</td>
      <td>0.950000</td>
      <td>0.962963</td>
      <td>0.946667</td>
      <td>0.964286</td>
      <td>0.916667</td>
      <td>1.000000</td>
      <td>0.935897</td>
      <td>0.935897</td>
      <td>0.987013</td>
      <td>0.871429</td>
      <td>0.965517</td>
      <td>0.959459</td>
      <td>0.952381</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.968085</td>
      <td>0.949153</td>
      <td>1.000000</td>
      <td>0.886364</td>
      <td>0.972603</td>
      <td>0.952941</td>
      <td>0.946237</td>
      <td>0.872340</td>
      <td>0.935897</td>
      <td>0.948052</td>
      <td>0.950000</td>
      <td>0.983871</td>
      <td>0.982456</td>
      <td>0.959459</td>
      <td>1.000000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.962963</td>
      <td>0.957265</td>
      <td>0.974359</td>
      <td>0.923077</td>
      <td>0.959459</td>
      <td>0.958580</td>
      <td>0.931217</td>
      <td>0.931818</td>
      <td>0.935897</td>
      <td>0.941935</td>
      <td>0.968153</td>
      <td>0.924242</td>
      <td>0.973913</td>
      <td>0.959459</td>
      <td>0.975610</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
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<pre>
Minmax scaling
Accuracy: 0.896
Average precision: 0.894
Average recall: 0.899
Average fscore: 0.895
</pre>
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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.967742</td>
      <td>0.861538</td>
      <td>0.948052</td>
      <td>0.831325</td>
      <td>0.913793</td>
      <td>0.987952</td>
      <td>0.903614</td>
      <td>0.954023</td>
      <td>0.827160</td>
      <td>0.761905</td>
      <td>0.9125</td>
      <td>0.808219</td>
      <td>0.835821</td>
      <td>0.921053</td>
      <td>0.975</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.957447</td>
      <td>0.949153</td>
      <td>0.960526</td>
      <td>0.784091</td>
      <td>0.726027</td>
      <td>0.964706</td>
      <td>0.806452</td>
      <td>0.882979</td>
      <td>0.858974</td>
      <td>0.831169</td>
      <td>0.9125</td>
      <td>0.951613</td>
      <td>0.982456</td>
      <td>0.945946</td>
      <td>0.975</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.962567</td>
      <td>0.903226</td>
      <td>0.954248</td>
      <td>0.807018</td>
      <td>0.809160</td>
      <td>0.976190</td>
      <td>0.852273</td>
      <td>0.917127</td>
      <td>0.842767</td>
      <td>0.795031</td>
      <td>0.9125</td>
      <td>0.874074</td>
      <td>0.903226</td>
      <td>0.933333</td>
      <td>0.975</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.0000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000</td>
    </tr>
  </tbody>
</table>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># try about 2 * hidden layers w/ 130 neurons, relu activation</span>
<span class="n">nn</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">130</span><span class="p">,</span><span class="mi">130</span><span class="p">),</span><span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Standard scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_std</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_std</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Minmax scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
</pre></div>

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<pre>Standard scaling
Accuracy: 0.949
Average precision: 0.949
Average recall: 0.951
Average fscore: 0.949
</pre>
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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.968421</td>
      <td>0.950000</td>
      <td>0.926829</td>
      <td>0.974684</td>
      <td>0.985294</td>
      <td>0.987805</td>
      <td>0.927083</td>
      <td>0.976471</td>
      <td>0.935065</td>
      <td>0.891566</td>
      <td>0.961538</td>
      <td>0.895522</td>
      <td>0.982456</td>
      <td>0.924051</td>
      <td>0.951220</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.978723</td>
      <td>0.966102</td>
      <td>1.000000</td>
      <td>0.875000</td>
      <td>0.917808</td>
      <td>0.952941</td>
      <td>0.956989</td>
      <td>0.882979</td>
      <td>0.923077</td>
      <td>0.961039</td>
      <td>0.937500</td>
      <td>0.967742</td>
      <td>0.982456</td>
      <td>0.986486</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.973545</td>
      <td>0.957983</td>
      <td>0.962025</td>
      <td>0.922156</td>
      <td>0.950355</td>
      <td>0.970060</td>
      <td>0.941799</td>
      <td>0.927374</td>
      <td>0.929032</td>
      <td>0.925000</td>
      <td>0.949367</td>
      <td>0.930233</td>
      <td>0.982456</td>
      <td>0.954248</td>
      <td>0.962963</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.885
Average precision: 0.884
Average recall: 0.889
Average fscore: 0.884
</pre>
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<style>
    .dataframe thead tr:only-child th {
        text-align: right;
    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.956989</td>
      <td>0.918033</td>
      <td>0.903614</td>
      <td>0.860759</td>
      <td>0.717647</td>
      <td>0.975904</td>
      <td>0.902439</td>
      <td>0.953488</td>
      <td>0.831325</td>
      <td>0.830769</td>
      <td>0.902439</td>
      <td>0.830769</td>
      <td>0.777778</td>
      <td>0.942857</td>
      <td>0.950617</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.946809</td>
      <td>0.949153</td>
      <td>0.986842</td>
      <td>0.772727</td>
      <td>0.835616</td>
      <td>0.952941</td>
      <td>0.795699</td>
      <td>0.872340</td>
      <td>0.884615</td>
      <td>0.701299</td>
      <td>0.925000</td>
      <td>0.870968</td>
      <td>0.982456</td>
      <td>0.891892</td>
      <td>0.962500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.951872</td>
      <td>0.933333</td>
      <td>0.943396</td>
      <td>0.814371</td>
      <td>0.772152</td>
      <td>0.964286</td>
      <td>0.845714</td>
      <td>0.911111</td>
      <td>0.857143</td>
      <td>0.760563</td>
      <td>0.913580</td>
      <td>0.850394</td>
      <td>0.868217</td>
      <td>0.916667</td>
      <td>0.956522</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
</div>
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<div class="cell border-box-sizing code_cell rendered">
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<div class="prompt input_prompt">In&nbsp;[178]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># try about 2 * hidden layers w/ 120 neurons, identity activation</span>
<span class="n">nn</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">120</span><span class="p">,</span><span class="mi">120</span><span class="p">),</span><span class="n">activation</span><span class="o">=</span><span class="s1">&#39;identity&#39;</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Standard scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_std</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_std</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Minmax scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
</pre></div>

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<pre>Standard scaling
Accuracy: 0.871
Average precision: 0.871
Average recall: 0.874
Average fscore: 0.871
</pre>
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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.945055</td>
      <td>0.901639</td>
      <td>0.903614</td>
      <td>0.819277</td>
      <td>0.818182</td>
      <td>0.963855</td>
      <td>0.812500</td>
      <td>0.941176</td>
      <td>0.844156</td>
      <td>0.702381</td>
      <td>0.933333</td>
      <td>0.794521</td>
      <td>0.818182</td>
      <td>0.898551</td>
      <td>0.961538</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.914894</td>
      <td>0.932203</td>
      <td>0.986842</td>
      <td>0.772727</td>
      <td>0.739726</td>
      <td>0.941176</td>
      <td>0.838710</td>
      <td>0.851064</td>
      <td>0.833333</td>
      <td>0.766234</td>
      <td>0.875000</td>
      <td>0.935484</td>
      <td>0.947368</td>
      <td>0.837838</td>
      <td>0.937500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.929730</td>
      <td>0.916667</td>
      <td>0.943396</td>
      <td>0.795322</td>
      <td>0.776978</td>
      <td>0.952381</td>
      <td>0.825397</td>
      <td>0.893855</td>
      <td>0.838710</td>
      <td>0.732919</td>
      <td>0.903226</td>
      <td>0.859259</td>
      <td>0.878049</td>
      <td>0.867133</td>
      <td>0.949367</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.845
Average precision: 0.845
Average recall: 0.851
Average fscore: 0.844
</pre>
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        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.904255</td>
      <td>0.873016</td>
      <td>0.925000</td>
      <td>0.794521</td>
      <td>0.682927</td>
      <td>0.975904</td>
      <td>0.825000</td>
      <td>0.916667</td>
      <td>0.717391</td>
      <td>0.854545</td>
      <td>0.901235</td>
      <td>0.763158</td>
      <td>0.768116</td>
      <td>0.897059</td>
      <td>0.877778</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.904255</td>
      <td>0.932203</td>
      <td>0.973684</td>
      <td>0.659091</td>
      <td>0.767123</td>
      <td>0.952941</td>
      <td>0.709677</td>
      <td>0.819149</td>
      <td>0.846154</td>
      <td>0.610390</td>
      <td>0.912500</td>
      <td>0.935484</td>
      <td>0.929825</td>
      <td>0.824324</td>
      <td>0.987500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.904255</td>
      <td>0.901639</td>
      <td>0.948718</td>
      <td>0.720497</td>
      <td>0.722581</td>
      <td>0.964286</td>
      <td>0.763006</td>
      <td>0.865169</td>
      <td>0.776471</td>
      <td>0.712121</td>
      <td>0.906832</td>
      <td>0.840580</td>
      <td>0.841270</td>
      <td>0.859155</td>
      <td>0.929412</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
</div>
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<div class="cell border-box-sizing code_cell rendered">
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<div class="prompt input_prompt">In&nbsp;[180]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># try about 2 * hidden layers w/ 120 neurons, tanh activation</span>
<span class="n">nn</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">120</span><span class="p">,</span><span class="mi">120</span><span class="p">),</span><span class="n">activation</span><span class="o">=</span><span class="s1">&#39;tanh&#39;</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Standard scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_std</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_std</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Minmax scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
</pre></div>

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<pre>Standard scaling
Accuracy: 0.950
Average precision: 0.950
Average recall: 0.953
Average fscore: 0.950
</pre>
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<style>
    .dataframe thead tr:only-child th {
        text-align: right;
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    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.968421</td>
      <td>0.982759</td>
      <td>0.938272</td>
      <td>0.974684</td>
      <td>0.958904</td>
      <td>0.965116</td>
      <td>0.926316</td>
      <td>1.000000</td>
      <td>0.960526</td>
      <td>0.888889</td>
      <td>0.961039</td>
      <td>0.911765</td>
      <td>0.885246</td>
      <td>0.972973</td>
      <td>0.951807</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.978723</td>
      <td>0.966102</td>
      <td>1.000000</td>
      <td>0.875000</td>
      <td>0.958904</td>
      <td>0.976471</td>
      <td>0.946237</td>
      <td>0.882979</td>
      <td>0.935897</td>
      <td>0.935065</td>
      <td>0.925000</td>
      <td>1.000000</td>
      <td>0.947368</td>
      <td>0.972973</td>
      <td>0.987500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.973545</td>
      <td>0.974359</td>
      <td>0.968153</td>
      <td>0.922156</td>
      <td>0.958904</td>
      <td>0.970760</td>
      <td>0.936170</td>
      <td>0.937853</td>
      <td>0.948052</td>
      <td>0.911392</td>
      <td>0.942675</td>
      <td>0.953846</td>
      <td>0.915254</td>
      <td>0.972973</td>
      <td>0.969325</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.916
Average precision: 0.917
Average recall: 0.918
Average fscore: 0.917
</pre>
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<div class="output_html rendered_html output_subarea ">
<div>
<style>
    .dataframe thead tr:only-child th {
        text-align: right;
    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.976744</td>
      <td>0.950000</td>
      <td>0.903614</td>
      <td>0.882353</td>
      <td>0.901408</td>
      <td>0.952381</td>
      <td>0.836538</td>
      <td>0.975904</td>
      <td>0.920000</td>
      <td>0.896104</td>
      <td>0.924051</td>
      <td>0.904762</td>
      <td>0.815385</td>
      <td>0.958333</td>
      <td>0.951807</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.893617</td>
      <td>0.966102</td>
      <td>0.986842</td>
      <td>0.852273</td>
      <td>0.876712</td>
      <td>0.941176</td>
      <td>0.935484</td>
      <td>0.861702</td>
      <td>0.884615</td>
      <td>0.896104</td>
      <td>0.912500</td>
      <td>0.919355</td>
      <td>0.929825</td>
      <td>0.932432</td>
      <td>0.987500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.933333</td>
      <td>0.957983</td>
      <td>0.943396</td>
      <td>0.867052</td>
      <td>0.888889</td>
      <td>0.946746</td>
      <td>0.883249</td>
      <td>0.915254</td>
      <td>0.901961</td>
      <td>0.896104</td>
      <td>0.918239</td>
      <td>0.912000</td>
      <td>0.868852</td>
      <td>0.945205</td>
      <td>0.969325</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
</div>
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<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[181]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># try about 2 * hidden layers w/ 120 neurons, logistic activation</span>
<span class="n">nn</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">120</span><span class="p">,</span><span class="mi">120</span><span class="p">),</span><span class="n">activation</span><span class="o">=</span><span class="s1">&#39;logistic&#39;</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Standard scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_std</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_std</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Minmax scaling&quot;</span><span class="p">)</span>
<span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
</pre></div>

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<pre>Standard scaling
Accuracy: 0.948
Average precision: 0.947
Average recall: 0.949
Average fscore: 0.948
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.978947</td>
      <td>0.901639</td>
      <td>0.926829</td>
      <td>0.930233</td>
      <td>0.958333</td>
      <td>0.964286</td>
      <td>0.946237</td>
      <td>1.000000</td>
      <td>0.935065</td>
      <td>0.896104</td>
      <td>0.948718</td>
      <td>0.938462</td>
      <td>0.949153</td>
      <td>0.960000</td>
      <td>0.963855</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.989362</td>
      <td>0.932203</td>
      <td>1.000000</td>
      <td>0.909091</td>
      <td>0.945205</td>
      <td>0.952941</td>
      <td>0.946237</td>
      <td>0.882979</td>
      <td>0.923077</td>
      <td>0.896104</td>
      <td>0.925000</td>
      <td>0.983871</td>
      <td>0.982456</td>
      <td>0.972973</td>
      <td>1.000000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.984127</td>
      <td>0.916667</td>
      <td>0.962025</td>
      <td>0.919540</td>
      <td>0.951724</td>
      <td>0.958580</td>
      <td>0.946237</td>
      <td>0.937853</td>
      <td>0.929032</td>
      <td>0.896104</td>
      <td>0.936709</td>
      <td>0.960630</td>
      <td>0.965517</td>
      <td>0.966443</td>
      <td>0.981595</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
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<pre>
Minmax scaling
Accuracy: 0.844
Average precision: 0.843
Average recall: 0.847
Average fscore: 0.843
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.905263</td>
      <td>0.904762</td>
      <td>0.882353</td>
      <td>0.826087</td>
      <td>0.757143</td>
      <td>0.975904</td>
      <td>0.839080</td>
      <td>0.862069</td>
      <td>0.744444</td>
      <td>0.696203</td>
      <td>0.864198</td>
      <td>0.764706</td>
      <td>0.816667</td>
      <td>0.898551</td>
      <td>0.904762</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.914894</td>
      <td>0.966102</td>
      <td>0.986842</td>
      <td>0.647727</td>
      <td>0.726027</td>
      <td>0.952941</td>
      <td>0.784946</td>
      <td>0.797872</td>
      <td>0.858974</td>
      <td>0.714286</td>
      <td>0.875000</td>
      <td>0.838710</td>
      <td>0.859649</td>
      <td>0.837838</td>
      <td>0.950000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.910053</td>
      <td>0.934426</td>
      <td>0.931677</td>
      <td>0.726115</td>
      <td>0.741259</td>
      <td>0.964286</td>
      <td>0.811111</td>
      <td>0.828729</td>
      <td>0.797619</td>
      <td>0.705128</td>
      <td>0.869565</td>
      <td>0.800000</td>
      <td>0.837607</td>
      <td>0.867133</td>
      <td>0.926829</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<div class="cell border-box-sizing code_cell rendered">
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<div class="prompt input_prompt">In&nbsp;[184]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># try about 1 * hidden layers w/ 8 neurons</span>
<span class="k">for</span> <span class="n">activation</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;relu&quot;</span><span class="p">,</span> <span class="s2">&quot;logistic&quot;</span><span class="p">,</span> <span class="s2">&quot;tanh&quot;</span><span class="p">]:</span>
    <span class="n">nn</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,),</span><span class="n">activation</span><span class="o">=</span><span class="n">activation</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>
    
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Activation: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">activation</span><span class="p">))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Standard scaling&quot;</span><span class="p">)</span>
    <span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_std</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_std</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">()</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Minmax scaling&quot;</span><span class="p">)</span>
    <span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">()</span>
</pre></div>

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<pre>Activation: relu
Standard scaling
Accuracy: 0.821
Average precision: 0.816
Average recall: 0.823
Average fscore: 0.817
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.914894</td>
      <td>0.788732</td>
      <td>0.848837</td>
      <td>0.756098</td>
      <td>0.783333</td>
      <td>0.940476</td>
      <td>0.858824</td>
      <td>0.895349</td>
      <td>0.727273</td>
      <td>0.733333</td>
      <td>0.829268</td>
      <td>0.739130</td>
      <td>0.720588</td>
      <td>0.805970</td>
      <td>0.892857</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.914894</td>
      <td>0.949153</td>
      <td>0.960526</td>
      <td>0.704545</td>
      <td>0.643836</td>
      <td>0.929412</td>
      <td>0.784946</td>
      <td>0.819149</td>
      <td>0.717949</td>
      <td>0.714286</td>
      <td>0.850000</td>
      <td>0.822581</td>
      <td>0.859649</td>
      <td>0.729730</td>
      <td>0.937500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.914894</td>
      <td>0.861538</td>
      <td>0.901235</td>
      <td>0.729412</td>
      <td>0.706767</td>
      <td>0.934911</td>
      <td>0.820225</td>
      <td>0.855556</td>
      <td>0.722581</td>
      <td>0.723684</td>
      <td>0.839506</td>
      <td>0.778626</td>
      <td>0.784000</td>
      <td>0.765957</td>
      <td>0.914634</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.742
Average precision: 0.738
Average recall: 0.745
Average fscore: 0.739
</pre>
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<pre>c:\users\jerem_000\appdata\local\programs\python\python36\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
</pre>
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    .dataframe tbody tr th {
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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.858696</td>
      <td>0.818182</td>
      <td>0.774194</td>
      <td>0.708333</td>
      <td>0.553846</td>
      <td>0.950000</td>
      <td>0.728395</td>
      <td>0.733333</td>
      <td>0.662921</td>
      <td>0.661972</td>
      <td>0.797468</td>
      <td>0.594937</td>
      <td>0.637681</td>
      <td>0.757576</td>
      <td>0.833333</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.840426</td>
      <td>0.915254</td>
      <td>0.947368</td>
      <td>0.579545</td>
      <td>0.493151</td>
      <td>0.894118</td>
      <td>0.634409</td>
      <td>0.702128</td>
      <td>0.756410</td>
      <td>0.610390</td>
      <td>0.787500</td>
      <td>0.758065</td>
      <td>0.771930</td>
      <td>0.675676</td>
      <td>0.812500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.849462</td>
      <td>0.864000</td>
      <td>0.852071</td>
      <td>0.637500</td>
      <td>0.521739</td>
      <td>0.921212</td>
      <td>0.678161</td>
      <td>0.717391</td>
      <td>0.706587</td>
      <td>0.635135</td>
      <td>0.792453</td>
      <td>0.666667</td>
      <td>0.698413</td>
      <td>0.714286</td>
      <td>0.822785</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Activation: logistic
Standard scaling
Accuracy: 0.809
Average precision: 0.806
Average recall: 0.813
Average fscore: 0.806
</pre>
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    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.916667</td>
      <td>0.881356</td>
      <td>0.818182</td>
      <td>0.746032</td>
      <td>0.701493</td>
      <td>0.906977</td>
      <td>0.869048</td>
      <td>0.878378</td>
      <td>0.800000</td>
      <td>0.704545</td>
      <td>0.851351</td>
      <td>0.675000</td>
      <td>0.784615</td>
      <td>0.753247</td>
      <td>0.808989</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.936170</td>
      <td>0.881356</td>
      <td>0.947368</td>
      <td>0.534091</td>
      <td>0.643836</td>
      <td>0.917647</td>
      <td>0.784946</td>
      <td>0.691489</td>
      <td>0.820513</td>
      <td>0.805195</td>
      <td>0.787500</td>
      <td>0.870968</td>
      <td>0.894737</td>
      <td>0.783784</td>
      <td>0.900000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.926316</td>
      <td>0.881356</td>
      <td>0.878049</td>
      <td>0.622517</td>
      <td>0.671429</td>
      <td>0.912281</td>
      <td>0.824859</td>
      <td>0.773810</td>
      <td>0.810127</td>
      <td>0.751515</td>
      <td>0.818182</td>
      <td>0.760563</td>
      <td>0.836066</td>
      <td>0.768212</td>
      <td>0.852071</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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Minmax scaling
Accuracy: 0.695
Average precision: 0.691
Average recall: 0.695
Average fscore: 0.688
</pre>
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  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.822917</td>
      <td>0.790323</td>
      <td>0.768421</td>
      <td>0.758621</td>
      <td>0.412698</td>
      <td>0.865854</td>
      <td>0.828947</td>
      <td>0.707317</td>
      <td>0.666667</td>
      <td>0.510638</td>
      <td>0.785714</td>
      <td>0.558824</td>
      <td>0.526316</td>
      <td>0.625000</td>
      <td>0.739130</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.840426</td>
      <td>0.830508</td>
      <td>0.960526</td>
      <td>0.500000</td>
      <td>0.356164</td>
      <td>0.835294</td>
      <td>0.677419</td>
      <td>0.617021</td>
      <td>0.717949</td>
      <td>0.623377</td>
      <td>0.687500</td>
      <td>0.612903</td>
      <td>0.701754</td>
      <td>0.608108</td>
      <td>0.850000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.831579</td>
      <td>0.809917</td>
      <td>0.853801</td>
      <td>0.602740</td>
      <td>0.382353</td>
      <td>0.850299</td>
      <td>0.745562</td>
      <td>0.659091</td>
      <td>0.691358</td>
      <td>0.561404</td>
      <td>0.733333</td>
      <td>0.584615</td>
      <td>0.601504</td>
      <td>0.616438</td>
      <td>0.790698</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
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Activation: tanh
Standard scaling
Accuracy: 0.821
Average precision: 0.819
Average recall: 0.822
Average fscore: 0.819
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.913043</td>
      <td>0.870968</td>
      <td>0.875000</td>
      <td>0.688172</td>
      <td>0.793103</td>
      <td>0.962500</td>
      <td>0.833333</td>
      <td>0.852273</td>
      <td>0.788732</td>
      <td>0.74026</td>
      <td>0.804598</td>
      <td>0.728571</td>
      <td>0.724638</td>
      <td>0.785714</td>
      <td>0.927711</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.893617</td>
      <td>0.915254</td>
      <td>0.921053</td>
      <td>0.727273</td>
      <td>0.630137</td>
      <td>0.905882</td>
      <td>0.806452</td>
      <td>0.797872</td>
      <td>0.717949</td>
      <td>0.74026</td>
      <td>0.875000</td>
      <td>0.822581</td>
      <td>0.877193</td>
      <td>0.743243</td>
      <td>0.962500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.903226</td>
      <td>0.892562</td>
      <td>0.897436</td>
      <td>0.707182</td>
      <td>0.702290</td>
      <td>0.933333</td>
      <td>0.819672</td>
      <td>0.824176</td>
      <td>0.751678</td>
      <td>0.74026</td>
      <td>0.838323</td>
      <td>0.772727</td>
      <td>0.793651</td>
      <td>0.763889</td>
      <td>0.944785</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.00000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
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<pre>
Minmax scaling
Accuracy: 0.778
Average precision: 0.773
Average recall: 0.778
Average fscore: 0.773
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.863158</td>
      <td>0.836066</td>
      <td>0.848837</td>
      <td>0.759494</td>
      <td>0.500000</td>
      <td>0.938272</td>
      <td>0.772727</td>
      <td>0.851852</td>
      <td>0.783133</td>
      <td>0.627907</td>
      <td>0.728261</td>
      <td>0.637681</td>
      <td>0.746269</td>
      <td>0.850000</td>
      <td>0.845238</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.872340</td>
      <td>0.864407</td>
      <td>0.960526</td>
      <td>0.681818</td>
      <td>0.397260</td>
      <td>0.894118</td>
      <td>0.731183</td>
      <td>0.734043</td>
      <td>0.833333</td>
      <td>0.701299</td>
      <td>0.837500</td>
      <td>0.709677</td>
      <td>0.877193</td>
      <td>0.689189</td>
      <td>0.887500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.867725</td>
      <td>0.850000</td>
      <td>0.901235</td>
      <td>0.718563</td>
      <td>0.442748</td>
      <td>0.915663</td>
      <td>0.751381</td>
      <td>0.788571</td>
      <td>0.807453</td>
      <td>0.662577</td>
      <td>0.779070</td>
      <td>0.671756</td>
      <td>0.806452</td>
      <td>0.761194</td>
      <td>0.865854</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
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<div class="cell border-box-sizing code_cell rendered">
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<div class="prompt input_prompt">In&nbsp;[186]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># try about 1 * hidden layers w/ 30 neurons</span>
<span class="k">for</span> <span class="n">activation</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;relu&quot;</span><span class="p">,</span> <span class="s2">&quot;logistic&quot;</span><span class="p">,</span> <span class="s2">&quot;tanh&quot;</span><span class="p">]:</span>
    <span class="n">nn</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">30</span><span class="p">,),</span><span class="n">activation</span><span class="o">=</span><span class="n">activation</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">750</span><span class="p">)</span>
    
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Activation: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">activation</span><span class="p">))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Standard scaling&quot;</span><span class="p">)</span>
    <span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_std</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_std</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">()</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Minmax scaling&quot;</span><span class="p">)</span>
    <span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">()</span>
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<pre>Activation: relu
Standard scaling
Accuracy: 0.928
Average precision: 0.927
Average recall: 0.930
Average fscore: 0.928
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.988889</td>
      <td>0.932203</td>
      <td>0.882353</td>
      <td>0.912500</td>
      <td>0.868421</td>
      <td>0.965116</td>
      <td>0.888889</td>
      <td>0.975904</td>
      <td>0.935065</td>
      <td>0.891892</td>
      <td>0.949367</td>
      <td>0.884058</td>
      <td>0.887097</td>
      <td>0.971429</td>
      <td>0.975309</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.946809</td>
      <td>0.932203</td>
      <td>0.986842</td>
      <td>0.829545</td>
      <td>0.904110</td>
      <td>0.976471</td>
      <td>0.946237</td>
      <td>0.861702</td>
      <td>0.923077</td>
      <td>0.857143</td>
      <td>0.937500</td>
      <td>0.983871</td>
      <td>0.964912</td>
      <td>0.918919</td>
      <td>0.987500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.967391</td>
      <td>0.932203</td>
      <td>0.931677</td>
      <td>0.869048</td>
      <td>0.885906</td>
      <td>0.970760</td>
      <td>0.916667</td>
      <td>0.915254</td>
      <td>0.929032</td>
      <td>0.874172</td>
      <td>0.943396</td>
      <td>0.931298</td>
      <td>0.924370</td>
      <td>0.944444</td>
      <td>0.981366</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.875
Average precision: 0.874
Average recall: 0.878
Average fscore: 0.874
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.988889</td>
      <td>0.901639</td>
      <td>0.925000</td>
      <td>0.797619</td>
      <td>0.881356</td>
      <td>0.953488</td>
      <td>0.842697</td>
      <td>0.952381</td>
      <td>0.810127</td>
      <td>0.734940</td>
      <td>0.880952</td>
      <td>0.777778</td>
      <td>0.818182</td>
      <td>0.887324</td>
      <td>0.951220</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.946809</td>
      <td>0.932203</td>
      <td>0.973684</td>
      <td>0.761364</td>
      <td>0.712329</td>
      <td>0.964706</td>
      <td>0.806452</td>
      <td>0.851064</td>
      <td>0.820513</td>
      <td>0.792208</td>
      <td>0.925000</td>
      <td>0.903226</td>
      <td>0.947368</td>
      <td>0.851351</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.967391</td>
      <td>0.916667</td>
      <td>0.948718</td>
      <td>0.779070</td>
      <td>0.787879</td>
      <td>0.959064</td>
      <td>0.824176</td>
      <td>0.898876</td>
      <td>0.815287</td>
      <td>0.762500</td>
      <td>0.902439</td>
      <td>0.835821</td>
      <td>0.878049</td>
      <td>0.868966</td>
      <td>0.962963</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
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<pre>
Activation: logistic
Standard scaling
Accuracy: 0.928
Average precision: 0.928
Average recall: 0.930
Average fscore: 0.929
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.957895</td>
      <td>0.950000</td>
      <td>0.925926</td>
      <td>0.895349</td>
      <td>0.941176</td>
      <td>0.975309</td>
      <td>0.895833</td>
      <td>0.943182</td>
      <td>0.922078</td>
      <td>0.858974</td>
      <td>0.925926</td>
      <td>0.869565</td>
      <td>0.900000</td>
      <td>0.972222</td>
      <td>0.987179</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.968085</td>
      <td>0.966102</td>
      <td>0.986842</td>
      <td>0.875000</td>
      <td>0.876712</td>
      <td>0.929412</td>
      <td>0.924731</td>
      <td>0.882979</td>
      <td>0.910256</td>
      <td>0.870130</td>
      <td>0.937500</td>
      <td>0.967742</td>
      <td>0.947368</td>
      <td>0.945946</td>
      <td>0.962500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.962963</td>
      <td>0.957983</td>
      <td>0.955414</td>
      <td>0.885057</td>
      <td>0.907801</td>
      <td>0.951807</td>
      <td>0.910053</td>
      <td>0.912088</td>
      <td>0.916129</td>
      <td>0.864516</td>
      <td>0.931677</td>
      <td>0.916031</td>
      <td>0.923077</td>
      <td>0.958904</td>
      <td>0.974684</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.862
Average precision: 0.859
Average recall: 0.864
Average fscore: 0.860
</pre>
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  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.925532</td>
      <td>0.838235</td>
      <td>0.914634</td>
      <td>0.800000</td>
      <td>0.768116</td>
      <td>0.975904</td>
      <td>0.840909</td>
      <td>0.909091</td>
      <td>0.776471</td>
      <td>0.743243</td>
      <td>0.9</td>
      <td>0.805970</td>
      <td>0.803279</td>
      <td>0.914286</td>
      <td>0.962963</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.925532</td>
      <td>0.966102</td>
      <td>0.986842</td>
      <td>0.727273</td>
      <td>0.726027</td>
      <td>0.952941</td>
      <td>0.795699</td>
      <td>0.851064</td>
      <td>0.846154</td>
      <td>0.714286</td>
      <td>0.9</td>
      <td>0.870968</td>
      <td>0.859649</td>
      <td>0.864865</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.925532</td>
      <td>0.897638</td>
      <td>0.949367</td>
      <td>0.761905</td>
      <td>0.746479</td>
      <td>0.964286</td>
      <td>0.817680</td>
      <td>0.879121</td>
      <td>0.809816</td>
      <td>0.728477</td>
      <td>0.9</td>
      <td>0.837209</td>
      <td>0.830508</td>
      <td>0.888889</td>
      <td>0.968944</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.0</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
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<pre>
Activation: tanh
Standard scaling
Accuracy: 0.909
Average precision: 0.910
Average recall: 0.912
Average fscore: 0.909
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.934783</td>
      <td>0.963636</td>
      <td>0.915663</td>
      <td>0.886076</td>
      <td>0.915493</td>
      <td>0.975904</td>
      <td>0.851064</td>
      <td>0.965517</td>
      <td>0.944444</td>
      <td>0.811765</td>
      <td>0.925926</td>
      <td>0.840580</td>
      <td>0.805970</td>
      <td>0.943662</td>
      <td>0.962963</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.914894</td>
      <td>0.898305</td>
      <td>1.000000</td>
      <td>0.795455</td>
      <td>0.890411</td>
      <td>0.952941</td>
      <td>0.860215</td>
      <td>0.893617</td>
      <td>0.871795</td>
      <td>0.896104</td>
      <td>0.937500</td>
      <td>0.935484</td>
      <td>0.947368</td>
      <td>0.905405</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.924731</td>
      <td>0.929825</td>
      <td>0.955975</td>
      <td>0.838323</td>
      <td>0.902778</td>
      <td>0.964286</td>
      <td>0.855615</td>
      <td>0.928177</td>
      <td>0.906667</td>
      <td>0.851852</td>
      <td>0.931677</td>
      <td>0.885496</td>
      <td>0.870968</td>
      <td>0.924138</td>
      <td>0.968944</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
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<pre>
Minmax scaling
Accuracy: 0.886
Average precision: 0.886
Average recall: 0.889
Average fscore: 0.886
</pre>
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<table border="1" class="dataframe">
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      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.988889</td>
      <td>0.903226</td>
      <td>0.913580</td>
      <td>0.828947</td>
      <td>0.888889</td>
      <td>0.976190</td>
      <td>0.812500</td>
      <td>0.953488</td>
      <td>0.802326</td>
      <td>0.779221</td>
      <td>0.891566</td>
      <td>0.808219</td>
      <td>0.828125</td>
      <td>0.969697</td>
      <td>0.939759</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.946809</td>
      <td>0.949153</td>
      <td>0.973684</td>
      <td>0.715909</td>
      <td>0.767123</td>
      <td>0.964706</td>
      <td>0.838710</td>
      <td>0.872340</td>
      <td>0.884615</td>
      <td>0.779221</td>
      <td>0.925000</td>
      <td>0.951613</td>
      <td>0.929825</td>
      <td>0.864865</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.967391</td>
      <td>0.925620</td>
      <td>0.942675</td>
      <td>0.768293</td>
      <td>0.823529</td>
      <td>0.970414</td>
      <td>0.825397</td>
      <td>0.911111</td>
      <td>0.841463</td>
      <td>0.779221</td>
      <td>0.907975</td>
      <td>0.874074</td>
      <td>0.876033</td>
      <td>0.914286</td>
      <td>0.957055</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
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<div class="cell border-box-sizing code_cell rendered">
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<div class="prompt input_prompt">In&nbsp;[187]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># try about 1 * hidden layers w/ 45 neurons</span>
<span class="k">for</span> <span class="n">activation</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;relu&quot;</span><span class="p">,</span> <span class="s2">&quot;logistic&quot;</span><span class="p">,</span> <span class="s2">&quot;tanh&quot;</span><span class="p">]:</span>
    <span class="n">nn</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">45</span><span class="p">,),</span><span class="n">activation</span><span class="o">=</span><span class="n">activation</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">750</span><span class="p">)</span>
    
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Activation: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">activation</span><span class="p">))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Standard scaling&quot;</span><span class="p">)</span>
    <span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_std</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_std</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">()</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Minmax scaling&quot;</span><span class="p">)</span>
    <span class="n">train_test_report</span><span class="p">(</span><span class="n">X_train_minmax</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test_minmax</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">nn</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">()</span>
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<pre>Activation: relu
Standard scaling
Accuracy: 0.930
Average precision: 0.929
Average recall: 0.932
Average fscore: 0.930
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.956522</td>
      <td>0.949153</td>
      <td>0.914634</td>
      <td>0.915663</td>
      <td>0.894737</td>
      <td>0.943182</td>
      <td>0.922222</td>
      <td>0.976190</td>
      <td>0.878049</td>
      <td>0.910256</td>
      <td>0.973684</td>
      <td>0.867647</td>
      <td>0.898305</td>
      <td>1.000000</td>
      <td>0.939759</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.936170</td>
      <td>0.949153</td>
      <td>0.986842</td>
      <td>0.863636</td>
      <td>0.931507</td>
      <td>0.976471</td>
      <td>0.892473</td>
      <td>0.872340</td>
      <td>0.923077</td>
      <td>0.922078</td>
      <td>0.925000</td>
      <td>0.951613</td>
      <td>0.929825</td>
      <td>0.945946</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.946237</td>
      <td>0.949153</td>
      <td>0.949367</td>
      <td>0.888889</td>
      <td>0.912752</td>
      <td>0.959538</td>
      <td>0.907104</td>
      <td>0.921348</td>
      <td>0.900000</td>
      <td>0.916129</td>
      <td>0.948718</td>
      <td>0.907692</td>
      <td>0.913793</td>
      <td>0.972222</td>
      <td>0.957055</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
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<pre>
Minmax scaling
Accuracy: 0.863
Average precision: 0.861
Average recall: 0.866
Average fscore: 0.862
</pre>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.977778</td>
      <td>0.870968</td>
      <td>0.902439</td>
      <td>0.819444</td>
      <td>0.823529</td>
      <td>0.964706</td>
      <td>0.791209</td>
      <td>0.919540</td>
      <td>0.741935</td>
      <td>0.800000</td>
      <td>0.879518</td>
      <td>0.788732</td>
      <td>0.787879</td>
      <td>0.913043</td>
      <td>0.938272</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.936170</td>
      <td>0.915254</td>
      <td>0.973684</td>
      <td>0.670455</td>
      <td>0.767123</td>
      <td>0.964706</td>
      <td>0.774194</td>
      <td>0.851064</td>
      <td>0.884615</td>
      <td>0.727273</td>
      <td>0.912500</td>
      <td>0.903226</td>
      <td>0.912281</td>
      <td>0.851351</td>
      <td>0.950000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.956522</td>
      <td>0.892562</td>
      <td>0.936709</td>
      <td>0.737500</td>
      <td>0.794326</td>
      <td>0.964706</td>
      <td>0.782609</td>
      <td>0.883978</td>
      <td>0.807018</td>
      <td>0.761905</td>
      <td>0.895706</td>
      <td>0.842105</td>
      <td>0.845528</td>
      <td>0.881119</td>
      <td>0.944099</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
</div>
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<pre>
Activation: logistic
Standard scaling
Accuracy: 0.934
Average precision: 0.932
Average recall: 0.938
Average fscore: 0.934
</pre>
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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.968085</td>
      <td>0.949153</td>
      <td>0.937500</td>
      <td>0.916667</td>
      <td>0.887500</td>
      <td>0.976190</td>
      <td>0.945055</td>
      <td>0.965909</td>
      <td>0.888889</td>
      <td>0.918919</td>
      <td>0.961538</td>
      <td>0.885714</td>
      <td>0.873016</td>
      <td>0.934211</td>
      <td>0.975</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.968085</td>
      <td>0.949153</td>
      <td>0.986842</td>
      <td>0.750000</td>
      <td>0.972603</td>
      <td>0.964706</td>
      <td>0.924731</td>
      <td>0.904255</td>
      <td>0.923077</td>
      <td>0.883117</td>
      <td>0.937500</td>
      <td>1.000000</td>
      <td>0.964912</td>
      <td>0.959459</td>
      <td>0.975</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.968085</td>
      <td>0.949153</td>
      <td>0.961538</td>
      <td>0.825000</td>
      <td>0.928105</td>
      <td>0.970414</td>
      <td>0.934783</td>
      <td>0.934066</td>
      <td>0.905660</td>
      <td>0.900662</td>
      <td>0.949367</td>
      <td>0.939394</td>
      <td>0.916667</td>
      <td>0.946667</td>
      <td>0.975</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.879
Average precision: 0.878
Average recall: 0.880
Average fscore: 0.878
</pre>
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<style>
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    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.977778</td>
      <td>0.890625</td>
      <td>0.925926</td>
      <td>0.833333</td>
      <td>0.890625</td>
      <td>0.976190</td>
      <td>0.800000</td>
      <td>0.941860</td>
      <td>0.781609</td>
      <td>0.762500</td>
      <td>0.888889</td>
      <td>0.797101</td>
      <td>0.819672</td>
      <td>0.940299</td>
      <td>0.939759</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.936170</td>
      <td>0.966102</td>
      <td>0.986842</td>
      <td>0.738636</td>
      <td>0.780822</td>
      <td>0.964706</td>
      <td>0.817204</td>
      <td>0.861702</td>
      <td>0.871795</td>
      <td>0.792208</td>
      <td>0.900000</td>
      <td>0.887097</td>
      <td>0.877193</td>
      <td>0.851351</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.956522</td>
      <td>0.926829</td>
      <td>0.955414</td>
      <td>0.783133</td>
      <td>0.832117</td>
      <td>0.970414</td>
      <td>0.808511</td>
      <td>0.900000</td>
      <td>0.824242</td>
      <td>0.777070</td>
      <td>0.894410</td>
      <td>0.839695</td>
      <td>0.847458</td>
      <td>0.893617</td>
      <td>0.957055</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Activation: tanh
Standard scaling
Accuracy: 0.936
Average precision: 0.935
Average recall: 0.938
Average fscore: 0.936
</pre>
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    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.957895</td>
      <td>0.947368</td>
      <td>0.962025</td>
      <td>0.927711</td>
      <td>0.864198</td>
      <td>0.976190</td>
      <td>0.891304</td>
      <td>0.976190</td>
      <td>0.947368</td>
      <td>0.920000</td>
      <td>0.961039</td>
      <td>0.925373</td>
      <td>0.870968</td>
      <td>0.946667</td>
      <td>0.951807</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.968085</td>
      <td>0.915254</td>
      <td>1.000000</td>
      <td>0.875000</td>
      <td>0.958904</td>
      <td>0.964706</td>
      <td>0.881720</td>
      <td>0.872340</td>
      <td>0.923077</td>
      <td>0.896104</td>
      <td>0.925000</td>
      <td>1.000000</td>
      <td>0.947368</td>
      <td>0.959459</td>
      <td>0.987500</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.962963</td>
      <td>0.931034</td>
      <td>0.980645</td>
      <td>0.900585</td>
      <td>0.909091</td>
      <td>0.970414</td>
      <td>0.886486</td>
      <td>0.921348</td>
      <td>0.935065</td>
      <td>0.907895</td>
      <td>0.942675</td>
      <td>0.961240</td>
      <td>0.907563</td>
      <td>0.953020</td>
      <td>0.969325</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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<pre>
Minmax scaling
Accuracy: 0.874
Average precision: 0.870
Average recall: 0.876
Average fscore: 0.872
</pre>
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<style>
    .dataframe thead tr:only-child th {
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    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>grocery_store</th>
      <th>car</th>
      <th>city_center</th>
      <th>residential_area</th>
      <th>train</th>
      <th>forest_path</th>
      <th>cafe/restaurant</th>
      <th>home</th>
      <th>park</th>
      <th>tram</th>
      <th>bus</th>
      <th>library</th>
      <th>metro_station</th>
      <th>beach</th>
      <th>office</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>precision</th>
      <td>0.947368</td>
      <td>0.903226</td>
      <td>0.913580</td>
      <td>0.784810</td>
      <td>0.840580</td>
      <td>0.976190</td>
      <td>0.840909</td>
      <td>0.931034</td>
      <td>0.783133</td>
      <td>0.819444</td>
      <td>0.890244</td>
      <td>0.823529</td>
      <td>0.768116</td>
      <td>0.884058</td>
      <td>0.951220</td>
    </tr>
    <tr>
      <th>recall</th>
      <td>0.957447</td>
      <td>0.949153</td>
      <td>0.973684</td>
      <td>0.704545</td>
      <td>0.794521</td>
      <td>0.964706</td>
      <td>0.795699</td>
      <td>0.861702</td>
      <td>0.833333</td>
      <td>0.766234</td>
      <td>0.912500</td>
      <td>0.903226</td>
      <td>0.929825</td>
      <td>0.824324</td>
      <td>0.975000</td>
    </tr>
    <tr>
      <th>fscore</th>
      <td>0.952381</td>
      <td>0.925620</td>
      <td>0.942675</td>
      <td>0.742515</td>
      <td>0.816901</td>
      <td>0.970414</td>
      <td>0.817680</td>
      <td>0.895028</td>
      <td>0.807453</td>
      <td>0.791946</td>
      <td>0.901235</td>
      <td>0.861538</td>
      <td>0.841270</td>
      <td>0.853147</td>
      <td>0.962963</td>
    </tr>
    <tr>
      <th>support</th>
      <td>94.000000</td>
      <td>59.000000</td>
      <td>76.000000</td>
      <td>88.000000</td>
      <td>73.000000</td>
      <td>85.000000</td>
      <td>93.000000</td>
      <td>94.000000</td>
      <td>78.000000</td>
      <td>77.000000</td>
      <td>80.000000</td>
      <td>62.000000</td>
      <td>57.000000</td>
      <td>74.000000</td>
      <td>80.000000</td>
    </tr>
  </tbody>
</table>
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