LR_with_non-linear functions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To support both python 2 and python 3\n",
    "from __future__ import division, print_function, unicode_literals\n",
    "%config InlineBackend.figure_format = 'svg'  ###配置可以保存为矢量图\n",
    "%matplotlib inline \n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import axes3d\n",
    "import seaborn as sns\n",
    "import scipy as sp # scientific computation library\n",
    "# from sklearn.cross_validation import train_test_split\n",
    "from sklearn.model_selection import train_test_split #cross_validation\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1.1 第二范数正则化梯度下降求w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 1)\n",
      "(100, 1)\n",
      "(70, 1)\n",
      "(30, 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1edfbe9dbe0>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def f(x):\n",
    "    y=np.sin(x)\n",
    "    return y\n",
    "# X=np.arange(0,2*np.pi,0.2,dtype='float32')\n",
    "# sample_size=2*np.pi/0.2\n",
    "sample_size=100\n",
    "X=np.linspace(0,2*np.pi,sample_size)\n",
    "y=f(X)+np.random.normal(0,0.1,sample_size) \n",
    "y=np.atleast_2d(y).T\n",
    "print(y.shape)\n",
    "X = np.atleast_2d(X).T  #\n",
    "print(X.shape)\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=40)  \n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "plt.scatter(X_train,y_train, c='b')\n",
    "plt.scatter(X_test, y_test, c='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 2)\n",
      "(100, 3)\n",
      "(100, 4)\n",
      "(100, 5)\n",
      "(100, 6)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "#1到5次函数拟合,求出design matrix A\n",
    "# Amot=[]\n",
    "# for d in range(5):\n",
    "#     polyfeat = PolynomialFeatures(degree =d+1, include_bias = True)\n",
    "#     A_sklearn = polyfeat.fit_transform(X) \n",
    "#     print(A_sklearn.shape)\n",
    "#     Amot.append(A_sklearn)\n",
    "    \n",
    "polyfeat1 = PolynomialFeatures(degree =1, include_bias = True)\n",
    "A_sklearn1 = polyfeat1.fit_transform(X) \n",
    "print(A_sklearn1.shape)\n",
    "\n",
    "polyfeat2 = PolynomialFeatures(degree =2, include_bias = True)\n",
    "A_sklearn2 = polyfeat2.fit_transform(X) \n",
    "print(A_sklearn2.shape)\n",
    "\n",
    "polyfeat3 = PolynomialFeatures(degree =3, include_bias = True)\n",
    "A_sklearn3 = polyfeat3.fit_transform(X) \n",
    "print(A_sklearn3.shape)\n",
    "\n",
    "polyfeat4 = PolynomialFeatures(degree =4, include_bias = True)\n",
    "A_sklearn4 = polyfeat4.fit_transform(X) \n",
    "print(A_sklearn4.shape)\n",
    "\n",
    "polyfeat5 = PolynomialFeatures(degree =5, include_bias = True)\n",
    "A_sklearn5 = polyfeat5.fit_transform(X) \n",
    "print(A_sklearn5.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "in: Xmat 1*N\n",
    "out: X add a column of ones to N*2 matrix, N*3\n",
    "'''\n",
    "def designmat1(Xmat): # Xmat 2*N times, N:numbers of datasets\n",
    "#     print('Xmat',Xmat.shape) #50\n",
    "    #(1) assemble each input vector as a row \n",
    "    X = np.atleast_2d(Xmat).T  #to 2d array then transpose, N*2\n",
    "    #(2) add a column of ones to N*2 matrix\n",
    "    col1 = np.ones(np.shape(X)[0]).reshape(np.shape(X)[0],1) #the column vector which consists of n ones\n",
    "    # reshapte refers to ravel a array, this convert to N*1\n",
    "    X = np.concatenate((col1, X), axis=1) #Join a sequence of arrays:X along an existing axis.\n",
    "    n,p=X.shape\n",
    "    print('X',n,p)\n",
    "#     print('X=',X)\n",
    "    return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "梯度下降函数\n",
    "w:p*1(p为拟合次数)\n",
    "X:n*p(n为数据集个数),为design matrix\n",
    "y:n*1\n",
    "l2是lambda,正则化参数\n",
    "core是学习率=1, 0.000000006,  rate = 0.001\n",
    "初值w,rate,n_iterations=50都是由自己给定的\n",
    "'''\n",
    "def reg_gradientdescent(w, X, y,l2,core=1, rate = 0.001, n_iterations = 100):\n",
    "    wtseq = [w] #列表\n",
    "#     whistory = []\n",
    "    losshistory = [] \n",
    "    \n",
    "    for iteration in range(n_iterations):\n",
    "#         whistory.append(w)\n",
    "        w_without_t0=w[1:]  #把截距项去掉\n",
    "        meanrss = 0.5*np.square(y-X.dot(w)).mean()+0.5*l2*np.sum(np.square(w_without_t0)) #标量\n",
    "#         print(meanrss)\n",
    "        losshistory.append(meanrss)\n",
    "        w_with_t0_0=np.r_[np.zeros([1,1]),w[1:]]  #set w[0]=0,不考虑截距项\n",
    "#         gradients =  core*2/len(X) * X.T.dot(X.dot(w) - y) + \\\n",
    "#                      + 2 * l2 * w_with_t0_0\n",
    "#         gradients =  core*1/len(X) * X.T.dot(X.dot(w) - y) + \\\n",
    "#                      + 1 * l2 * w_with_t0_0\n",
    "        gradients =  core*2/len(X) * X.T.dot(X.dot(w) - y) + 2 * l2 * w\n",
    "        w = w - rate * gradients \n",
    "        \n",
    "#         if(iteration==0):\n",
    "#             print(w.shape)    #2*1\n",
    "        wtseq.append(w) \n",
    "#         if(iteration==0):\n",
    "#             print(wtseq)\n",
    "#     whist=np.asarray(whistory)\n",
    "    whist=np.asarray(wtseq)\n",
    "    meanlosstrace=np.asarray(losshistory)\n",
    "    return  w, whist, meanlosstrace\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def reg_gradientdescent(w, X, y,l2, core=1, rate = 0.1, n_iterations = 50):\n",
    "#     wtseq = [w]\n",
    "#     losshistory = [] \n",
    "#     for iteration in range(n_iterations):\n",
    "#         meanrss = np.square(y-X.dot(w)).mean()\n",
    "#         gradients =  core*2/len(X) * X.T.dot(X.dot(w) - y) + 2 * l2 * w\n",
    "#         w = w - rate * gradients\n",
    "#         wtseq.append(w)\n",
    "#         losshistory.append(meanrss)\n",
    "#     return w, np.array(wtseq),np.array(losshistory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def gradsqloss(Amat, y, wt):\n",
    "#     n, p = Amat.shape\n",
    "#     return (-2/n)*Amat.T.dot((y-Amat.dot(wt))+2 * l2 * wt)\n",
    "\n",
    "# def reg_gradientdescent(Amat, y, winit, rate, l2,numiter):\n",
    "#     n, p = Amat.shape\n",
    "#     whistory = []\n",
    "#     meanrsshistory = [] \n",
    "#     w = winit\n",
    "    \n",
    "#     for i in range(numiter): \n",
    "#         meanrss = np.square(y-Amat.dot(w)).mean()\n",
    "#         whistory.append(w)\n",
    "#         meanrsshistory.append(meanrss)\n",
    "#         grad = gradsqloss(Amat, y, w)\n",
    "#         w = w - rate*grad\n",
    "#     return w, np.asarray(whistory), np.asarray(meanrsshistory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2,)\n",
      "(2, 1)\n"
     ]
    }
   ],
   "source": [
    "# wt_init = np.array([[-0.5], [-1]])  ###2*1,不是一个一维数组\n",
    "n, p = A_sklearn1.shape\n",
    "wt_init = np.random.randn(p)\n",
    "print(wt_init.shape)\n",
    "wt_init=np.atleast_2d(wt_init).T\n",
    "print(wt_init.shape)\n",
    "# wt_init=[[-0.5],[-0.1]]\n",
    "numiter=50\n",
    "rate=0.001\n",
    "l2=0.0001\n",
    "# wfin, whist, meanlosstrace= reg_gradientdescent(A_sklearn1,y,wt_init,rate,l2,numiter)\n",
    "wfin, whist, meanlosstrace= reg_gradientdescent(wt_init,A_sklearn1,y,l2)\n",
    "# print(w_path)\n",
    "# print(w_path.shape) #51*2*1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2,)\n",
      "(2, 1)\n",
      "Initial weight: [[1.76405235]\n",
      " [0.40015721]]\n",
      "wfin= [[ 0.97736948]\n",
      " [-0.30658686]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 一次拟合\n",
    "# w0=(-2.5,2.5)+np.random.randn(2)\n",
    "# print(w0.shape)\n",
    "np.random.seed(0)\n",
    "n_iterations=50\n",
    "l2=0.0001\n",
    "n, p = A_sklearn1.shape\n",
    "wt_init = np.random.randn(p)\n",
    "print(wt_init.shape)\n",
    "wt_init=np.atleast_2d(wt_init).T\n",
    "print(wt_init.shape)\n",
    "# wt_init=[[0.3],[0.1]]\n",
    "print(\"Initial weight:\", wt_init)\n",
    "wfin, whist, meanlosstrace = reg_gradientdescent(wt_init, A_sklearn1, y, l2,n_iterations)\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2,figsize=(12,4)) #画一行两列\n",
    "ax[0].scatter(X,y,marker='o',color='k')\n",
    "print(\"wfin=\",wfin)\n",
    "ax[0].plot(X,wfin[0]+wfin[1]*X)  #拟合的曲线\n",
    "ax[1].plot(meanlosstrace)  #误差曲线\n",
    "ax[0].set_title(\"Linear fitting\")\n",
    "ax[0].set_xlabel(\"x\")\n",
    "ax[0].set_ylabel(\"y\")\n",
    "ax[1].set_title(\"Mean loss with iterations\")\n",
    "ax[1].set_xlabel(\"Number of iterations\")\n",
    "ax[1].set_ylabel(\"Mean of sum of squares of residual\")\n",
    "plt.savefig('fit1.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3,)\n",
      "Initial weight: [[1.76405235]\n",
      " [0.40015721]\n",
      " [0.97873798]]\n",
      "wfin= [[ 1.54332174]\n",
      " [ 0.14952383]\n",
      " [-0.11133115]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1edf9d334e0>]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 二次拟合\n",
    "np.random.seed(0)\n",
    "n, p = A_sklearn2.shape\n",
    "wt_init = np.random.randn(p)\n",
    "print(wt_init.shape)\n",
    "wt_init=np.atleast_2d(wt_init).T  #p*1\n",
    "# wt_init=[[0.3],[0.1],[0.1]]\n",
    "print(\"Initial weight:\", wt_init)\n",
    "core=1\n",
    "l2=5\n",
    "rate=10**(-5)\n",
    "n_iterations=1000\n",
    "wfin, whist, meanlosstrace = reg_gradientdescent(wt_init, A_sklearn2, y,l2,core,rate,n_iterations)\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2,figsize=(12,4)) #画一行两列\n",
    "ax[0].scatter(X,y,marker='o',color='k')\n",
    "# y_predict=\n",
    "print(\"wfin=\",wfin)\n",
    "y_pridict= A_sklearn2.dot(wfin)\n",
    "ax[0].plot(X,y_pridict)  #拟合的曲线\n",
    "ax[0].set_title(\"Quadric Fit\")\n",
    "ax[1].plot(meanlosstrace)  #误差曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4,)\n",
      "(4, 1)\n",
      "Initial weight: [[-0.05125456]\n",
      " [ 1.63637689]\n",
      " [-0.78471995]\n",
      " [ 0.08447873]]\n",
      "wfin= [[-0.05088581]\n",
      " [ 1.62508532]\n",
      " [-0.77931446]\n",
      " [ 0.08387402]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1edfa966e10>]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#三次拟合\n",
    "np.random.seed(0)\n",
    "n, p = A_sklearn3.shape\n",
    "wt_init = np.random.randn(p)\n",
    "print(wt_init.shape)\n",
    "wt_init=np.atleast_2d(wt_init).T\n",
    "print(wt_init.shape)\n",
    "# wt_init=[[0.3],[0.1],[0.1],[0.1]]\n",
    "wt_init=np.array([[-0.05125456],[ 1.63637689],[-0.78471995],[ 0.08447873]])\n",
    "print(\"Initial weight:\", wt_init)\n",
    "core=1\n",
    "l2=0.35 #0.0005 #20\n",
    "rate=10**(-5)\n",
    "n_iterations=1000\n",
    "wfin, whist, meanlosstrace = reg_gradientdescent(wt_init, A_sklearn3, y, l2,core,rate,n_iterations)\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2,figsize=(12,4)) #画一行两列\n",
    "ax[0].scatter(X,y,marker='o',color='k')\n",
    "# y_predict=\n",
    "print(\"wfin=\",wfin)\n",
    "y_pridict= A_sklearn3.dot(wfin)\n",
    "ax[0].plot(X,y_pridict)  #拟合的曲线\n",
    "ax[1].plot(meanlosstrace)  #误差曲线\n",
    "plg.sav"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5,)\n",
      "(5, 1)\n",
      "Initial weight: [[ 0.03268925]\n",
      " [ 1.30413531]\n",
      " [-0.52649501]\n",
      " [ 0.01777361]\n",
      " [ 0.00544374]]\n",
      "wfin= [[ 0.03268948]\n",
      " [ 1.30414441]\n",
      " [-0.52649868]\n",
      " [ 0.01777375]\n",
      " [ 0.00544378]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1edfbc2e2e8>]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#四次拟合\n",
    "np.random.seed(0)\n",
    "n, p = A_sklearn4.shape\n",
    "wt_init = np.random.randn(p)\n",
    "print(wt_init.shape)\n",
    "wt_init=np.atleast_2d(wt_init).T\n",
    "print(wt_init.shape)\n",
    "wt_init=np.array([[ 0.03268925],[ 1.30413531],[-0.52649501],[ 0.01777361],[ 0.00544374]])\n",
    "print(\"Initial weight:\", wt_init)\n",
    "core=1\n",
    "l2=10**(-5)\n",
    "rate=10**(-6)\n",
    "n_iterations=1000\n",
    "wfin, whist, meanlosstrace = reg_gradientdescent(wt_init, A_sklearn4, y, l2,core,rate,n_iterations)\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2,figsize=(12,4)) #画一行两列\n",
    "ax[0].scatter(X,y,marker='o',color='k')\n",
    "# y_predict=\n",
    "print(\"wfin=\",wfin)\n",
    "y_pridict= A_sklearn4.dot(wfin)\n",
    "ax[0].plot(X,y_pridict)  #拟合的曲线\n",
    "ax[1].plot(meanlosstrace)  #误差曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6,)\n",
      "(6, 1)\n",
      "Initial weight: [[ 0.06609572]\n",
      " [ 0.76199846]\n",
      " [ 0.30174765]\n",
      " [-0.39249996]\n",
      " [ 0.08598679]\n",
      " [-0.00544834]]\n",
      "wfin= [[ 0.06609577]\n",
      " [ 0.76199899]\n",
      " [ 0.30174786]\n",
      " [-0.39250023]\n",
      " [ 0.08598685]\n",
      " [-0.00544834]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1edfbc35160>]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#五次拟合\n",
    "np.random.seed(0)\n",
    "n, p = A_sklearn5.shape\n",
    "wt_init = np.random.randn(p)\n",
    "print(wt_init.shape)\n",
    "wt_init=np.atleast_2d(wt_init).T\n",
    "print(wt_init.shape)\n",
    "wt_init=np.array([[ 0.06609572],[ 0.76199846],[ 0.30174765],[-0.39249996],[ 0.08598679],[-0.00544834]])\n",
    "print(\"Initial weight:\", wt_init)\n",
    "core=1\n",
    "l2=10**(-5)\n",
    "rate=10**(-7)\n",
    "n_iterations=1000\n",
    "wfin, whist, meanlosstrace = reg_gradientdescent(wt_init, A_sklearn5, y, l2,core,rate,n_iterations)\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2,figsize=(12,4)) #画一行两列\n",
    "ax[0].scatter(X,y,marker='o',color='k')\n",
    "# y_predict=\n",
    "print(\"wfin=\",wfin)\n",
    "y_pridict= A_sklearn5.dot(wfin)\n",
    "ax[0].plot(X,y_pridict)  #拟合的曲线\n",
    "ax[1].plot(meanlosstrace)  #误差曲线"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1.2 Analytical expression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ridgeRegres(xMat,yMat,lam=0.2):\n",
    "#     print(\"xMat:\",xMat.shape)\n",
    "    xTx = np.dot(xMat.T,xMat)\n",
    "    denom = xTx + np.eye(np.shape(xMat)[1])*lam\n",
    "    if np.linalg.det(denom) == 0.0:\n",
    "        print(\"This matrix is singular, cannot do inverse\")\n",
    "        return\n",
    "    ws = np.dot(np.linalg.inv(denom), np.dot(xMat.T,yMat))  #np.linalg.pinv 求广义逆\n",
    "    return ws\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ridgeRegres2(X,lam):\n",
    "    U, S, V = np.linalg.svd(X.T.dot(X) + lam * np.identity(np.shape(X)[1]))\n",
    "    S = np.diag(S)\n",
    "    X_sq_reg_inv = V.dot(np.linalg.pinv(S)).dot(U.T)\n",
    "    w = X_sq_reg_inv.dot(X.T).dot(y)\n",
    "    return w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.04017951]\n",
      " [ 1.10755172]\n",
      " [ 0.00897911]\n",
      " [-0.29113982]\n",
      " [ 0.07049721]\n",
      " [-0.00458029]]\n"
     ]
    }
   ],
   "source": [
    "wfin=ridgeRegres(A_sklearn5,y,lam=10**(-5))\n",
    "print(wfin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wfin= [[ 0.06609572]\n",
      " [ 0.76199846]\n",
      " [ 0.30174765]\n",
      " [-0.39249996]\n",
      " [ 0.08598679]\n",
      " [-0.00544834]]\n",
      "wfin= [[ 0.03268925]\n",
      " [ 1.30413531]\n",
      " [-0.52649501]\n",
      " [ 0.01777361]\n",
      " [ 0.00544374]]\n",
      "wfin= [[-0.05125456]\n",
      " [ 1.63637689]\n",
      " [-0.78471995]\n",
      " [ 0.08447873]]\n",
      "wfin= [[ 0.89707093]\n",
      " [-0.30276498]\n",
      " [ 0.00403395]]\n",
      "wfin= [[ 0.87123567]\n",
      " [-0.27752985]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1edfa4dd978>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#####同一个lambda, 不同的次数拟合, 外面再加一个大循环用lambda\n",
    "# fig, ax = plt.subplots(nrows=1, ncols=2,figsize=(12,4)) #画一行两列\n",
    "# lam=3  #10**(-5)\n",
    "lam=0.35\n",
    "wfin=ridgeRegres(A_sklearn5,y,lam)\n",
    "plt.scatter(X,y,marker='o',color='k')\n",
    "print(\"wfin=\",wfin)\n",
    "y_pridict= A_sklearn5.dot(wfin)\n",
    "plt.plot(X,y_pridict,label=5)  #拟合的曲线\n",
    "\n",
    "wfin=ridgeRegres(A_sklearn4,y,lam)\n",
    "plt.scatter(X,y,marker='o',color='k')\n",
    "print(\"wfin=\",wfin)\n",
    "y_pridict= A_sklearn4.dot(wfin)\n",
    "plt.plot(X,y_pridict,label=4)  #拟合的曲线\n",
    "\n",
    "wfin=ridgeRegres(A_sklearn3,y,lam)\n",
    "plt.scatter(X,y,marker='o',color='k')\n",
    "print(\"wfin=\",wfin)\n",
    "y_pridict= A_sklearn3.dot(wfin)\n",
    "plt.plot(X,y_pridict,label=3)  #拟合的曲线\n",
    "\n",
    "wfin=ridgeRegres(A_sklearn2,y,lam)\n",
    "plt.scatter(X,y,marker='o',color='k')\n",
    "print(\"wfin=\",wfin)\n",
    "y_pridict= A_sklearn2.dot(wfin)\n",
    "plt.plot(X,y_pridict,label=2)  #拟合的曲线\n",
    "\n",
    "wfin=ridgeRegres(A_sklearn1,y,lam)\n",
    "plt.scatter(X,y,marker='o',color='k')\n",
    "print(\"wfin=\",wfin)\n",
    "y_pridict= A_sklearn1.dot(wfin)\n",
    "plt.plot(X,y_pridict,label=1)  #拟合的曲线\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### 同一次数拟合, 不同的lambda"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1.3 Plot the measure of the mean of the squared residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.81890774]\n",
      " [-0.24489484]]\n",
      "[[ 0.87977343]\n",
      " [-0.3030341 ]\n",
      " [ 0.00908676]]\n",
      "[[-0.00687639]\n",
      " [ 1.55070029]\n",
      " [-0.75373584]\n",
      " [ 0.08138993]]\n",
      "[[ 7.94878682e-02]\n",
      " [ 1.17071860e+00]\n",
      " [-4.44243077e-01]\n",
      " [-1.08776151e-03]\n",
      " [ 6.86138646e-03]]\n",
      "[[ 0.07983499]\n",
      " [ 0.72397695]\n",
      " [ 0.30532156]\n",
      " [-0.38352735]\n",
      " [ 0.08292332]\n",
      " [-0.00517737]]\n"
     ]
    }
   ],
   "source": [
    "# 同一lambda,不同次数多项式的拟合,train集得到拟合系数,design matrix由train集得到,test集测试\n",
    "# lamb=3\n",
    "lamb=0.35\n",
    "polyfeat1 = PolynomialFeatures(degree =1, include_bias = True)\n",
    "A_train_sklearn1 = polyfeat1.fit_transform(X_train) \n",
    "wtrain1=ridgeRegres(A_train_sklearn1,y_train,lamb)\n",
    "print(wtrain1)\n",
    "\n",
    "polyfeat2 = PolynomialFeatures(degree =2, include_bias = True)\n",
    "A_train_sklearn2 = polyfeat2.fit_transform(X_train) \n",
    "wtrain2=ridgeRegres(A_train_sklearn2,y_train,lamb)\n",
    "print(wtrain2)\n",
    "\n",
    "polyfeat3 = PolynomialFeatures(degree =3, include_bias = True)\n",
    "A_train_sklearn3 = polyfeat3.fit_transform(X_train) \n",
    "wtrain3=ridgeRegres(A_train_sklearn3,y_train,lamb)\n",
    "print(wtrain3)\n",
    "\n",
    "polyfeat4 = PolynomialFeatures(degree =4, include_bias = True)\n",
    "A_train_sklearn4 = polyfeat4.fit_transform(X_train) \n",
    "wtrain4=ridgeRegres(A_train_sklearn4,y_train,lamb)\n",
    "print(wtrain4)\n",
    "\n",
    "polyfeat5 = PolynomialFeatures(degree =5, include_bias = True)\n",
    "A_train_sklearn5 = polyfeat5.fit_transform(X_train) \n",
    "wtrain5=ridgeRegres(A_train_sklearn5,y_train,lamb)\n",
    "print(wtrain5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    " Amat:n*p\n",
    " wfin:p*1\n",
    " '''\n",
    "def fun_y_pridict(Amat,wfin):\n",
    "    y_predict=Amat.dot(wfin)\n",
    "    return y_predict\n",
    "\n",
    "'''\n",
    " Amat:n*p\n",
    " wfin:p*1\n",
    " y:n*1,true value\n",
    " lamb:scalar\n",
    " '''\n",
    "def fun_loss(w,Amat,y,lamb=10**(-5)):\n",
    "     w_without_t0=w[1:]  #把截距项去掉\n",
    "     meanrss= 0.5*np.square(y-Amat.dot(w)).mean()+0.5*lamb*np.sum(np.square(w_without_t0)) #标量\n",
    "     meanrss2=0.5*mean_squared_error(Amat.dot(w),y)+0.5*lamb*np.sum(np.square(w_without_t0))\n",
    "#      print(\"meanrss=\",meanrss,meanrss2)  两者完全相等\n",
    "     meanrss3=0.5*np.square(y-Amat.dot(w)).mean()+0.5*lamb*np.sum(np.square(w))\n",
    "     return meanrss3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.12095755943410148\n",
      "0.12092644764363479\n",
      "0.006127119330809584\n",
      "0.005833035777402429\n",
      "0.003272272341850821\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.12099043568614502\n",
      "0.120965413752276\n",
      "0.006261195063330397\n",
      "0.005903879301742812\n",
      "0.003307270363846237\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.12131919820658053\n",
      "0.12135507483868814\n",
      "0.007601952388538527\n",
      "0.006612314545146639\n",
      "0.0036572505838003942\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.12460682341093555\n",
      "0.1252516857028096\n",
      "0.021009525640619822\n",
      "0.01369666697918491\n",
      "0.0071570527833419665\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.15748307545448578\n",
      "0.164217794344024\n",
      "0.1550852581614328\n",
      "0.08454019131956762\n",
      "0.042155074778757684\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.48624559588998806\n",
      "0.5538788807561681\n",
      "1.4958425833695623\n",
      "0.7929754347233947\n",
      "0.39213529473291486\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.216828974635549\n",
      "1.4197924061164884\n",
      "4.475303306054295\n",
      "2.3672759756207884\n",
      "1.1698691168532642\n"
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#由test集得到design matrix,用于在test集上预测\n",
    "#同一个lambda, 不同的次数\n",
    "lamb_array=[10**(-5),10**(-4),10**(-3),10**(-2),10**(-1),1,3]\n",
    "for j in range(len(lamb_array)):\n",
    "    polyfeat1 = PolynomialFeatures(degree =1, include_bias = True)\n",
    "    Atext1 = polyfeat1.fit_transform(X_test) \n",
    "\n",
    "    polyfeat2 = PolynomialFeatures(degree =2, include_bias = True)\n",
    "    Atext2 = polyfeat2.fit_transform(X_test) \n",
    "\n",
    "    polyfeat3 = PolynomialFeatures(degree =3, include_bias = True)\n",
    "    Atext3 = polyfeat3.fit_transform(X_test) \n",
    "\n",
    "    polyfeat4 = PolynomialFeatures(degree =4, include_bias = True)\n",
    "    Atext4 = polyfeat4.fit_transform(X_test) \n",
    "\n",
    "    polyfeat5 = PolynomialFeatures(degree =5, include_bias = True)\n",
    "    Atext5 = polyfeat5.fit_transform(X_test) \n",
    "\n",
    "    # lamb=3\n",
    "    lamb=10**(-5)\n",
    "    meanloss1=fun_loss(wtrain1, Atext1, y_test,lamb_array[j])\n",
    "    meanloss2=fun_loss(wtrain2, Atext2, y_test,lamb_array[j])\n",
    "    meanloss3=fun_loss(wtrain3, Atext3, y_test,lamb_array[j])\n",
    "    meanloss4=fun_loss(wtrain4, Atext4, y_test,lamb_array[j])\n",
    "    meanloss5=fun_loss(wtrain5, Atext5, y_test,lamb_array[j])\n",
    "\n",
    "    meanloss_list=[]\n",
    "    meanloss_list.append(meanloss1)\n",
    "    meanloss_list.append(meanloss2)\n",
    "    meanloss_list.append(meanloss3)\n",
    "    meanloss_list.append(meanloss4)\n",
    "    meanloss_list.append(meanloss5)\n",
    "    meanloss_array= np.asarray(meanloss_list)\n",
    "\n",
    "    # y_predict1=fun_y_pridict(Atext1,wtrain1)\n",
    "    # sns.residplot(X_test,y_predict1,order=1,scatter_kws={'color':'b'},label=\"1\")\n",
    "\n",
    "    # y_predict2=fun_y_pridict(Atext2,wtrain2)\n",
    "    # sns.residplot(X_test,y_predict2,order=1,label=\"2\")\n",
    "\n",
    "    # y_predict3=fun_y_pridict(Atext3,wtrain3)\n",
    "    # sns.residplot(X_test,y_predict3,order=1,label=\"3\")\n",
    "\n",
    "    # y_predict4=fun_y_pridict(Atext4,wtrain4)\n",
    "    # sns.residplot(X_test,y_predict4,order=1,label=\"4\")\n",
    "\n",
    "    # y_predict5=fun_y_pridict(Atext5,wtrain5)\n",
    "    # sns.residplot(X_test,y_predict5,order=1,label=\"5\")\n",
    "\n",
    "\n",
    "    # sns.residplot(X_test,y_test,order=1,scatter_kws={'color':'b'},label=\"1\")\n",
    "\n",
    "    # sns.residplot(X_test,y_test,order=2,label=\"2\")  #多次拟合不行\n",
    "\n",
    "    # sns.residplot(X_test,y_test,order=3,label=\"3\")\n",
    "\n",
    "    # sns.residplot(X_test,y_test,order=4,label=\"4\")\n",
    "\n",
    "    # sns.residplot(X_test,y_test,order=5,label=\"5\")\n",
    "\n",
    "    # title=\"the residuals distribution for different degrees(1-5),lambda=0.35\"\n",
    "    # plt.xlabel('x')  \n",
    "    # plt.ylabel('residuals') \n",
    "    # plt.title(title)\n",
    "    # plt.legend()\n",
    "\n",
    "    fig,ax=plt.subplots(1,1)\n",
    "    # ax.set_xlim([1,5])\n",
    "    for i in range(5):\n",
    "        print(meanloss_array[i])\n",
    "        ax.scatter(i+1,meanloss_array[i])\n",
    "        title=\"mean of the squared residuals for different degrees(1-5),lambda=\"+str(lamb_array[j])\n",
    "        ax.set_title(title)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0052619259024158475, 0.005440627726773646, 0.007225950042929162, 0.0249111249732203, 0.186382954263467, 1.0250318330482773, 1.70514670459256]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 同一次数多项式的拟合,对应于不同lambda,train集得到拟合系数,test集测试\n",
    "d=3\n",
    "polyfeat = PolynomialFeatures(degree =3, include_bias = True)\n",
    "Atext = polyfeat.fit_transform(X_test) \n",
    "lamb_array=[10**(-5),10**(-4),10**(-3),10**(-2),10**(-1),1,3]\n",
    "# lamb_array=np.arange(0.1,1,0.1,dtype='float32')\n",
    "# lamb_array=[10**(-5),10**(-3),1,3,10]\n",
    "# print(len(lamb_array))\n",
    "wfin_list=[]\n",
    "ypredict_list=[]\n",
    "meanloss_list=[]\n",
    "for i in range(len(lamb_array)):\n",
    "    wfin=ridgeRegres(A_train_sklearn3,y_train,lamb_array[i]) #p*1(p=6)\n",
    "    wfin_list.append(wfin)\n",
    "    y_predict=fun_y_pridict(Atext,wfin)  #n*1(n=30)\n",
    "    meanloss=fun_loss(wfin, Atext, y_test,lamb_array[i])\n",
    "    meanloss_list.append(meanloss)\n",
    "#   sns.residplot(X_test,y_test,order=i+1,label=\"lambda\"+str(i+1))\n",
    "    \n",
    "#     print(y_predict.shape)\n",
    "#     ypredict_list.append(y_predict)\n",
    "# wfin_array=np.asarray(wfin_list)\n",
    "# ypredict_array=np.asarray(ypredict_list)\n",
    "# print(ypredict_array.shape)\n",
    "\n",
    "# title=\"the residuals distribution for different lambda,degree=3\"\n",
    "# plt.xlabel('x')  \n",
    "# plt.ylabel('residuals') \n",
    "# plt.title(title)\n",
    "# plt.legend()\n",
    "\n",
    "# meanloss_array=np.asarray(meanloss_list)\n",
    "print(meanloss_list)\n",
    "fig,ax=plt.subplots(1,1)\n",
    "# ax.set_xlim([1,5])\n",
    "for i in range(len(lamb_array)):\n",
    "    ax.scatter(i+1, meanloss_list[i],label=\"lambda\"+str(lamb_array[i]))#\"lambda\"+str(i+1)\n",
    "title=\"mean of the squared residuals for different lambda,degree=\"+str(d)\n",
    "ax.set_title(title)\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 Linear regression generalise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(90, 1)\n",
      "(10, 1)\n",
      "[[5.26772102]\n",
      " [3.36372547]\n",
      " [4.44265628]\n",
      " [2.85599332]\n",
      " [2.7925268 ]\n",
      " [2.47519421]\n",
      " [1.3962634 ]\n",
      " [5.07732146]\n",
      " [0.63466518]\n",
      " [0.        ]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1edfd5a68d0>"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train_one,X_test_one,y_train_one,y_test_one=train_test_split(X,y,test_size=0.1,random_state=42)  \n",
    "print(X_train_one.shape)\n",
    "print(X_test_one.shape)\n",
    "print(X_test_one)\n",
    "plt.scatter(X_train_one,y_train_one, c='b')\n",
    "plt.scatter(X_test_one,y_test_one, c='r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LeaveOneOut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import LeaveOneOut\n",
    "\n",
    "loo = LeaveOneOut()\n",
    "i=0\n",
    "d=3\n",
    "meanloss_list=[]\n",
    "meanloss_mean_list=[]\n",
    "meanloss_sum=0\n",
    "for j in range(len(lamb_array)):\n",
    "    i=0\n",
    "    meanloss_sum=0\n",
    "    meanloss_list=[]\n",
    "    for train_index, test_index in loo.split(X):  #将样本分成100等分,留1份作为测试,一共重复100次\n",
    "#     print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n",
    "        X_train1, X_test1 = X[train_index], X[test_index]\n",
    "        y_train1, y_test1 = y[train_index], y[test_index]\n",
    "        i+=1\n",
    "#     print(X_train, X_test, y_train, y_test)\n",
    "#     print(X_train1.shape, X_test1.shape, y_train1.shape, y_test1.shape)\n",
    "        polyfit = PolynomialFeatures(degree =d, include_bias = True)\n",
    "        Amat = polyfit.fit_transform(X_train1) \n",
    "        Amat_test=polyfit.fit_transform(X_test1) \n",
    "        wfin=ridgeRegres(Amat,y_train1,lamb_array[j]) #p*1\n",
    "#     wfin_list.append(wfin)\n",
    "        y_predict=fun_y_pridict(Amat_test,wfin)  #n*1\n",
    "        meanloss=fun_loss(wfin, Amat_test, y_test1,lamb_array[j])\n",
    "        meanloss_list.append(meanloss)\n",
    "#         print(\"meanloss=\"+str(meanloss))\n",
    "#         meanloss_sum+=meanloss\n",
    "#     meanloss_sum_mean=meanloss_sum/i\n",
    "    meanloss_sum_mean=np.mean(meanloss_list)  #求得对于每个lamb每个留一验证的均值\n",
    "#     print(\"meanloss_sum_mean=\"+str(meanloss_sum_mean))\n",
    "    meanloss_mean_list.append(meanloss_sum_mean)  #记录对于每个lamb的meanloss的均值\n",
    "#     print(i) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.008225227504279516, 0.008405805605355232, 0.01021036314972987, 0.028134383748531146, 0.19596650118500242, 1.1975059080877573, 2.0732843020328184]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# meanloss_array=np.asarray(meanloss_list)\n",
    "fig,ax=plt.subplots(1,1)\n",
    "# ax.set_xlim([1,5])\n",
    "print(meanloss_mean_list)\n",
    "for i in range(len(lamb_array)):\n",
    "    ax.scatter(i+1, meanloss_mean_list[i],label=\"lambda=\"+str(lamb_array[i]))\n",
    "title=\"mean of the squared residuals for different lambda,degree=\"+str(d)\n",
    "ax.set_title(title)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 3] [0 1]\n",
      "[0 1] [2 3]\n",
      "[0 2] [1 3]\n",
      "[1 3] [0 2]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RepeatedKFold\n",
    "X1 = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])\n",
    "random_state = 12883823\n",
    "rkf = RepeatedKFold(n_splits=2, n_repeats=2, random_state=random_state)\n",
    "for train, test in rkf.split(X1):\n",
    "    print(\"%s %s\" % (train, test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10 Fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RepeatedKFold\n",
    "random_state = 12883823\n",
    "rkf = RepeatedKFold(n_splits=10, n_repeats=1, random_state=random_state)\n",
    "i=0\n",
    "d=3\n",
    "meanloss_list=[]\n",
    "meanloss_mean_list=[]\n",
    "meanloss_sum=0\n",
    "for j in range(len(lamb_array)):\n",
    "    i=0\n",
    "    meanloss_sum=0\n",
    "    meanloss_list=[]\n",
    "    for train_index, test_index in rkf.split(X):\n",
    "#         print(train_index)\n",
    "        X_train1, X_test1 = X[train_index], X[test_index]\n",
    "        y_train1, y_test1 = y[train_index], y[test_index]\n",
    "        i+=1\n",
    "#     print(X_train, X_test, y_train, y_test)\n",
    "#         print(X_train1.shape, X_test1.shape, y_train1.shape, y_test1.shape)\n",
    "        polyfit = PolynomialFeatures(degree =d, include_bias = True)\n",
    "        Amat = polyfit.fit_transform(X_train1) \n",
    "        Amat_test=polyfit.fit_transform(X_test1) \n",
    "        wfin=ridgeRegres(Amat,y_train1,lamb_array[j]) #p*1\n",
    "#     wfin_list.append(wfin)\n",
    "        y_predict=fun_y_pridict(Amat_test,wfin)  #n*1\n",
    "        meanloss=fun_loss(wfin, Amat_test, y_test1,lamb_array[j])\n",
    "        meanloss_list.append(meanloss)\n",
    "#         print(\"meanloss=\"+str(meanloss))\n",
    "#         meanloss_sum+=meanloss\n",
    "#     meanloss_sum_mean=meanloss_sum/i\n",
    "    meanloss_sum_mean=np.mean(meanloss_list)  #求得对于每个lamb每个验证的均值\n",
    "#     print(\"meanloss_sum_mean=\"+str(meanloss_sum_mean))\n",
    "    meanloss_mean_list.append(meanloss_sum_mean)  #记录对于每个lamb的meanloss的均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.008351179815822148, 0.00853211329538001, 0.010340088156860126, 0.02828482498303022, 0.1951512493315707, 1.151597968928105, 1.9634998712575693]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(1,1)\n",
    "# ax.set_xlim([1,5])\n",
    "print(meanloss_mean_list)\n",
    "for i in range(len(lamb_array)):\n",
    "    ax.scatter(i+1, meanloss_mean_list[i],label=\"lambda=\"+str(lamb_array[i]))\n",
    "title=\"mean of the squared residuals for different lambda,degree=\"+str(d)\n",
    "ax.set_title(title)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.007053160226309376,\n",
       " 0.007233869726496547,\n",
       " 0.009039259932614192,\n",
       " 0.0269241245068834,\n",
       " 0.19020445084153323,\n",
       " 1.0005595341011517,\n",
       " 1.4322379170339086]"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[0.007053160226309376, 0.007233869726496547, 0.009039259932614192, 0.0269241245068834, 0.19020445084153323, 1.0005595341011517, 1.4322379170339086]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}