Example_trans_prob_distr.py

import numpy as np

example_A = np.array([[0.6, 0.4], [0.4, 0.6]])   # 2 dimensional array
example_B = np.array([[1., 0.], [0.3, 0.7]])   # 2 dimensional array
example_C = np.array(   # 3 x 3
    [
        [0.7,  0.0,  0.3 ],
        [0.21, 0.68, 0.11],
        [0.0,  0.2,  0.8 ]
    ]
)
example_D = np.array(   # 10 x 10
    [
        [0.315395, 0.077240, 0.051185, 0.027917, 0.071391, 0.024412, 0.068670, 0.130996, 0.226410, 0.006384],
        [0.005368, 0.343257, 0.327585, 0.019219, 0.047062, 0.099252, 0.052346, 0.006597, 0.091136, 0.008178],
        [0.120688, 0.079572, 0.231081, 0.222689, 0.051919, 0.100178, 0.006764, 0.035329, 0.140787, 0.010993],
        [0.021886, 0.006375, 0.099946, 0.367267, 0.040180, 0.087184, 0.089100, 0.141977, 0.059686, 0.086399],
        [0.111740, 0.047757, 0.102825, 0.017564, 0.188194, 0.109210, 0.126799, 0.116645, 0.150732, 0.028534],
        [0.072345, 0.051594, 0.186664, 0.024739, 0.029590, 0.326199, 0.042372, 0.020928, 0.012936, 0.232633],
        [0.141550, 0.099750, 0.056263, 0.036549, 0.205336, 0.091534, 0.206593, 0.011088, 0.073229, 0.078108],
        [0.022030, 0.124302, 0.024641, 0.138589, 0.132246, 0.022811, 0.141506, 0.340417, 0.046691, 0.006767],
        [0.041055, 0.075495, 0.063362, 0.027271, 0.034941, 0.131530, 0.186870, 0.052422, 0.340712, 0.046342],
        [0.021277, 0.047517, 0.113352, 0.069870, 0.062543, 0.155779, 0.188612, 0.070641, 0.047718, 0.222691],
    ]
)
example_E = np.array(   # 7 x 14
    [
        [0.192924, 0.089701, 0.021833, 0.152806, 0.011611, 0.086720, 0.165786, 0.015761, 0.109188, 0.015682, 0.025470, 0.073875, 0.035944, 0.002699],
        [0.014871, 0.216260, 0.005303, 0.119562, 0.118172, 0.133585, 0.157966, 0.055477, 0.053210, 0.011355, 0.062284, 0.031863, 0.013020, 0.007072],
        [0.112542, 0.160423, 0.236331, 0.165273, 0.004366, 0.013188, 0.002696, 0.033401, 0.005471, 0.068635, 0.047322, 0.036100, 0.049478, 0.064774],
        [0.015249, 0.090436, 0.031359, 0.210700, 0.074665, 0.018877, 0.040756, 0.017114, 0.109960, 0.024227, 0.171975, 0.112956, 0.050542, 0.031184],
        [0.027588, 0.047386, 0.108748, 0.053770, 0.197346, 0.011192, 0.087548, 0.013377, 0.081362, 0.006143, 0.116144, 0.139228, 0.083607, 0.026561],
        [0.028725, 0.021479, 0.187318, 0.012265, 0.045138, 0.275349, 0.000883, 0.005216, 0.004482, 0.086729, 0.058492, 0.107850, 0.044141, 0.121933],
        [0.031631, 0.049566, 0.058419, 0.181859, 0.114117, 0.020115, 0.203303, 0.102987, 0.100286, 0.043775, 0.009645, 0.062433, 0.013086, 0.008778],
    ]
)