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# Необходмые команды импорта.
import sys
sys.path.append('../physlearn/')
sys.path.append('../source')
import numpy as np
from numpy import linalg as LA
import tensorflow as tf
from matplotlib import pylab as plt
from IPython.display import clear_output
import math_util
%matplotlib inline

# Model Parameters
m = 4500 # размер сеток обучения
M = 4 # количество выходных нейронов(базисных функций)
a = -45
b = 45
x_grid = np.linspace(a, b, m, endpoint=True)#.reshape(1, m)

sess = tf.Session()
x = tf.placeholder(tf.double)
trial_func = [tf.sin(x), tf.cos(x), tf.sin(2*x), tf.cos(5*x)]
y_set = [1*tf.sin(x), 0*tf.cos(x), 2.5*tf.sigmoid(2*x), -3.1*tf.cos(5*x)]
sess.run(tf.initializers.global_variables(), {x:x_grid})


    

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A = tf.transpose(trial_func)
A_T = trial_func

y_0 = tf.reduce_sum(input_tensor=y_set, axis=0)
y = tf.expand_dims(y_0, -1)
omega = tf.matmul(tf.matmul(tf.matrix_inverse(tf.matmul(A_T, A)), A_T), y)
regression_fit = tf.matmul(tf.transpose(trial_func), omega)
noninvariance_factor = (1 / m) * tf.reduce_sum(tf.square(y - regression_fit))


    

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def plot_all(x_in):
    func_set_matrix = sess.run(trial_func, {x:x_in})
    for i in range(M):
        plt.plot(x_grid, func_set_matrix[i])
    plt.plot(x_in, sess.run(y_0, {x:x_in}))


    

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np.array(sess.run(y, {x:x_grid})).shape


    

    
Out[17]:





(1500, 1)

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plot_all(x_grid)


    

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regression_res = sess.run(omega, {x:x_grid})


    

In [20]:

    

regression_res


    

    
Out[20]:





array([[ 1.23357094e+00],
       [ 1.10717721e-01],
       [ 2.64177864e-03],
       [-3.11185372e+00]])

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