使用tensorflow实现矩阵分解方式

> import matplotlib.pyplot as plt
> import numpy as np

> import tensorflow as tf

> from tensorflow.python.framework import ops
> ops.reset_default_graph()

> sess=tf.Session()

> x_vals=np.linspace(0,10,100)

> y_vals=x_vals+np.random.normal(0,1,100)

> x_vals_column=np.transpose(np.matrix(x_vals))
> ones_column=np.transpose(np.matrix(np.repeat(1,100)))
> A=np.column_stack((x_vals_column,ones_column))
> b=np.transpose(np.matrix(y_vals))
> A_tensor=tf.constant(A)

> b_tensor=tf.constant(b)

> tA_A=tf.matmul(tf.transpose(A_tensor),A_tensor)
> L=tf.cholesky(tA_A)
> tA_b=tf.matmul(tf.transpose(A_tensor),b)
> sol1=tf.matrix_solve(L,tA_b)

> sol2=tf.matrix_solve(tf.transpose(L),sol1)

> solution_eval=sess.run(sol2)
> solution_eval
array([[1.01379067],
    [0.02290901]])
> slope=solution_eval[0][0]
> y_intercept=solution_eval[1][0]
> print('slope:'+str(slope))
slope:1.0137906744047482
> print('y_intercept:'+str(y_intercept))
y_intercept:0.022909011828880693
> best_fit=[]
> for i in x_vals:
...  best_fit.append(slope*i+y_intercept)
...
> plt.plot(x_vals,y_vals,'o',label='Data')
[<matplotlib.lines.Line2D object at 0x000001E0A58DD9B0>]
> plt.plot(x_vals,best_fit,'r-',label='Best fit line',linewidth=3)
[<matplotlib.lines.Line2D object at 0x000001E0A2DFAF98>]
> plt.legend(loc='upper left')
<matplotlib.legend.Legend object at 0x000001E0A58F03C8>

> plt.show()