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import numpy as np
import tensorflow as tf
import tensorboard_jupyter as tb
import matplotlib.pyplot as plt
from tensorflow.python.framework import ops
from sklearn import datasets
ops.reset_default_graph()
sess = tf.Session()
x_vals = np.linspace(0, 10, 100)
# y = x に正規表現でノイズを加える
y_vals = x_vals + np.random.normal(0, 1, 100)
# 計画行列Aを作成する
x_vals_column = np.transpose(np.matrix(x_vals))
ones_column = np.transpose(np.matrix(np.repeat(1, 100)))
# 2つの縦ベクトルを列に並べる
A = np.column_stack((x_vals_column, ones_column))
# 行列bを作成
b = np.transpose(np.matrix(y_vals))
with tf.name_scope('A'):
A_tensor = tf.constant(A)
with tf.name_scope('B'):
b_tensor = tf.constant(b)
# コレスキー分解
tA_A = tf.matmul(tf.transpose(A_tensor), A_tensor)
L = tf.cholesky(tA_A)
# L * y = t(A) * bを解く
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)
# 解から傾きと切片を抽出
slope = solution_eval[0][0]
y_intercept = solution_eval[1][0]
print('slope: ' + str(slope))
print('y_intercept: ' + str(y_intercept))
tf.summary.FileWriter('./log/', sess.graph)
tb.show_graph(tf.get_default_graph().as_graph_def())
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