from __future__ import print_function
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
nr=np.random

learning_rate = 0.01
training_epochs=1000
display_step=50

train_X = np.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,
                         7.042,10.791,5.313,7.997,5.654,9.27,3.1])
train_Y = np.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,
                         2.827,3.465,1.65,2.904,2.42,2.94,1.3])

n_samples = train_X.shape[0]
X=tf.placeholder('float')
Y=tf.placeholder('float')
W=tf.Variable(nr.randn(),name='weight')
b=tf.Variable(nr.randn(),name='bias')

predict=tf.add(tf.multiply(X,W),b)
loss=tf.reduce_sum(tf.pow(predict-Y,2)/(2*n_samples))
optimizer=tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)
init=tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    for epoch in range(training_epochs):
        for x,y in zip(train_X,train_Y):
            sess.run(optimizer,feed_dict={X:x,Y:y})
        if (epoch+1) % display_step==0:
            c=sess.run(loss,feed_dict={X:train_X,Y:train_Y})
            print('Epoch:',epoch+1,'Loss:',c,'W:',sess.run(W),'b:',sess.run(b))
    train_loss=sess.run(loss,feed_dict={X:train_X,Y:train_Y})
    print('Finally,Loss:',train_loss,'W:',sess.run(W),'b:',sess.run(b))
    plt.rcParams['font.sans-serif']=['SimHei'] #指定默认字体 SimHei为黑体
    plt.rcParams['axes.unicode_minus']=False #显示负号
    fig=plt.figure()
    plt.plot(train_X,train_Y,'ro',label='原来的数据')
    plt.plot(train_X,sess.run(W) * train_X + sess.run(b),label='拟合数据')
    plt.legend()
    plt.show()

    test_X = np.asarray([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1])
    test_Y = np.asarray([1.84, 2.273, 3.2, 2.831, 2.92, 3.24, 1.35, 1.03])

    test_loss=sess.run(tf.reduce_sum(tf.pow(predict-Y,2))/(2*test_X.shape[0]),feed_dict={X:test_X,Y:test_Y})
    print('testloss:',test_loss)
    print('abs(train_loss-test_loss):',abs(train_loss-test_loss))
    fig=plt.figure()
    plt.plot(test_X,test_Y,'bo',label='测试数据')
    plt.plot(train_X,sess.run(W) * train_X + sess.run(b),label='拟合数据')
    plt.legend()
    plt.show()