import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
date = np.linspace(1,15,15)
endPrice = np.array([2511.90,2538.26,2510.68,2591.66,2732.98,2701.69,2701.29,2678.67,2726.50,2681.50,2739.17,2715.07,2823.58,2864.90,2919.08])
beginPrice = np.array([2438.71,2500.88,2534.95,2512.52,2594.04,2743.26,2697.47,2695.24,2678.23,2722.13,2674.93,2744.13,2717.46,2832.73,2877.40])
print(date)
plt.figure()
for i in range(0,15):
    # 1 柱状图
    dateOne = np.zeros([2])
    dateOne[0] = i;
    dateOne[1] = i;
    priceOne = np.zeros([2])
    priceOne[0] = beginPrice[i]
    priceOne[1] = endPrice[i]
    if endPrice[i]>beginPrice[i]:
        plt.plot(dateOne,priceOne,'r',lw=8)
    else:
        plt.plot(dateOne,priceOne,'g',lw=8)
#plt.show()
# A(15*1)*w1(1*10)+b1(1*10) = B(15*10)
# B(15*10)*w2(10*1)+b2(15*1) = C(15*1)
# 1 A B C
dateNormal = np.zeros([15,1])
priceNormal = np.zeros([15,1])
for i in range(0,15):
    dateNormal[i,0] = i/14.0;
    priceNormal[i,0] = endPrice[i]/3000.0;
x = tf.placeholder(tf.float32,[None,1])# N行1列的
y = tf.placeholder(tf.float32,[None,1])
# B
w1 = tf.Variable(tf.random_uniform([1,10],0,1))
b1 = tf.Variable(tf.zeros([1,10]))
wb1 = tf.matmul(x,w1)+b1
layer1 = tf.nn.relu(wb1) # 激励函数
# C
w2 = tf.Variable(tf.random_uniform([10,1],0,1))
b2 = tf.Variable(tf.zeros([15,1]))
wb2 = tf.matmul(layer1,w2)+b2
layer2 = tf.nn.relu(wb2)
loss = tf.reduce_mean(tf.square(y-layer2))# y 真实 layer2 计算 标准差
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)#表示我们每次调整的步长 梯度下降法
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range(0,10000):#训练一万次来终止
        sess.run(train_step,feed_dict={x:dateNormal,y:priceNormal})
    # 经过第47行代码训练出一组非常优化的w1w2 b1b2.如何检测w1w2 b1b2是否有效呢？我们就给它一个新的输入层A A + wb(新的预测值) -->layer2(新的预测值放到layer2中)
    # 所以我们要看当前的layer2是否准确，如果准确我们就把当前预测值的结果绘制出来
    pred = sess.run(layer2,feed_dict={x:dateNormal})
    predPrice = np.zeros([15,1])
    for i in range(0,15):
        predPrice[i,0] = (pred*3000)[i,0]
    plt.plot(date,predPrice,'b',lw=1)
    plt.show()

2-22 小综合：人工神经网络逼近股票价格4_学习