import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
rng=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(rng.randn(),name="weight")
b=tf.Variable(rng.randn(),name="bias")

pred=tf.add(tf.multiply(X,w),b)

cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)

optimizer=tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

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(cost, feed_dict={X: train_X, Y: train_Y})
            print("Epoch:", '%04d' % (epoch + 1), "cost=", "{:.9f}".format(c),"W=", sess.run(w), "b=", sess.run(b))
    print("Optimizer finished")
    training_cost=sess.run(cost,feed_dict={X:train_X,Y:train_Y})
    print("Training cost=", training_cost, "W=", sess.run(w), "b=", sess.run(b), '\n')

    # Graphic display
    plt.plot(train_X, train_Y, 'ro', label='Original data')
    plt.plot(train_X, sess.run(w) * train_X + sess.run(b), label='Fitted line')
    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_cost=sess.run(tf.reduce_sum(tf.pow(pred-Y,2))/(2*test_X.shape[0]),feed_dict={X: test_X, Y: test_Y})
    print("Testing cost=", test_cost)
    print("Absolute mean square loss difference:", abs(
        training_cost - test_cost))

    plt.plot(test_X, test_Y, 'bo', label='Testing data')
    plt.plot(train_X, sess.run(w) * train_X + sess.run(b), label='Fitted line')
    plt.legend()
    plt.show()

