import tensorflow as tf
import numpy
import matplotlib.pyplot as plt

rng = numpy.random

#Parameteres 
learning_rate = 0.01
training_epochs = 1000
display_step = 50

train_X = numpy.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 = numpy.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]

print train_X;
print train_Y
print n_samples

X = tf.placeholder("float")
Y = tf.placeholder("float")

#Set model weights 
W = tf.Variable(rng.randn(), name = "weight")
b = tf.Variable(rng.randn(), name = "bias")

#Construct a linear model 
pred = tf.add(tf.multiply(X,W),b)

#Mean squared error 
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)
    
    #fit all training data 
    for epoch in range(training_epochs) : 
        for(x,y) in zip(train_X, train_Y) : 
            sess.run(optimizer, feed_dict={X:x, Y:y})
            
        #Display logs per epoch step 
        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 "Optimization 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()
        