# Linear Regressoin
# Rewrite/copy of Aymeric Damien 

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
rng = np.random



lr = 0.01
num_epochs = 1000
display_steps = 50

train_x = numpy.asarray(

#training Data
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])7654
n_samples = train_X.shape[0]


x = tf.placeholder("float")
y = tf.placeholder("float")

W = tf.Variable(rng.random() ,  name = "weight")
b = tf.Variable(rng.random() , name = "bias")


pred = tf.add(tf.mul(x , W) , b)

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

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

init = tf.global_variables_initializer()


with tf.Session() as sess :
    sess.run(init)

    for epoch in range(num_epochs):
        for (x_ ,y_) in zip(train_X , train_Y):
            sess.run(optimizer , feed_dict = {x:x_ , y:y_})

        if (epoch + 1) % display_steps == 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_
    print "Training cost =" , training_cost , "W= " , sess.run(W) , "b= " , sess.run(b) , "\n"


    plt.plot(train_X , train_Y , "ro" , label = "Original data")
    plt.plot(train_X , sess.run(W) * train_X + sess.run(b) , label="Fittled Line")
    plt.legend()
    plt.show()






30f2fd69552805d15a585fa69a86a737a4599152
# Linear Regressoin
# Rewrite/copy of Aymeric Damien 

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
rng = np.random



lr = 0.01
num_epochs = 1000
display_steps = 50

train_x = numpy.asarray(

#training Data
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])7654
n_samples = train_X.shape[0]


x = tf.placeholder("float")
y = tf.placeholder("float")

W = tf.Variable(rng.random() ,  name = "weight")
b = tf.Variable(rng.random() , name = "bias")


pred = tf.add(tf.mul(x , W) , b)

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

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

init = tf.global_variables_initializer()


with tf.Session() as sess :
    sess.run(init)

    for epoch in range(num_epochs):
        for (x_ ,y_) in zip(train_X , train_Y):
            sess.run(optimizer , feed_dict = {x:x_ , y:y_})

        if (epoch + 1) % display_steps == 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_
    print "Training cost =" , training_cost , "W= " , sess.run(W) , "b= " , sess.run(b) , "\n"


    plt.plot(train_X , train_Y , "ro" , label = "Original data")
    plt.plot(train_X , sess.run(W) * train_X + sess.run(b) , label="Fittled Line")
    plt.legend()
    plt.show()





