#!/usr/bin/env python
# coding: utf-8

# In[1]:


import numpy as np
import numpy.random as rng
import tensorflow as tf


# In[2]:


din = rng.randn(100,1)
out = din * .2 + .1+ .1 * rng.normal(size=din.shape)


# In[3]:


get_ipython().run_line_magic('matplotlib', 'inline')
import matplotlib.pyplot as plt


# In[4]:


plt.scatter(din, out)


# In[52]:


x = tf.placeholder(np.float32, [None])
w = tf.Variable([0.0])
b = tf.Variable([0.0])

y = tf.mul(x, w) + b

y_ = tf.placeholder("float")
loss = tf.reduce_sum(tf.pow(y_ - y, 2))

init = tf.initialize_all_variables()
optim = tf.train.GradientDescentOptimizer(.005).minimize(loss)

with tf.Session() as sess:
    feed_dict={x: din, y_: out}
    sess.run(init)
    print(sess.run(loss, feed_dict=feed_dict))
    for step in range(50):
        sess.run(optim, feed_dict=feed_dict)
        cost_curr = sess.run(loss, feed_dict=feed_dict)
        print("Iteration", step, "Loss", cost_curr)
        print(sess.run(w))
        
    beta = [sess.run(w), sess.run(b)]


# In[53]:


plt.scatter(din, out)
x = np.linspace(din.min(), din.max(), 101)
plt.plot(x, beta[0] * x + beta[1])


# In[54]:


np.polyfit(din, out ,1)


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