import numpy as np
import pandas as pd

import utility

df = utility.load_data()
x_data = df.values[:, 1:]
y_data = df.values[:, 0]
m, n = x_data.shape


import tensorflow as tf
x = tf.Variable(x_data)
y = tf.Variable(y_data)
w = tf.Variable(tf.zeros((n, 1)))

a = 0.001

iter = 0
sum_ = 0

print_gap = 1.0e-3
break_gap = 1.0e-10

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

	while True:
		h = tf.matmul(x, w)
		diff = h-tf.reshape(y, h.shape)
		cost = a*tf.reduce_mean(diff**2, axis=0)/2
		gradient = a*tf.matmul(diff, x, True)
		w -= tf.reshape(gradient, w.shape)

		values = sess.run((h, cost, gradient, tf.reshape(w, [-1])))

		max__ = max(abs(values[2]))[0]
		sum__ = sum(abs(values[2]))[0]
		sum_ += sum__
		if sum_ >= print_gap:
			print('{}: {} ({})'.format(iter, values[1], values[3]))
			sum_ -= print_gap

		if max__ < break_gap:
			break

		iter += 1

	print('{}: {} ({})'.format(iter, values[1], values[3]))
	print('{}'.format(np.c_[values[0], y_data]))