import numpy as np
import pandas as pd

import utility

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


import tensorflow as tf
x = tf.placeholder(tf.float32)
y = tf.placeholder(tf.float32)
feed = {x: x_data, y: y_data.reshape((m, 1))}

w = tf.Variable(tf.zeros((n, 1)))

a = tf.Variable(0.0002)
h = tf.matmul(x, w)
cost = tf.reduce_mean(tf.square(h-y))
opt = tf.train.GradientDescentOptimizer(a)
train = opt.minimize(cost)

iter = 0
sum_ = 0

stepper = utility.Stepper()

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

	while True:
		w1 = sess.run(w)

		sess.run(train, feed_dict=feed)

		stepper.add_step(sess.run(cost, feed))
		if stepper.is_print_turn():
			print('{}: {}'.format(iter, sess.run(cost, feed_dict=feed)))

		if stepper.is_break_turn():
			break

		iter += 1

	h_v = sess.run(h, feed_dict=feed)

	print('{}: {}'.format(iter, sess.run(cost, feed_dict=feed)))
	print('{}'.format(np.c_[h_v, y_data]))