import numpy as np
import pandas as pd


class Loader:
	def load_data():
		#df = pd.read_csv('data/sample.txt', delimiter=',', header=None).astype(np.float32)

		#df = pd.read_csv('data/ex1data1.txt', delimiter=',', header=None).astype(np.float32)

		df = pd.read_csv('data/train.csv', delimiter=',', comment='#').astype(np.float32)
		df[0] = df['x']
		df[1] = df['y']

		df[2] = pd.Series([1]*len(df[0]))
		df = df.reindex(columns=[1, 2, 0])

		return df


class Stepper:
	def __init__(self, print_gap=1.0e-4, break_gap=1.0e-9):
		self._prev_cost = 0
		self._cost = 0
		self._cost_diff = 0

		self._serial_divergence_cnt = 0

		self.accumulator = 0

		self._print_gap = print_gap
		self._break_gap = break_gap
		self._b_print_turn = False
		self._b_break_turn = False

	def add_step(self, step):
		self._b_print_turn = False
		self._b_break_turn = False

		self._prev_cost = self._cost
		self._cost = step
		self._cost_diff = self._cost - self._prev_cost

		self.accumulator += abs(self._cost_diff)

		if self.accumulator >= self._print_gap:
			self.accumulator = 0
			self._b_print_turn = True

		if self._prev_cost != 0 and (self._cost_diff > 0 or abs(self._cost_diff) < self._break_gap):
			self._serial_divergence_cnt += 1
			if self._serial_divergence_cnt >= 5:
				self._b_break_turn = True
		else:
			self._serial_divergence_cnt = 0

	def is_print_turn(self):
		return self._b_print_turn

	def is_break_turn(self):
		return self._b_break_turn