update

utility.py

@@ -2,16 +2,63 @@ import numpy as np
 import pandas as pd
 
 
-def load_data():
-	df = pd.read_csv('data/sample.txt', delimiter=',', header=None).astype(np.float32)
+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/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 = 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])
+		df[2] = pd.Series([1]*len(df[0]))
+		df = df.reindex(columns=[1, 2, 0])
 
-	return df
+		return df
+
+
+class Stepper:
+	def __init__(self, print_gap=1.0e-2, 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 += self._cost_diff
+
+		if self.accumulator >= self._print_gap:
+			self.accumulator -= self._print_gap
+			self._b_print_turn = True
+
+		if self._prev_cost != 0 and self._cost_diff > 0:
+			self._serial_divergence_cnt += 1
+			if self._serial_divergence_cnt >= 3:
+				self._b_break_turn = True
+		else:
+			self._serial_divergence_cnt = 0
+
+		if abs(self._cost_diff) < self._break_gap:
+			self._b_break_turn = True
+
+	def is_print_turn(self):
+		return self._b_print_turn
+
+	def is_break_turn(self):
+		return self._b_break_turn