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import numpy as np
class Meter(object):
"""Meters provide a way to keep track of important statistics in an online manner.
This class is abstract, but provides a standard interface for all meters to follow.
"""
def reset(self):
"""Reset the meter to default settings."""
pass
def add(self, value):
"""Log a new value to the meter
Args:
value: Next result to include.
"""
pass
def value(self):
"""Get the value of the meter in the current state."""
pass
class AverageValueMeter(Meter):
def __init__(self):
super(AverageValueMeter, self).__init__()
self.reset()
self.val = 0
def add(self, value, n=1):
self.val = value
self.sum += value
self.var += value * value
self.n += n
if self.n == 0:
self.mean, self.std = np.nan, np.nan
elif self.n == 1:
self.mean = 0.0 + self.sum # This is to force a copy in torch/numpy
self.std = np.inf
self.mean_old = self.mean
self.m_s = 0.0
else:
self.mean = self.mean_old + (value - n * self.mean_old) / float(self.n)
self.m_s += (value - self.mean_old) * (value - self.mean)
self.mean_old = self.mean
self.std = np.sqrt(self.m_s / (self.n - 1.0))
def value(self):
return self.mean, self.std
def reset(self):
self.n = 0
self.sum = 0.0
self.var = 0.0
self.val = 0.0
self.mean = np.nan
self.mean_old = 0.0
self.m_s = 0.0
self.std = np.nan
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