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):
        '''Resets 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
