import numpy as np


class PolyakAverage(object):
    r"""Keep a running average of a quantity via Polyak averaging. 
    
    Compared with estimating mean, it is more sentitive to recent changes. 
    
    Args:
        alpha (float): factor to control the sensitivity to recent changes, in the range [0, 1].
            Zero is most sensitive to recent change. 
    
    """
    def __init__(self, alpha):
        assert alpha >= 0 and alpha <= 1
        self.alpha = alpha
        
        self.x = None
        
    def __call__(self, x):
        r"""Update the estimate. 
        
        Args:
            x (object): additional data to update the estimation of running average. 
        """
        x = np.asarray(x)
        if self.x is None:
            self.x = x
        else:
            self.x = self.alpha*self.x + (1 - self.alpha)*x
        return self.x
        
    def get_current(self):
        r"""Return the current running average. """
        return self.x

07a080f45e7a59e9470e8a7bfb25daf1f4517287
<mask token>


class PolyakAverage(object):
    <mask token>

    def __init__(self, alpha):
        assert alpha >= 0 and alpha <= 1
        self.alpha = alpha
        self.x = None
    <mask token>
    <mask token>

<mask token>


class PolyakAverage(object):
    <mask token>

    def __init__(self, alpha):
        assert alpha >= 0 and alpha <= 1
        self.alpha = alpha
        self.x = None

    def __call__(self, x):
        """Update the estimate. 
        
        Args:
            x (object): additional data to update the estimation of running average. 
        """
        x = np.asarray(x)
        if self.x is None:
            self.x = x
        else:
            self.x = self.alpha * self.x + (1 - self.alpha) * x
        return self.x

    def get_current(self):
        """Return the current running average. """
        return self.x

<mask token>


class PolyakAverage(object):
    """Keep a running average of a quantity via Polyak averaging. 
    
    Compared with estimating mean, it is more sentitive to recent changes. 
    
    Args:
        alpha (float): factor to control the sensitivity to recent changes, in the range [0, 1].
            Zero is most sensitive to recent change. 
    
    """

    def __init__(self, alpha):
        assert alpha >= 0 and alpha <= 1
        self.alpha = alpha
        self.x = None

    def __call__(self, x):
        """Update the estimate. 
        
        Args:
            x (object): additional data to update the estimation of running average. 
        """
        x = np.asarray(x)
        if self.x is None:
            self.x = x
        else:
            self.x = self.alpha * self.x + (1 - self.alpha) * x
        return self.x

    def get_current(self):
        """Return the current running average. """
        return self.x

import numpy as np


class PolyakAverage(object):
    """Keep a running average of a quantity via Polyak averaging. 
    
    Compared with estimating mean, it is more sentitive to recent changes. 
    
    Args:
        alpha (float): factor to control the sensitivity to recent changes, in the range [0, 1].
            Zero is most sensitive to recent change. 
    
    """

    def __init__(self, alpha):
        assert alpha >= 0 and alpha <= 1
        self.alpha = alpha
        self.x = None

    def __call__(self, x):
        """Update the estimate. 
        
        Args:
            x (object): additional data to update the estimation of running average. 
        """
        x = np.asarray(x)
        if self.x is None:
            self.x = x
        else:
            self.x = self.alpha * self.x + (1 - self.alpha) * x
        return self.x

    def get_current(self):
        """Return the current running average. """
        return self.x

import numpy as np


class PolyakAverage(object):
    r"""Keep a running average of a quantity via Polyak averaging. 
    
    Compared with estimating mean, it is more sentitive to recent changes. 
    
    Args:
        alpha (float): factor to control the sensitivity to recent changes, in the range [0, 1].
            Zero is most sensitive to recent change. 
    
    """
    def __init__(self, alpha):
        assert alpha >= 0 and alpha <= 1
        self.alpha = alpha
        
        self.x = None
        
    def __call__(self, x):
        r"""Update the estimate. 
        
        Args:
            x (object): additional data to update the estimation of running average. 
        """
        x = np.asarray(x)
        if self.x is None:
            self.x = x
        else:
            self.x = self.alpha*self.x + (1 - self.alpha)*x
        return self.x
        
    def get_current(self):
        r"""Return the current running average. """
        return self.x
