runningstat

RunningStat

Tool for efficiently computing the mean and stdev of arrays. The arrays themselves are not stored separately, instead, they are accumulated.

Source code in evotorch/neuroevolution/net/runningstat.py

class RunningStat:
    """
    Tool for efficiently computing the mean and stdev of arrays.
    The arrays themselves are not stored separately,
    instead, they are accumulated.
    """

    def __init__(self):
        """
        ``__init__(...)``: Initialize the RunningStat.

        In the beginning, the number of arrays is 0,
        and the sum and the sum of squares are set as NaN.
        """
        # self.sum = np.zeros(shape, dtype='float32')
        # self.sumsq = np.full(shape, eps, dtype='float32')
        # self.count = eps
        self.reset()

    def reset(self):
        """
        Reset the RunningStat to its initial state.
        """
        self._sum = float("nan")
        self._sumsq = float("nan")
        self._count = 0

    def _increment(self, s, ssq, c):
        # self.sum += s
        # self.sumsq += ssq
        # self.count += c
        if self._count == 0:
            self._sum = np.array(s, dtype="float32")
            self._sumsq = np.array(ssq, dtype="float32")
        else:
            self._sum += s
            self._sumsq += ssq
        self._count += c

    @property
    def count(self) -> int:
        """
        Get the number of arrays accumulated.
        """
        return self._count

    @property
    def sum(self) -> np.ndarray:
        """
        Get the sum of all accumulated arrays.
        """
        return self._sum

    @property
    def sum_of_squares(self) -> np.ndarray:
        """
        Get the sum of squares of all accumulated arrays.
        """
        return self._sumsq

    @property
    def mean(self) -> np.ndarray:
        """
        Get the mean of all accumulated arrays.
        """
        return self._sum / self._count

    @property
    def stdev(self) -> np.ndarray:
        """
        Get the standard deviation of all accumulated arrays.
        """
        return np.sqrt(np.maximum(self._sumsq / self._count - np.square(self.mean), 1e-2))

    # def _set_from_init(self, init_mean, init_std, init_count):
    #    init_mean = np.asarray(init_mean, dtype='float32')
    #    init_std = np.asarray(init_std, dtype='float32')
    #    self._sum = init_mean * init_count
    #    self._sumsq = (np.square(init_mean) + np.square(init_std)) * init_count
    #    self._count = init_count

    def update(self, x: Union[np.ndarray, "RunningStat"]):
        """
        Accumulate more data into the RunningStat object.
        If the argument is an array, that array is added
        as one more data element.
        If the argument is another RunningStat instance,
        all the stats accumulated by that RunningStat object
        are added into this RunningStat object.
        """
        if isinstance(x, RunningStat):
            if x.count > 0:
                self._increment(x.sum, x.sum_of_squares, x.count)
        else:
            self._increment(x, np.square(x), 1)

    def normalize(self, x: Union[np.ndarray, list]) -> np.ndarray:
        """
        Normalize the array x according to the accumulated stats.
        """
        x = np.array(x, dtype="float32")
        x -= self.mean
        x /= self.stdev
        return x

    def __copy__(self):
        return deepcopy(self)

    def __get_repr(self):
        return "<RunningStat, count: " + str(self._count) + ">"

    def __str__(self):
        return self.__get_repr()

    def __repr__(self):
        return self.__get_repr()

count: int property readonly

Get the number of arrays accumulated.

mean: ndarray property readonly

Get the mean of all accumulated arrays.

stdev: ndarray property readonly

Get the standard deviation of all accumulated arrays.

sum: ndarray property readonly

Get the sum of all accumulated arrays.

sum_of_squares: ndarray property readonly

Get the sum of squares of all accumulated arrays.

__init__(self) special

__init__(...): Initialize the RunningStat.

In the beginning, the number of arrays is 0, and the sum and the sum of squares are set as NaN.

Source code in evotorch/neuroevolution/net/runningstat.py

def __init__(self):
    """
    ``__init__(...)``: Initialize the RunningStat.

    In the beginning, the number of arrays is 0,
    and the sum and the sum of squares are set as NaN.
    """
    # self.sum = np.zeros(shape, dtype='float32')
    # self.sumsq = np.full(shape, eps, dtype='float32')
    # self.count = eps
    self.reset()

normalize(self, x)

Normalize the array x according to the accumulated stats.

Source code in evotorch/neuroevolution/net/runningstat.py

def normalize(self, x: Union[np.ndarray, list]) -> np.ndarray:
    """
    Normalize the array x according to the accumulated stats.
    """
    x = np.array(x, dtype="float32")
    x -= self.mean
    x /= self.stdev
    return x

reset(self)

Reset the RunningStat to its initial state.

Source code in evotorch/neuroevolution/net/runningstat.py

def reset(self):
    """
    Reset the RunningStat to its initial state.
    """
    self._sum = float("nan")
    self._sumsq = float("nan")
    self._count = 0

update(self, x)

Accumulate more data into the RunningStat object. If the argument is an array, that array is added as one more data element. If the argument is another RunningStat instance, all the stats accumulated by that RunningStat object are added into this RunningStat object.

Source code in evotorch/neuroevolution/net/runningstat.py

def update(self, x: Union[np.ndarray, "RunningStat"]):
    """
    Accumulate more data into the RunningStat object.
    If the argument is an array, that array is added
    as one more data element.
    If the argument is another RunningStat instance,
    all the stats accumulated by that RunningStat object
    are added into this RunningStat object.
    """
    if isinstance(x, RunningStat):
        if x.count > 0:
            self._increment(x.sum, x.sum_of_squares, x.count)
    else:
        self._increment(x, np.square(x), 1)