runningstat

RunningStat

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

This RunningStat is implemented as a wrapper around RunningNorm. The difference is that the interface of RunningStat is simplified to expect only numpy arrays, and expect only non-vectorized observations. With this simplified interface, RunningStat is meant to be used by GymNE, on classical non-vectorized gym tasks.

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.

    This RunningStat is implemented as a wrapper around RunningNorm.
    The difference is that the interface of RunningStat is simplified
    to expect only numpy arrays, and expect only non-vectorized
    observations.
    With this simplified interface, RunningStat is meant to be used
    by GymNE, on classical non-vectorized gym tasks.
    """

    def __init__(self):
        """
        `__init__(...)`: Initialize the RunningStat.
        """
        self._rn: Optional[RunningNorm] = None
        self.reset()

    def reset(self):
        """
        Reset the RunningStat to its initial state.
        """
        self._rn = None

    @property
    def count(self) -> int:
        """
        Get the number of arrays accumulated.
        """
        if self._rn is None:
            return 0
        else:
            return self._rn.count

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

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

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

    @property
    def stdev(self) -> np.ndarray:
        """
        Get the standard deviation of all accumulated arrays.
        """
        return self._rn.stdev.numpy()

    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:
                if self._rn is None:
                    self._rn = deepcopy(x._rn)
                else:
                    self._rn.update(x._rn)
        else:
            if self._rn is None:
                x = np.array(x, dtype="float32")
                self._rn = RunningNorm(shape=x.shape, dtype="float32", device="cpu")
            self._rn.update(x)

    def normalize(self, x: Union[np.ndarray, list]) -> np.ndarray:
        """
        Normalize the array x according to the accumulated stats.
        """
        if self._rn is None:
            return x
        else:
            x = np.array(x, dtype="float32")
            return self._rn.normalize(x)

    def __copy__(self):
        return deepcopy(self)

    def __repr__(self) -> str:
        return f"<{self.__class__.__name__}, count: {self.count}>"

    def to(self, device: Union[str, torch.device]) -> "RunningStat":
        """
        If the target device is cpu, return this RunningStat instance itself.
        A RunningStat object is meant to work with numpy arrays. Therefore,
        any device other than the cpu will trigger an error.

        Args:
            device: The target device. Only cpu is supported.
        Returns:
            The original RunningStat.
        """
        if torch.device(device) == torch.device("cpu"):
            return self
        else:
            raise ValueError(
                f"The received target device is {repr(device)}. However, RunningStat can only work on a cpu."
            )

    def to_layer(self) -> nn.Module:
        """
        Make a PyTorch module which normalizes the its inputs.

        Returns:
            An ObsNormLayer instance.
        """
        return self._rn.to_layer()

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.

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

def __init__(self):
    """
    `__init__(...)`: Initialize the RunningStat.
    """
    self._rn: Optional[RunningNorm] = None
    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.
    """
    if self._rn is None:
        return x
    else:
        x = np.array(x, dtype="float32")
        return self._rn.normalize(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._rn = None

to(self, device)

If the target device is cpu, return this RunningStat instance itself. A RunningStat object is meant to work with numpy arrays. Therefore, any device other than the cpu will trigger an error.

Parameters:

Name	Type	Description	Default
device	Union[str, torch.device]	The target device. Only cpu is supported.	required

Returns:

Type	Description
RunningStat	The original RunningStat.

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

def to(self, device: Union[str, torch.device]) -> "RunningStat":
    """
    If the target device is cpu, return this RunningStat instance itself.
    A RunningStat object is meant to work with numpy arrays. Therefore,
    any device other than the cpu will trigger an error.

    Args:
        device: The target device. Only cpu is supported.
    Returns:
        The original RunningStat.
    """
    if torch.device(device) == torch.device("cpu"):
        return self
    else:
        raise ValueError(
            f"The received target device is {repr(device)}. However, RunningStat can only work on a cpu."
        )

to_layer(self)

Make a PyTorch module which normalizes the its inputs.

Returns:

Type	Description
Module	An ObsNormLayer instance.

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

def to_layer(self) -> nn.Module:
    """
    Make a PyTorch module which normalizes the its inputs.

    Returns:
        An ObsNormLayer instance.
    """
    return self._rn.to_layer()

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:
            if self._rn is None:
                self._rn = deepcopy(x._rn)
            else:
                self._rn.update(x._rn)
    else:
        if self._rn is None:
            x = np.array(x, dtype="float32")
            self._rn = RunningNorm(shape=x.shape, dtype="float32", device="cpu")
        self._rn.update(x)