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Holds metadata for and computes running central moments.
tfp.experimental.stats.RunningCentralMoments(
shape, moment, dtype=tf.float32
)
RunningCentralMoments will compute arbitrary central moments in
streaming fashion following the formula proposed by Philippe Pebay
(2008) [1]. For reference, the formula we refer to is the incremental
version of arbitrary moments (equation 2.9). Since the algorithm computes
moments as a function of lower ones, even if not requested, all lower
moments will be computed as well. The moments that are actually returned
is specified by the moment parameter at initialization. Note, while
any arbitrarily high central moment is theoretically supported,
RunningCentralMoments cannot guarantee numerical stability for all
moments.
RunningCentralMoments objects do not hold state information. That
information, which includes intermediate calculations, are held in a
RunningCentralMomentsState as returned via initialize and update
method calls.
References
[1]: Philippe Pebay. Formulas for Robust, One-Pass Parallel Computation of Covariances and Arbitrary-Order Statistical Moments. Technical Report SAND2008-6212, 2008. https://prod-ng.sandia.gov/techlib-noauth/access-control.cgi/2008/086212.pdf
Args | |
|---|---|
shape
|
Python Tuple or TensorShape representing the shape of
incoming samples.
|
moment
|
Integer or iterable of integers that represent the desired moments to return. |
dtype
|
Dtype of incoming samples and the resulting statistics.
By default, the dtype is tf.float32. Any integer dtypes will be
cast to corresponding floats (i.e. tf.int32 will be cast to
tf.float32), as intermediate calculations should be performing
floating-point division.
|
Methods
finalize
finalize(
state
)
Finalizes streaming computation for all central moments.
| Args | |
|---|---|
state
|
RunningCentralMomentsState that represents the current state
of running statistics.
|
| Returns | |
|---|---|
all_moments
|
A Tensor representing estimates of the requested central
moments. Its leading dimension indexes the moment, in order of those
requested (i.e. in order of self.moment).
|
initialize
initialize()
Initializes an empty RunningCentralMomentsState.
The RunningCentralMomentsState contains a RunningMeanState and
a Tensor representing the sum of exponentiated residuals. The sum
of exponentiated residuals is a Tensor of shape
(self.max_moment - 1, self.shape), which contains the sum of
residuals raised to the nth power, for all 2 <= n <= self.max_moment.
| Returns | |
|---|---|
state
|
RunningCentralMomentsState representing a stream of no
inputs.
|
update
update(
state, new_sample
)
Update the RunningCentralMomentsState with a new sample.
| Args | |
|---|---|
state
|
RunningCentralMomentsState that represents the current
state of running statistics.
|
new_sample
|
Incoming Tensor sample with shape and dtype compatible with
those used to form the RunningCentralMomentsState.
|
| Returns | |
|---|---|
state
|
RunningCentralMomentsState with updated calculations.
|