probly.representation.sampling.array_sample¶

Numpy-based sample representation.

Classes

ArraySample(array, sample_axis)

A sample of predictions stored in a numpy array.

class probly.representation.sampling.array_sample.ArraySample(array, sample_axis)[source]¶

Bases: Sample, NDArrayOperatorsMixin, Generic

A sample of predictions stored in a numpy array.

Parameters:

array (NDArray)
sample_axis (int)

classmethod from_iterable(samples, sample_axis='auto', dtype=None)[source]¶

Create an ArraySample from a sequence of samples.

Parameters:

samples (Iterable[D]) – The predictions to create the sample from.
sample_axis (SampleAxis) – The dimension along which samples are organized.
dtype (DTypeLike) – Desired data type of the array.

Returns:

The created ArraySample.

Return type:

Self

classmethod from_sample(sample, sample_axis='auto', dtype=None)[source]¶

Parameters:

sample (Sample[D])
sample_axis (SampleAxis)
dtype (DTypeLike)

Return type:

Self

concat(other)[source]¶

Parameters:: other (Sample[D])
Return type:: Self

copy()[source]¶

Create a copy of the ArraySample.

Returns:: A copy of the ArraySample.
Return type:: Self

move_sample_axis(new_sample_axis)[source]¶

Return a new ArraySample with the sample dimension moved to new_sample_axis.

Parameters:: new_sample_axis (int) – The new sample dimension.
Returns:: A new ArraySample with the sample dimension moved.
Return type:: ArraySample[D]

sample_mean()[source]¶

Compute the mean of the sample.

Return type:: D

sample_std(ddof=1)[source]¶

Compute the standard deviation of the sample.

Parameters:: ddof (int)
Return type:: D

sample_var(ddof=1)[source]¶

Compute the variance of the sample.

Parameters:: ddof (int)
Return type:: D

to_device(device)[source]¶

Move the underlying array to the specified device.

Parameters:: device (Literal['cpu']) – The target device.
Returns:: A new ArraySample on the specified device.
Return type:: Self

transpose(*axes)[source]¶

Return a transposed version of the ArraySample.

This method implicitly also provides full axis tracking support for - np.moveaxis - np.rollaxis Those functions call out to transpose methods for custom array types.

Parameters:: axes (int | None) – The axes to transpose.
Returns:: A transposed version of the ArraySample.
Return type:: Self

property T: Self¶: The transposed version of the underlying array.

array: NDArray¶

property device: str¶: The device of the underlying array.

property dtype: DTypeLike¶: The data type of the underlying array.

property mT: Self¶: The transposed version of the underlying array.

property ndim: int¶: The number of dimensions of the underlying array.

sample_axis: int¶

property sample_size: int¶: Return the number of samples.

property samples: ndarray¶: Return an iterator over the samples.

property shape: tuple[int, ...]¶: The shape of the underlying array.

property size: int¶: The total number of elements in the underlying array.