odatse.util.resampling module

class odatse.util.resampling.BinarySearch(weights: Iterable)[source]

Bases: Resampler

A resampler that uses binary search to sample based on given weights.

Initialize the BinarySearch resampler with the given weights.

Parameters:: weights (Iterable) – An iterable of weights.

__init__(weights: Iterable)[source]

Initialize the BinarySearch resampler with the given weights.

Parameters:: weights (Iterable) – An iterable of weights.

_sample(r: float) → int[source]

Perform a binary search to find the index corresponding to the given random number.

Parameters:: r (float) – A random number scaled by the maximum weight.
Returns:: The index corresponding to the random number.
Return type:: int

reset(weights: Iterable)[source]

Reset the resampler with new weights.

Parameters:: weights (Iterable) – An iterable of weights.

sample(rs: RandomState) → int[source]

sample(rs: RandomState, size) → ndarray

Sample indices based on the weights.

Parameters:

rs (np.random.RandomState) – A random state for generating random numbers.
size – The number of samples to generate. If None, a single sample is generated.

Returns:

A single sampled index or an array of sampled indices.

Return type:

int or np.ndarray

class odatse.util.resampling.Resampler[source]: Bases: ABC

class odatse.util.resampling.WalkerTable(weights: Iterable)[source]

Bases: Resampler

A resampler that uses Walker’s alias method to sample based on given weights.

Initialize the WalkerTable resampler with the given weights.

Parameters:: weights (Iterable) – An iterable of weights.

__init__(weights: Iterable)[source]

Initialize the WalkerTable resampler with the given weights.

Parameters:: weights (Iterable) – An iterable of weights.

_sample(r: float) → int[source]

Perform a sampling operation based on the given random number.

Parameters:: r (float) – A random number scaled by the number of weights.
Returns:: The index corresponding to the random number.
Return type:: int

reset(weights: Iterable)[source]

Reset the resampler with new weights.

Parameters:: weights (Iterable) – An iterable of weights.

sample(rs: RandomState) → int[source]

sample(rs: RandomState, size) → ndarray

Sample indices based on the weights.

Parameters:

rs (np.random.RandomState) – A random state for generating random numbers.
size – The number of samples to generate. If None, a single sample is generated.

Returns:

A single sampled index or an array of sampled indices.

Return type:

int or np.ndarray