swyft.weightedmarginals

class swyft.weightedmarginals.WeightedMarginalSamples(weights: Dict[Tuple[int, ...], Union[numpy.ndarray, torch.Tensor]], v: numpy.ndarray)

Parameters:

• weights (Dict[Tuple[int, ...], Union[ndarray, Tensor]]) –

• v (ndarray) –

get_df(marginal_index)

convert a weighted marginal into a dataframe with the marginal_indices, ‘weight’, and ‘logweight’ as columns

Parameters:

marginal_index (Union[int, Sequence[int], Sequence[Sequence[int]]]) – which marginal to select. one at a time.

Returns:

DataFrame with marginal_indices, ‘weight’, and ‘logweight’ for columns

Return type:

DataFrame

get_df_dict()

produce a map from marginal_index to df for all dfs and marginal_indices

Return type:

Dict[Tuple[int, …], DataFrame]

get_logweight(marginal_index)

access the logweight for a certain marginal by marginal_index

Parameters:

marginal_index (Union[int, Sequence[int], Sequence[Sequence[int]]]) – which marginal to select. one at at time.

Returns:

logweight

Return type:

ndarray

get_logweight_marginal(marginal_index)

access the logweight and parameter values for a marginal by index

Parameters:

marginal_index (Union[int, Sequence[int], Sequence[Sequence[int]]]) – which marginal to select. one at a time.

Returns:

the logweight and the parameter values

Return type:

logweight, marginal