mixle.models.dirichlet_process_mixture module

Dirichlet-process mixture experiment helpers.

This module keeps nonparametric-mixture logic in the model layer. It exposes small stick-breaking utilities and a dependency-free truncated variational mixture loop over ordinary mixle.stats component estimators.

class TruncatedDirichletProcessMixtureFitResult(model, responsibilities, history)

Bases: object

Fitted truncated DPM plus variational responsibilities and history.

Parameters:

• model (TruncatedDirichletProcessMixtureModel)

• responsibilities (ndarray)

• history (list[float])

model: TruncatedDirichletProcessMixtureModel

responsibilities: ndarray

history: list[float]

class TruncatedDirichletProcessMixtureModel(components, alpha=1.0, gamma=None, weights=None, name=None)

Bases: object

Truncated stick-breaking mixture over existing mixle component models.

Parameters:

• components (Sequence[SequenceEncodableProbabilityDistribution])

• alpha (float)

• gamma (Any | None)

• weights (Any | None)

• name (str | None)

property expected_log_weights: ndarray

Return E_q[log pi_k] under the variational stick posteriors.

component_log_density(x)

Return component log densities for one observation.

Parameters:

x (Any)

Return type:

ndarray

log_density(x)

Return the finite-truncation mixture log density for one observation.

Parameters:

x (Any)

Return type:

float

density(x)

Return the finite-truncation mixture density for one observation.

Parameters:

x (Any)

Return type:

float

responsibilities(data, expected=True)

Return posterior component probabilities for observations.

Parameters:

• data (Sequence[Any])

• expected (bool)

Return type:

ndarray

effective_components(threshold=0.01)

Count components with posterior mean stick weight above threshold.

Parameters:

threshold (float)

Return type:

int

sample(size=None, seed=None)

Draw observations from the finite truncation.

Parameters:

• size (int | None)

• seed (int | None)

Return type:

Any | list[Any]

stick_breaking_weights(stick_fractions, residual=True)

Convert stick fractions into mixture weights.

When residual is true, the returned vector has one extra final entry containing the remaining stick mass. This is the usual finite truncation.

Parameters:

• stick_fractions (Any)

• residual (bool)

Return type:

ndarray

expected_log_stick_weights(gamma)

Return E_q[log pi_k] for truncated Beta stick posteriors.

Parameters:

gamma (Any)

Return type:

ndarray

mean_stick_weights(gamma)

Return E_q[pi_k] under independent Beta stick posteriors.

Parameters:

gamma (Any)

Return type:

ndarray

sample_crp_assignments(num_obs, alpha, seed=None)

Sample Chinese-restaurant-process assignments and table counts.

Parameters:

• num_obs (int)

• alpha (float)

• seed (int | None)

Return type:

tuple[ndarray, ndarray]

fit_truncated_dpm(data, initial_components, component_estimator, alpha=1.0, max_its=50, tol=1.0e-8, sort_components=True, name=None)

Fit a truncated DP mixture by coordinate-ascent variational updates.

The component M-steps are delegated to ordinary mixle.stats estimators. This keeps component likelihood math and sufficient statistics in their distribution modules.

Parameters:

• data (Sequence[Any])

• initial_components (Sequence[SequenceEncodableProbabilityDistribution])

• component_estimator (ParameterEstimator | Sequence[ParameterEstimator])

• alpha (float)

• max_its (int)

• tol (float | None)

• sort_components (bool)

• name (str | None)

Return type:

TruncatedDirichletProcessMixtureFitResult