perform#

ClusterizeInitializer.perform(X, dists, cluster_match_strategy, estimation_strategies, optimizer=<rework_pysatl_mpest.optimizers.scipy_nelder_mead.ScipyNelderMead object>)[source]#

Performs cluster-based initialization of mixture model parameters.

Parameters:

  • X (ArrayLike) – Input data points for initialization.

  • dists (list[ContinuousDistribution]) – List of distribution models to initialize.

  • cluster_match_strategy (ClusterMatchStrategy) – Strategy for matching clusters to distribution models.

  • estimation_strategies (list[EstimationStrategy]) – List of estimation strategies for each distribution model.

  • optimizer (Optimizer) – Optimizer that will be used in estimation strategies. By default, ScipyNelderMead.

Returns:

Initialized mixture model with estimated parameters and weights.

Return type:

MixtureModel

Notes

The method follows these steps: 1. Sets up the models and configuration 2. Performs clustering on the input data 3. Estimates parameters using either accurate or fast initialization 4. Normalizes component weights 5. Returns the initialized mixture model