refactor: change cluster_match_strategy structure, matching methods separated into

greedy, hungarian, permutation methods, also for scoring functions aic and likelihood
feat: Enums for matching methods, scoring methods
tests: tests that include cluster_match_strategy commented/deleted

Author: VITYANA

rework_pysatl_mpest/initializers/__init__.py

@@ -9,65 +9,21 @@
 
 initializers provide good starting points for EM algorithm and other optimization
 methods, helping to avoid poor local optima and improving convergence.
-
-**Usage Example**
-
-.. code-block:: python
-
-    >>> from rework_pysatl_mpest.distributions.exponential import Exponential
-    >>> from sklearn.cluster import KMeans
-    >>> from rework_pysatl_mpest.initializers.clusterize_initializer import ClusterizeInitializer
-    >>> from rework_pysatl_mpest.initializers import ClusterMatchStrategy, EstimationStrategy
-    >>> from rework_pysatl_mpest.core.mixture import MixtureModel
-
-    >>> # Create initializer with KMeans clustering
-    >>> initializer_cluster = ClusterizeInitializer(
-    ...     is_accurate=True,
-    ...     is_soft=False,
-    ...     clusterizer=KMeans(n_clusters=3)
-    ... )
-
-    >>> # Create distribution models to initialize
-    >>> distributions = [Exponential(loc=0.0, rate=0.1),
-    >>>Exponential(loc=5.0, rate=0.05), Exponential(loc=10.0, rate=0.01)]
-
-    >>> # Generate sample data
-    >>> mixture = MixtureModel(distributions, [0.3, 0.4, 0.3])
-    >>> X = mixture.generate(300)
-
-    >>> # Perform initialization
-    >>> mixture_model = initializer_cluster.perform(
-    ...     X=X,
-    ...     dists=distributions,
-    ...     cluster_match_strategy=ClusterMatchStrategy.AKAIKE,
-    ...     estimation_strategies=[EstimationStrategy.QFUNCTION] * len(distributions)
-    ... )
-
-    >>> # The mixture model is now initialized with estimated parameters
-    >>> print(f"Number of components: {len(mixture_model.components)}")
-    >>> print(f"Weights: {mixture_model.weights}")
 """
 
 __author__ = "Viktor Khanukaev"
 __copyright__ = "Copyright (c) 2025 PySATL project"
 __license__ = "SPDX-License-Identifier: MIT"
 
 from ._estimation_strategies.q_function import q_function_strategy, q_function_strategy_exponential
-from .cluster_match_strategy import (
-    match_clusters_for_models_akaike,
-    match_clusters_for_models_log_likelihood,
-)
 from .clusterize_initializer import ClusterizeInitializer
 from .initializer import Initializer
-from .strategies import ClusterMatchStrategy, EstimationStrategy
+from .strategies import EstimationStrategy
 
 __all__ = [
-    "ClusterMatchStrategy",
     "ClusterizeInitializer",
     "EstimationStrategy",
     "Initializer",
-    "match_clusters_for_models_akaike",
-    "match_clusters_for_models_log_likelihood",
     "q_function_strategy",
     "q_function_strategy_exponential",
 ]