Base class for clustering algorithm.

BaseClusterer(ABC, Base)

Abstract base class for clustering algorithms.

This class defines the core interface for clustering models. It enforces the implementation of the fit and predict methods in all derived classes, and provides a default implementation for fit_predict and get_params.

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Function fit(...)

def fit(self, X: npt.NDArray, verbose: bool = True) -> BaseClusterer

Fit the model to the training data. This abstract method must be implemented by subclasses.

Parameters:

• X: npt.NDArray Input data used for training the model.
• verbose: bool, default=True Flag to enable or disable detailed output during training.

Returns:

• self: Instance of the class that implements this method.

Implementation:

• AiNet

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Function predict(...)

def predict(self, X: npt.NDArray) -> Optional[npt.NDArray]

Generate predictions based on the input data. This abstract method must be implemented by subclasses.

Parameters:

• X: npt.NDArray Input data for which predictions will be generated.

Returns:

• predictions: Optional[npt.NDArray] Predicted cluster labels for each input sample, or None if prediction is not possible.

Implementation:

• AiNet

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Function fit_predict(...)

def fit_predict(self, X: npt.NDArray, verbose: bool = True) -> Optional[npt.NDArray]

Convenience method that combines fit and predict in a single call.

Parameters:

• X: npt.NDArray Input data for which predictions will be generated.
• verbose: bool, default=True Flag to enable or disable detailed output during training.

Returns:

• predictions: Optional[npt.NDArray] Predicted cluster labels for each input sample, or None if prediction is not possible.