artlib.hierarchical.SMART

SMART [14].

Classes

SMART

SMART for Hierachical Clustering.

Module Contents

class artlib.hierarchical.SMART.SMART(base_ART_class: Type, rho_values: list[float] | numpy.ndarray, base_params: dict, **kwargs)

Bases: artlib.hierarchical.DeepARTMAP.DeepARTMAP

SMART for Hierachical Clustering.

This module implements SMART as first published in: [14]

SMART accepts an uninstantiated BaseART class and hierarchically clusters data in a divisive fashion by using a set of vigilance values that monotonically increase in their restrictiveness. SMART is a special case of DeepARTMAP, which forms the backbone of this class, where all channels receive the same data.

rho_values
prepare_data(X: numpy.ndarray | list[numpy.ndarray], y: numpy.ndarray | None = None) → numpy.ndarray | Tuple[list[numpy.ndarray], numpy.ndarray | None]

Prepare data for clustering.

Parameters:

X (np.ndarray) – The dataset to prepare.

Returns:

Prepared data.

Return type:

np.ndarray

restore_data(X: numpy.ndarray | list[numpy.ndarray], y: numpy.ndarray | None = None) → numpy.ndarray | Tuple[list[numpy.ndarray], numpy.ndarray | None]

Restore data to its original form before preparation.

Parameters:

X (np.ndarray) – The dataset to restore.

Returns:

Restored data.

Return type:

np.ndarray

fit(X: numpy.ndarray, y: numpy.ndarray | None = None, max_iter=1, match_tracking: Literal['MT+', 'MT-', 'MT0', 'MT1', 'MT~'] = 'MT+', epsilon: float = 0.0)

Fit the SMART model to the data.

Parameters:

  • X (np.ndarray) – The dataset to fit the model on.

  • y (np.ndarray, optional) – Not used, present for compatibility.

  • max_iter (int, optional) – The number of iterations to run the model on the data.

  • match_tracking ({"MT+", "MT-", "MT0", "MT1", "MT~"}, optional) – The match reset method to use when adjusting vigilance.

  • epsilon (float, optional) – A small value to adjust vigilance during match tracking.

Returns:

Fitted SMART model.

Return type:

SMART

partial_fit(X: numpy.ndarray, y: numpy.ndarray | None = None, match_tracking: Literal['MT+', 'MT-', 'MT0', 'MT1', 'MT~'] = 'MT+', epsilon: float = 0.0)

Partial fit the SMART model to the data.

Parameters:

  • X (np.ndarray) – The dataset to partially fit the model on.

  • y (np.ndarray, optional) – Not used, present for compatibility.

  • match_tracking ({"MT+", "MT-", "MT0", "MT1", "MT~"}, optional) – The match reset method to use when adjusting vigilance.

  • epsilon (float, optional) – A small value to adjust vigilance during match tracking.

Returns:

Partially fitted SMART model.

Return type:

SMART

plot_cluster_bounds(ax: matplotlib.axes.Axes, colors: artlib.common.utils.IndexableOrKeyable, linewidth: int = 1)

Visualize the cluster boundaries.

Parameters:

  • ax (Axes) – The matplotlib axes on which to plot the cluster boundaries.

  • colors (IndexableOrKeyable) – The colors to use for each cluster.

  • linewidth (int, optional) – The width of the boundary lines.

Return type:

None

visualize(X: numpy.ndarray, y: numpy.ndarray, ax: matplotlib.axes.Axes | None = None, marker_size: int = 10, linewidth: int = 1, colors: artlib.common.utils.IndexableOrKeyable | None = None)

Visualize the clustering of the data with cluster boundaries.

Parameters:

  • X (np.ndarray) – The dataset to visualize.

  • y (np.ndarray) – The cluster labels for the data points.

  • ax (Axes, optional) – The matplotlib axes on which to plot the visualization.

  • marker_size (int, optional) – The size of the data points in the plot.

  • linewidth (int, optional) – The width of the cluster boundary lines.

  • colors (IndexableOrKeyable, optional) – The colors to use for each cluster.

Return type:

None