artlib.topological.TopoART

Topo ART [18].

Classes

TopoART

Topo ART for Topological Clustering.

Module Contents

class artlib.topological.TopoART.TopoART(base_module: artlib.common.BaseART.BaseART, beta_lower: float, tau: int, phi: int)

Bases: artlib.common.BaseART.BaseART

Topo ART for Topological Clustering.

This module implements Topo ART as first published in: [18].

Topo ART clusters accepts an instantiated BaseART module and generates a topological clustering by recording the first and second resonant cluster relationships in an adjacency matrix. Further, it updates the second resonant cluster with a lower learning rate than the first, providing for a distributed learning model.

base_module

adjacency

_permanent_mask

static validate_params(params: dict)

Validate clustering parameters.

Parameters:

params (dict) – A dictionary containing parameters for the algorithm.

Raises:

AssertionError – If the required parameters are not provided or are invalid.

property W: List[numpy.ndarray]

Get the weight matrix of the base module.

Returns:

The weight matrix of the base ART module.

Return type:

list[np.ndarray]

validate_data(X: numpy.ndarray)

Validate the data prior to clustering.

Parameters:

X (np.ndarray) – The input dataset.

prepare_data(X: numpy.ndarray) → numpy.ndarray

Prepare data for clustering.

Parameters:

X (np.ndarray) – The input dataset.

Returns:

Prepared (normalized) data.

Return type:

np.ndarray

restore_data(X: numpy.ndarray) → numpy.ndarray

Restore data to the state prior to preparation.

Parameters:

X (np.ndarray) – The input dataset.

Returns:

Restored data.

Return type:

np.ndarray

category_choice(i: numpy.ndarray, w: numpy.ndarray, params: dict) → tuple[float, dict | None]

Get the activation of the cluster.

Parameters:

• i (np.ndarray) – Data sample.

• w (np.ndarray) – Cluster weight or information.

• params (dict) – Parameters for the algorithm.

Returns:

Cluster activation and cache used for later processing.

Return type:

tuple[float, Optional[dict]]

match_criterion(i: numpy.ndarray, w: numpy.ndarray, params: dict, cache: dict | None = None) → Tuple[float, Dict | None]

Get the match criterion of the cluster.

Parameters:

• i (np.ndarray) – Data sample.

• w (np.ndarray) – Cluster weight or information.

• params (dict) – Parameters for the algorithm.

• cache (dict, optional) – Values cached from previous calculations.

Returns:

Cluster match criterion and cache used for later processing.

Return type:

tuple[float, dict]

match_criterion_bin(i: numpy.ndarray, w: numpy.ndarray, params: dict, cache: dict | None = None, op: Callable = operator.ge) → tuple[bool, dict]

Get the binary match criterion of the cluster.

Parameters:

• i (np.ndarray) – Data sample.

• w (np.ndarray) – Cluster weight or information.

• params (dict) – Parameters for the algorithm.

• cache (dict, optional) – Values cached from previous calculations.

• op (Callable, default=operator.ge) – Comparison operator to use for the binary match criterion.

Returns:

Binary match criterion and cache used for later processing.

Return type:

tuple[bool, dict]

update(i: numpy.ndarray, w: numpy.ndarray, params: dict, cache: dict | None = None) → numpy.ndarray

Update the cluster weight.

Parameters:

• i (np.ndarray) – Data sample.

• w (np.ndarray) – Cluster weight or information.

• params (dict) – Parameters for the algorithm.

• cache (dict, optional) – Values cached from previous calculations.

Returns:

Updated cluster weight.

Return type:

np.ndarray

new_weight(i: numpy.ndarray, params: dict) → numpy.ndarray

Generate a new cluster weight.

Parameters:

• i (np.ndarray) – Data sample.

• params (dict) – Parameters for the algorithm.

Returns:

Newly generated cluster weight.

Return type:

np.ndarray

add_weight(new_w: numpy.ndarray)

Add a new cluster weight.

Parameters:

new_w (np.ndarray) – New cluster weight to add.

prune(X: numpy.ndarray)

Prune clusters based on the number of associated samples.

Parameters:

X (np.ndarray) – The input dataset.

post_step_fit(X: numpy.ndarray)

Perform post-fit operations, such as cluster pruning, after fitting each sample.

Parameters:

X (np.ndarray) – The input dataset.

_match_tracking(cache: List[Dict] | Dict, epsilon: float, params: List[Dict] | Dict, method: Literal['MT+', 'MT-', 'MT0', 'MT1', 'MT~']) → bool

Adjust the vigilance parameter based on match tracking methods.

Parameters:

• cache (dict) – Cached values from previous calculations.

• epsilon (float) – Adjustment factor for the vigilance parameter.

• params (dict) – Parameters for the algorithm.

• method (Literal["MT+", "MT-", "MT0", "MT1", "MT~"]) – Method to use for match tracking.

Returns:

True if the match tracking continues, False otherwise.

Return type:

bool

_set_params(new_params)

Set new parameters for the base module.

Parameters:

new_params (dict) – New parameters to set.

_deep_copy_params() → dict

Create a deep copy of the parameters.

Returns:

Deep copy of the parameters.

Return type:

dict

step_fit(x: numpy.ndarray, match_reset_func: Callable | None = None, match_tracking: Literal['MT+', 'MT-', 'MT0', 'MT1', 'MT~'] = 'MT+', epsilon: float = 0.0) → int

Fit the model to a single sample.

Parameters:

• x (np.ndarray) – Data sample.

• match_reset_func (Callable, optional) – Function to reset the match based on custom criteria.

• match_tracking (Literal["MT+", "MT-", "MT0", "MT1", "MT~"], default="MT+") – Method to reset the match.

• epsilon (float, default=0.0) – Adjustment factor for vigilance.

Returns:

Cluster label of the input sample.

Return type:

int

get_cluster_centers() → List[numpy.ndarray]

Get the centers of each cluster.

Returns:

Cluster centroids.

Return type:

List[np.ndarray]

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

Visualize the boundaries of each cluster.

Parameters:

• ax (Axes) – Figure axes.

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

• linewidth (int, default=1) – Width of boundary lines.