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fast_plscan 0.1.dev50+g75b83112e documentation
fast_plscan 0.1.dev50+g75b83112e documentation

Features

  • Basic usage
  • Exploration plots
  • Centroids, Medoids, and Exemplars
  • Soft clustering
  • Clustering new data points
  • Other distance metrics
  • Clustering with multiple components
  • Clustering with sample weights
  • Other persistence measures

Demonstrations

  • Cluster selection strategies
  • Parameter sensitivity analysis
  • Performance Benchmarks

API reference

  • fast_plscan
    • PLSCAN
      • VALID_BALLTREE_METRICS
      • VALID_KDTREE_METRICS
      • condensed_tree_
      • core_distances_
      • labels_
      • leaf_tree_
      • minimum_spanning_tree_
      • persistence_trace_
      • probabilities_
      • selected_clusters_
      • single_linkage_tree_
      • cluster_layers
      • compute_centroids
      • compute_exemplar_indices
      • compute_medoid_indices
      • distance_cut
      • fit
      • fit_predict
      • get_metadata_routing
      • get_params
      • min_cluster_size_cut
      • set_fit_request
      • set_params
    • clusters_from_spanning_forest
    • extract_mutual_spanning_forest
    • compute_mutual_spanning_tree
    • get_distance_callback
    • compute_centroids_from_features
    • compute_exemplar_indices_from_trees
    • compute_medoid_indices_from_features
    • compute_medoid_indices_from_graph
  • fast_plscan.prediction
    • all_points_membership_vectors
    • approximate_predict
    • membership_vectors
  • fast_plscan.plots
    • CondensedTree
      • plot
      • to_networkx
      • to_numpy
      • to_pandas
    • LeafTree
      • plot
      • to_networkx
      • to_numpy
      • to_pandas
    • PersistenceTrace
      • plot
      • to_numpy
      • to_pandas

Development

  • Local development
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