Searched refs:condensed_tree (Results 1 – 6 of 6) sorted by relevance
/dports/math/py-hdbscan/hdbscan-0.8.27/hdbscan/ |
H A D | flat.py | 279 condensed_tree = clusterer.condensed_tree_ 344 condensed_tree, 414 condensed_tree = clusterer.condensed_tree_ 493 condensed_tree._raw_tree, 506 condensed_tree._raw_tree, 612 condensed_tree._raw_tree, 619 condensed_tree._raw_tree, 664 tree = condensed_tree._raw_tree 716 def re_init(predData, condensed_tree, argument 806 def _new_select_clusters(condensed_tree, argument [all …]
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H A D | _hdbscan_tree.pyx | 62 condensed_tree : numpy recarray 164 cpdef dict compute_stability(np.ndarray condensed_tree): argument 186 cdef np.intp_t largest_child = condensed_tree['child'].max() 187 cdef np.intp_t smallest_cluster = condensed_tree['parent'].min() 188 cdef np.intp_t num_clusters = (condensed_tree['parent'].max() - 194 sorted_child_data = np.sort(condensed_tree[['child', 'lambda_val']], 201 parents = condensed_tree['parent'] 202 sizes = condensed_tree['child_size'] 203 lambdas = condensed_tree['lambda_val'] 229 for i in range(condensed_tree.shape[0]): [all …]
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H A D | prediction.py | 98 def __init__(self, data, condensed_tree, min_samples, argument 106 selected_clusters = sorted(condensed_tree._select_clusters()) 108 raw_condensed_tree = condensed_tree._raw_tree
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H A D | plots.py | 42 def _get_leaves(condensed_tree): argument 43 cluster_tree = condensed_tree[condensed_tree['child_size'] > 1] 46 return [condensed_tree['parent'].min()]
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H A D | hdbscan_.py | 56 condensed_tree = condense_tree(single_linkage_tree, 58 stability_dict = compute_stability(condensed_tree) 59 labels, probabilities, stabilities = get_clusters(condensed_tree, 66 return (labels, probabilities, stabilities, condensed_tree,
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/dports/math/py-hdbscan/hdbscan-0.8.27/notebooks/ |
H A D | How Soft Clustering for HDBSCAN Works.ipynb | 157 "def exemplars(cluster_id, condensed_tree):\n", 158 " raw_tree = condensed_tree._raw_tree\n",
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