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Searched refs:condensed_tree (Results 1 – 6 of 6) sorted by relevance

/dports/math/py-hdbscan/hdbscan-0.8.27/hdbscan/
H A Dflat.py279 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
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H A D_hdbscan_tree.pyx62 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]):
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H A Dprediction.py98 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
H A Dplots.py42 def _get_leaves(condensed_tree): argument
43 cluster_tree = condensed_tree[condensed_tree['child_size'] > 1]
46 return [condensed_tree['parent'].min()]
H A Dhdbscan_.py56 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,
/dports/math/py-hdbscan/hdbscan-0.8.27/notebooks/
H A DHow Soft Clustering for HDBSCAN Works.ipynb157 "def exemplars(cluster_id, condensed_tree):\n",
158 " raw_tree = condensed_tree._raw_tree\n",