Searched refs:points_to_predict (Results 1 – 2 of 2) sorted by relevance
| /dports/math/py-hdbscan/hdbscan-0.8.27/hdbscan/ |
| H A D | prediction.py | 331 def approximate_predict(clusterer, points_to_predict): argument
375 points_to_predict = np.asarray(points_to_predict)
377 if points_to_predict.shape[1] != \
396 for i in range(points_to_predict.shape[0]):
456 points_to_predict = np.asarray(points_to_predict)
458 if points_to_predict.shape[1] != \
493 for i in range(points_to_predict.shape[0]):
518 def membership_vector(clusterer, points_to_predict): argument
548 points_to_predict = points_to_predict.astype(np.float64)
560 for i in range(points_to_predict.shape[0]):
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| H A D | flat.py | 198 points_to_predict, argument
319 points_to_predict = np.asarray(points_to_predict)
334 labels = np.empty(points_to_predict.shape[0], dtype=np.int)
339 points_to_predict,
342 for i in range(points_to_predict.shape[0]):
363 clusterer, points_to_predict, argument
412 points_to_predict = points_to_predict.astype(np.float64)
448 result = np.empty((points_to_predict.shape[0], clusters.shape[0]),
454 prediction_data.tree.query(points_to_predict,
458 for i in range(points_to_predict.shape[0]):
[all …]
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