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Parameters --- clusterer : HDBSCAN A clustering object that has been fit to the data and had ``prediction_data=True`` set. batch_size : int, optional, default=min(4096, n_rows) Lowers memory requirement by computing distance-based membership in smaller batches of points in the training dat...
Input contains infinity or a value too large for dtype('float32') I do not get this error if I do not try to tune parameters. I have ensured my data does not have any NaN or np.inf - I replace +/- np.inf with np.nan and replace all NaN with 0 later. Before training, I hav...
Furthermore, clustering algorithms can execute in parallel on multi-core processor architectures, can operate in different metric spaces, can automatically eliminate outliers, and offer fine-tuning of hyper-parameters using (randomized) grid search and cross- validation. Lightning ...
Both HDBSCAN and OPTICS can usually perform better when there are clusters of varying densities in the data and are also less sensitive to the choice or initial min. points and ε parameters.
hdbscan(handle_[0], <float*>_input_ptr, <int> n_rows, <int> n_cols, <DistanceType> metric, params, deref(linkage_output), <float*> core_dists_ptr) else: raise ValueError("'connectivity' can only be one of " "{'knn', 'pairwise'}") self.fit_called_ = True self.condensed_tre...
hdbscan(handle_[0], <float*>_input_ptr, <int> n_rows, <int> n_cols, <DistanceType> metric, params, deref(linkage_output), <float*> core_dists_ptr) else: raise ValueError("'connectivity' can only be one of " "{'knn', 'pairwise'}") self.fit_called_ = True self.condensed_tre...
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Parameters --- clusterer : HDBSCAN A clustering object that has been fit to the data and had ``prediction_data=True`` set. batch_size : int, optional, default=min(4096, n_rows) Lowers memory requirement by computing distance-based membership in smaller batches of points in the training dat...
Parameters --- handle : cuml.Handle Specifies the cuml.handle that holds internal CUDA state for computations in this model. Most importantly, this specifies the CUDA stream that will be used for the model's computations, so users can run different models concurrently in different streams by ...