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import numpy as np from scipy.signal import find_peaks import matplotlib.pyplot as plt # # Example data: Replace with your actual data # P_new = np.load('P_new.npy') n_rows, n_cols = P_new.shape T = np.real(P_new) # Simulating a 2D matrix # Peak detection parameters min_peak...
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...
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.
<float*>mst_weights_ptr) self.hdbscan_output_ = <size_t>linkage_output cdef HDBSCANParams params params.min_samples = self.min_samples # params.alpha = self.alpha params.min_cluster_size = self.min_cluster_size params.max_cluster_size = self.max_cluster_size params.cluster_selection_epsilon...
<float*>mst_weights_ptr) self.hdbscan_output_ = <size_t>linkage_output cdef HDBSCANParams params params.min_samples = self.min_samples # params.alpha = self.alpha params.min_cluster_size = self.min_cluster_size params.max_cluster_size = self.max_cluster_size params.cluster_selection_epsilon...