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...
Update cluster endpoint to use UMAP+HDBSCAN Remove threshold and add n_neighbors and is_deterministic as params 4.1.6 #205 Add param max_chunks_per_doc to rerank Enforce model param for rerank 4.1.5 #208 Fix a missing import for CohereConnectionError 4.1.4 #204 Add generate_preference...
Here is a simple example for using these machine learning functions: Note Some of these parameters are optional and depend on the specific algorithm being used. Create an instance of the class (for the algorithm you want to use) and pass over the algorithm parameters. ...
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At this point, we could already carry out most of the analyses common in the bird song literature, for example, by extracting some simple acoustic parameters from the segmented data. Instead, we want to preserve all the temporal and spectral information that is available in the spectrograms to...
Fig. 8 presents an example of three clusters which have been identified using a HDBSCAN algorithm on a particular 10-minute interval lightning data. Fig. 8. Clustering lightning data using HDBSCAN algorithm. It can be seen from the Fig. 8 that not all data points (i....
Default parameters were used for all classifiers and clustering algorithms. It is straightforward to observe that execution times largely depend on the chosen algorithms, with HDBSCAN the least expensive and spectral clustering the most expensive choice among clustering techniques, irrespective of classifier...
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.
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...