sklearn.cluster.estimate_bandwidth
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sklearn.cluster.estimate_bandwidth(X, quantile=0.3, n_samples=None, random_state=0, n_jobs=None)
[source]
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Estimate the bandwidth to use with the mean-shift algorithm.
That this function takes time at least quadratic in n_samples. For large datasets, it’s wise to set that parameter to a small value.
Parameters: |
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X : array-like, shape=[n_samples, n_features] -
Input points. -
quantile : float, default 0.3 -
should be between [0, 1] 0.5 means that the median of all pairwise distances is used. -
n_samples : int, optional -
The number of samples to use. If not given, all samples are used. -
random_state : int, RandomState instance or None (default) -
The generator used to randomly select the samples from input points for bandwidth estimation. Use an int to make the randomness deterministic. See Glossary. -
n_jobs : int or None, optional (default=None) -
The number of parallel jobs to run for neighbors search. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details. |
Returns: |
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bandwidth : float -
The bandwidth parameter. |
Examples using sklearn.cluster.estimate_bandwidth