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python Programming Glossary: scipy.spatial.distanceIs there any numpy autocorrellation function with standardized output? http://stackoverflow.com/questions/12269834/is-there-any-numpy-autocorrellation-function-with-standardized-output solve # my # numpy problem ... # import numpy as np import scipy.spatial.distance # functions to be defined ... # # def autocorr x result np.correlate..
more efficient way to calculate distance in numpy? http://stackoverflow.com/questions/17527340/more-efficient-way-to-calculate-distance-in-numpy #Uses 26Gb return R def getR4 VVm VVs HHm HHs from scipy.spatial.distance import cdist t0 time.time precomputed_flat numpy.column_stack.. # uses 9 Gb ram return R def getR5 VVm VVs HHm HHs from scipy.spatial.distance import cdist t0 time.time precomputed_flat numpy.column_stack.. There is of course also SciPy's spatial module cdist from scipy.spatial.distance import cdist dist cdist precomputed_flat measured_flat 'euclidean'..
Is it possible to specify your own distance function using Scikits.Learn K-Means Clustering? http://stackoverflow.com/questions/5529625/is-it-possible-to-specify-your-own-distance-function-using-scikits-learn-k-means a small kmeans that uses any of the 20 odd distances in scipy.spatial.distance or a user function. Comments would be welcome this has had only.. env python # kmeans.py using any of the 20 odd metrics in scipy.spatial.distance # kmeanssample 2 pass first sample sqrt N from __future__ import.. import division import random import numpy as np from scipy.spatial.distance import cdist # scipy spatial distance.py # http docs.scipy.org..
Equivalent of Matlab's cluster quality function? http://stackoverflow.com/questions/6644445/equivalent-of-matlabs-cluster-quality-function numpy as np from scipy.cluster.vq import kmeans2 from scipy.spatial.distance import pdist squareform from scikits.learn import datasets import..
Calculating the percentage of variance measure for k-means? http://stackoverflow.com/questions/6645895/calculating-the-percentage-of-variance-measure-for-k-means such as cosine similarity is used EDIT 2 Distortion from scipy.spatial.distance import cdist D cdist points centroids 'euclidean' sum numpy.min.. Euclidean as a distance measure . You can also use the scipy.spatial.distance.cdist function to calculate the distances with the function.. numpy as np from scipy.cluster.vq import kmeans vq from scipy.spatial.distance import cdist import matplotlib.pyplot as plt # load the iris..
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