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python Programming Glossary: euclideanEuclidean distance between points in two different Numpy arrays, not within http://stackoverflow.com/questions/1871536/euclidean-distance-between-points-in-two-different-numpy-arrays-not-within distance between points in two different Numpy arrays not within.. of x y coordinates and I would like to find the minimum Euclidean distance between each point in one array with all the points..
Inverse Distance Weighted (IDW) Interpolation with Python http://stackoverflow.com/questions/3104781/inverse-distance-weighted-idw-interpolation-with-python if different X coordinates measure different things Euclidean distance can be way off. For example if X0 is in the range 0..
Identifying points with the smallest Euclidean distance http://stackoverflow.com/questions/5119644/identifying-points-with-the-smallest-euclidean-distance points with the smallest Euclidean distance I have a collection of n dimensional points and I.. What kind of optimisation can I apply to it RELATED Euclidean distance between points in two different Numpy arrays not within..
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 the data vectors to X 1 then cosinedistance X Y 1 X . Y Euclidean distance X Y ^2 2 is fast. For bit vectors keep the norms separately..
http://stackoverflow.com/questions/5551286/filling-gaps-in-a-numpy-array This gives a nearest neighbor like result but not in Euclidean or Manhattan distance I think it might be nearest neighbor if..
Finding k-nearest neighbors for a given vector? http://stackoverflow.com/questions/5684370/finding-k-nearest-neighbors-for-a-given-vector between them. If distance_fn is None the default the Euclidean distance is used. Returns a dictionary of the class to the weight.. dist i else # Default Use a fast implementation of the Euclidean distance temp numpy.zeros len x # Predefining temp allows reuse.. between them. If distance_fn is None the default the Euclidean distance is used. weights calculate knn x weight_fn weight_fn..
Calculating the percentage of variance measure for k-means? http://stackoverflow.com/questions/6645895/calculating-the-percentage-of-variance-measure-for-k-means function returns this measure by default computed with Euclidean as a distance measure . You can also use the scipy.spatial.distance.cdist..
Python machine-learning library? http://stackoverflow.com/questions/7326958/python-machine-learning-library neighbors requires an appropriate similarity metric Euclidean distance is the usual choice though not always the best one..
Detecting if an object from one image is in another image with OpenCV http://stackoverflow.com/questions/7881133/detecting-if-an-object-from-one-image-is-in-another-image-with-opencv not sure how to quantify similar pixels. I guess just the Euclidean distance of their RGB values The problem there is that it kind..
Python: tf-idf-cosine: to find document similarity http://stackoverflow.com/questions/12118720/python-tf-idf-cosine-to-find-document-similarity count features and apply TF IDF normalization and row wise euclidean normalization you can do it in one operation with TfidfVectorizer..
Compare similarity of images using OpenCV with Python http://stackoverflow.com/questions/13379909/compare-similarity-of-images-using-opencv-with-python .reshape y.size 1 .flatten #distance matrix based on euclidean distance row_x col_x meshgrid range x.shape 0 range x.shape..
calculate euclidean distance with numpy http://stackoverflow.com/questions/1401712/calculate-euclidean-distance-with-numpy euclidean distance with numpy I have two points in 3D xa ya za xb yb.. numpy.array xa ya za b numpy.array xb yb zb python numpy euclidean distance share improve this question Use numpy.linalg.norm..
more efficient way to calculate distance in numpy? http://stackoverflow.com/questions/17527340/more-efficient-way-to-calculate-distance-in-numpy R spdist.cdist precomputed_flat measured_flat 'sqeuclidean' #.T print R4 t time.time t0 R.shape #17.7022118568 108225 10500.. HHm.flatten R spdist.cdist precomputed_flat measured_flat 'euclidean' #.T print R5 t time.time t0 R.shape #15.6070930958 108225 10500.. is also why those two methods take 26Gb And why is the sqeuclidean slower than the euclidean sqeuclidean should just do VVm VVs..
Euclidean distance between points in two different Numpy arrays, not within http://stackoverflow.com/questions/1871536/euclidean-distance-between-points-in-two-different-numpy-arrays-not-within a way to efficiently generate this submatrix python numpy euclidean distance share improve this question Months later scipy.spatial.distance.cdist..
Python: For each list element apply a function across the list http://stackoverflow.com/questions/493367/python-for-each-list-element-apply-a-function-across-the-list I am using takes two coordinates and calculates the euclidean distance. I'm trying to find minimum euclidean distance between.. the euclidean distance. I'm trying to find minimum euclidean distance between any two points but I don't need a fancy algorithm...
Identifying points with the smallest Euclidean distance http://stackoverflow.com/questions/5119644/identifying-points-with-the-smallest-euclidean-distance arrays not within python algorithm numpy nearest neighbor euclidean distance share improve this question Try scipy.spatial.distance.pdist..
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 def kmeans X centres delta .001 maxiter 10 metric euclidean p 2 verbose 1 centres Xtocentre distances kmeans X initial centres.. n return X sampleix def nearestcentres X centres metric euclidean p 2 each X nearest centre any metric euclidean2 ~ withinss is.. metric euclidean p 2 each X nearest centre any metric euclidean2 ~ withinss is more sensitive to outliers cityblock manhattan..
Calculating the percentage of variance measure for k-means? http://stackoverflow.com/questions/6645895/calculating-the-percentage-of-variance-measure-for-k-means import cdist D cdist points centroids 'euclidean' sum numpy.min D axis 1 The output for the first set of points.. centroids numpy.array 6 7 1 2 D cdist points centroids 'euclidean' sum numpy.min D axis 1 9.0644951022459797 I guess the last.. 0 1 1 0 1 0 9 9 9 10 9 8 10 9 10 8 # centroids C 9 8 0 0 # euclidean distance from each point to each cluster centroid D pdist2 X..
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