I am new to this forum and not a native english speaker, so please be nice! :) Here is the challenge I face at the moment:
I am new to Numpy and I would like to ask you how to calculate euclidean distance between points stored in a vector.
Given two points P,Q and a delta, I defined the equivalence relation ~=, where P ~= Q if EuclideanDistance(P,Q) <= delta. Now, given a set S of n points, in the example S = (A, B, C, D, E, F) and n
I\'m working with a CBIR (Content-based Image Retrieval) project which will draw RGB histogram of images and also calculate the distance between other images with query image.
I am working towards comparing multiple images. I have these image data as column vectors of a matrix called \"images.\" I want to assess the similarity of images by first computing their Eucledian di
I\'m attempting to find the euclidean distance in meters between two points on an MKMapView using iPhone 开发者_StackOverflow社区OS 3.2. The problem is that I have these coordinates in terms of latitu
List1 contains a high number (~7^10) of N-dimensional points (N <=10), List2 contains the same or fewer number of N-dimensional points (N <=10).
Given : An array A[1..n] of real numbers. Goal : An array D[1..n] such that D[i] = min{ distance(i,j) : A[j] > A[i] }
I have an array of points in numpy: points = rand(dim, n_points) And I want to开发者_JAVA技巧: Calculate all the l2 norm (euclidian distance) between a certain point and all other points
I need to calculate the overlap (amount or yes/no) that two rectangles make on a special x/y grid. The grid is 500x500 but the sides and corners connect (are continuous). So the next point after 499 b