euclidean-distance

I have large data frames consisting of pairs of X and Y coordinates, and wish to calculate the Euclidean distances between consecutive coordinates (minimal size is around 2000 pairs of coordinates). Thus, I want to calculate the distance from row 1 to 2, row 2 to 3, row 3 to 4, etc. This question nicely shows how to calculate the Euclidean distance between the first and last point for track data, but my data are closer to: dff <- structure(list(A = c(0L, 0L, 0L, 0L, 0L, 0L), T = 0:5, X = c(668L, 670L, 672L, 674L, 676L, 678L), Y = c(259L, 259L, 259L, 259L, 259L, 260L), V = c(NA, 0, 0, 0, 0, 0))

I have an Nx3 array that contains N 3D points a1 b1 c1 a2 b2 c2 .... aN bN cN I want to calculate Euclidean distance in a NxN array that measures the Euclidean distance between each pair of 3D points. ( i , j ) in result array returns the distance between (ai,bi,ci) and (aj,bj,cj) . Is it possible to write a code in matlab without loop ? The challenge of your problem is to make a N*N matrix and the result should return in this matrix without using loops. I overcome this challenge by giving suitable dimension to Bsxfun function. By default X and ReshapedX should have the same dimensions when we

I am using H2O with R to calculate the euclidean distance between 2 data.frames: set.seed(121) #create the data df1<-data.frame(matrix(rnorm(1000),ncol=10)) df2<-data.frame(matrix(rnorm(300),ncol=10)) #init h2o h2o.init() #transform to h2o df1.h<-as.h2o(df1) df2.h<-as.h2o(df2) if I use normal calculations, i.e. the first row: distance1<-sqrt(sum((df1[1,]-df2[1,])^2)) And If I use the H2O library: distance.h2o<-h2o.distance(df1.h[1,],df2.h[1,],"l2") print(distance1) print(distance.h2o) The distance1 and distance.h2o are not the same. Does anybody knows why? Thanks!! It seems as if h2o.distance

I have a numpy array like: import numpy as np a = np.array([[1,0,1,0], [1,1,0,0], [1,0,1,0], [0,0,1,1]]) I would like to calculate euclidian distance between each pair of rows. from scipy.spatial import distance for i in range(0,a.shape[0]): d = [np.sqrt(np.sum((a[i]-a[j])**2)) for j in range(i+1,a.shape[0])] print(d) [1.4142135623730951, 0.0, 1.4142135623730951] [1.4142135623730951, 2.0] [1.4142135623730951] [] Is there any better pythonic way to do this since i have to run this code on a huge numpy array? And for completeness, einsum is often referenced for distance calculations. a = np

I'm trying to draw some circles and I was sort of hoping they would intersect with some points, alas... library(maptools) library(plotrix) xy <- matrix(runif(20, min = -100, max = 100), ncol = 2) distance <- spDistsN1(xy, xy[1, ]) plot(0,0, xlim = c(-100, 100), ylim = c(-100, 100), type = "n") points(data.frame(xy)) points(xy[1, 1], xy[1, 2], pch = 16) draw.circle(xy[1, 1], xy[1, 2], radius = distance) The above code does the following: Create 10 random points and choose one (first) point that would serve as an "anchor". Calculate distance from anchor to all other points. This will be our

I have the network representation in a 2D coordinate space. I have an adjacency matrix Adj (which is sparse) and a coordinate matrix with the x,y values of all the points/nodes/vertices in the graph which are drawn. I would like to compute as efficiently as possible the distance between these points. I would like to avoid cycling through the entries in the matrix and computing the pairwise distances one by one. [n, d] = size(coordinate); assert(d == 2); resi = sparse(Adj * diag(1:n)); resj = sparse(diag(1:n) * Adj); res = sparse(zeros(n)); f = find(Adj) res(f) = sqrt((coordinate(resi(f), 1) -

This question already has an answer here: Efficient distance calculation between N points and a reference in numpy/scipy 6 answers Minimum Euclidean distance between points in two different Numpy arrays, not within 5 answers In Matlab there exists the pdist2 command. Given the matrix mx2 and the matrix nx2 , each row of matrices represents a 2d point. Now I want to create a mxn matrix such that (i,j) element represents the distance from i th point of mx2 matrix to j th point of nx2 matrix. I simply call the command pdist2(M,N) . I am looking for an alternative to this in python. I can of