euclidean-distance

问题 I am trying to calculate the height of a person using Kinect. Here is how I calculate the height (this code is not very sophisticated, it is only for testing purposes): double h2s = Math.Sqrt(Math.Pow(headX - shoulderCenterX, 2) + Math.Pow(headY - shoulderCenterY, 2)+ Math.Pow(headZ - shoulderCenterZ, 2)); double s2hip = Math.Sqrt(Math.Pow(shoulderCenterX - hipX, 2) + Math.Pow(shoulderCenterY - hipY, 2) + Math.Pow(shoulderCenterZ - hipZ, 2)); double hip2Lhip = Math.Sqrt(Math.Pow(hipX -

问题 My assignment is to create main class in which I initialize the value of any point to be at (0,0,0) and to be able to access and mutate all three values (x,y,z) individually. To do this I have used getters and setters. My next task is to create a method within my main class (which I shall call "distanceTo") that calculates the distance between two points. How do I go about creating the method " distanceTo " that calculates the distance between two points by taking in the x,y,z coordinates ? I

问题 My assignment is to create main class in which I initialize the value of any point to be at (0,0,0) and to be able to access and mutate all three values (x,y,z) individually. To do this I have used getters and setters. My next task is to create a method within my main class (which I shall call "distanceTo") that calculates the distance between two points. How do I go about creating the method " distanceTo " that calculates the distance between two points by taking in the x,y,z coordinates ? I

问题 I want to calculate the euclidean distance between two vectors (or two Matrx rows, doesn't matter). Is there a good function for that in OpenCV? 回答1: yes. Mat a,b; // num of rows/cols/channels does not matter, they just have to be equal for both double dist = norm(a,b,NORM_L2); 回答2: Source: OpenCV, C++: Distance between two points Mat pts1(nPts, 1, CV_8UC2), pts2(nPts, 1, CV_8UC2); // populate them Mat diffPts = pts1-pts2; Mat ptsx, ptsy; // split your points in x and y vectors. maybe

问题 I have the following Pandas DataFrame: In [31]: import pandas as pd sample = pd.DataFrame({'Sym1': ['a','a','a','d'],'Sym2':['a','c','b','b'],'Sym3':['a','c','b','d'],'Sym4':['b','b','b','a']},index=['Item1','Item2','Item3','Item4']) In [32]: print(sample) Out [32]: Sym1 Sym2 Sym3 Sym4 Item1 a a a b Item2 a c c b Item3 a b b b Item4 d b d a and I want to find the elegant way to get the distance between each Item according to this distance matrix: In [34]: DistMatrix = pd.DataFrame({'a': [0,0

问题 Let's say I have two 4-dimensional vectors (i.e. a and b) as follows: a = {a1, a2, a3, a4} b= {b1, b2, b3, b4} How do I compute the Euclidean distance between these vectors? 回答1: The euclidian distance calculus is independent of dimensions. In your case, the euclidian distance between a and b can be written as: d(a,b) = sqrt(sum_{i=1}^{4} (a[i] - b[i])^2). Or, more specifically: d(a,b) = sqrt( (a1-b1)^2 + (a2-b2)^2 + (a3-b3)^2 + (a4-b4)^2 ). 回答2: public static float ndistance(float[] a, float

问题 BigList = rand(20, 3) LittleList = rand(5, 3) I'd like to find for each row in the big list the 'closest' row in the little list, as defined by the euclidean norm (i.e. sum of squared distances between the corresponding values in the k=3 dimension). I can see how to do this using two loops, but it seems like there ought to be a better way to do this using built in matrix operations. 回答1: Approach #1 There is a built in MATLAB function pdist2 which finds "Pairwise distance between two sets of

问题 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 distance. I then want to create a matrix over which I can execute multiple random walks. Right now, my code is as follows: % clear % clc % close all % % load tea.mat; images = Input.X; M = zeros(size(images, 2), size (images, 2)); for i = 1:size(images, 2) for j = 1:size(images, 2) normImageTemp =

问题 I have two pandas dataframes d1 and d2 that look like these: d1 looks like: output value1 value2 value2 1 100 103 87 1 201 97.5 88.9 1 144 54 85 d2 looks like: output value1 value2 value2 0 100 103 87 0 201 97.5 88.9 0 144 54 85 0 100 103 87 0 201 97.5 88.9 0 144 54 85 The column output has a value of 1 for all rows in d1 and 0 for all rows in d2. It's a grouping variable. I need to find euclidean distance between each rows of d1 and d2 (not within d1 or d2). If d1 has m rows and d2 has n

问题 I'm trying to find the euclidean distance between two points, confined by an irregular polygon. (ie. the distance would have to be calculated as a route through the window given) Here is an reproducible example: library(spatstat) #Simple example of a polygon and points. ex.poly <- data.frame(x=c(0,5,5,2.5,0), y=c(0,0,5,2.5,5)) points <- data.frame(x=c(0.5, 2.5, 4.5), y=c(4,1,4)) bound <- owin(poly=data.frame(x=ex.poly$x, y=ex.poly$y)) test.ppp <- ppp(x=points$x, y=points$y, window=bound)