distance

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 would like to calculate the distance matrix of the rows in an array in R using Tanimoto/Jacquard Score as distance matrix. Is it possible to be done? If yes, could you mind to teach me how to do it? 回答1: vegan package has a vegdist function that can calculate, among other things, the Jaccard index. Assuming that's what you're after. It's use is pretty straightforward. library(vegan) data(varespec) vare.dist <- vegdist(varespec, method = "jaccard") Other available methods are method

问题 I'm looking to perform classification on data with mostly categorical features. For that purpose, Euclidean distance (or any other numerical assuming distance) doesn't fit. I'm looking for a kNN implementation for [R] where it is possible to select different distance methods, like Hamming distance. Is there a way to use common kNN implementations like the one in {class} with different distance metric functions? I'm using R 2.15 回答1: As long as you can calculate a distance/dissimilarity matrix

问题 I have created a table with the following columns: Text:varchar(255) Location:geography They contain a few city's from The Netherlands as data (got the coordinates from google maps): Rotterdam - POINT (51.925637 4.493408 4326) Utrecht - POINT (52.055868 5.103149 4326) Nijmegen - POINT (51.801822 5.828247 4326) Breda - POINT (51.542919 4.77356 4326) I want to know the distance between all city's in the DB from Rotterdam, so I perform this query: Select Text, Location, Location.STDistance

问题 I have a graph of N vertices where each vertex represents a place. Also I have vectors, one per user, each one of N coefficients where the coefficient's value is the duration in seconds spent at the corresponding place or 0 if that place was not visited. E.g. for the graph: the vector: v1 = {100, 50, 0 30, 0} would mean that we spent: 100secs at vertex 1 50secs at vertex 2 and 30secs at vertex 4 (vertices 3 & 5 where not visited, thus the 0s). I want to run a k-means clustering and I've

问题 I wonder if Triangle inequality is necessary for the distance measure used in kmeans. 回答1: k-means is designed for Euclidean distance, which happens to satisfy triangle inequality. Using other distance functions is risky, as it may stop converging . The reason however is not the triangle inequality, but the mean might not minimize the distance function . (The arithmetic mean minimizes the sum-of-squares, not arbitrary distances!) There are faster methods for k-means that exploit the triangle

问题 The Problem Imagine I am stood in an airport. Given a geographic coordinate pair, how can one efficiently determine which airport I am stood in? Inputs A coordinate pair (x,y) representing the location I am stood at. A set of coordinate pairs [(a1,b1), (a2,b2)...] where each coordinate pair represents one airport. Desired Output A coordinate pair (a,b) from the set of airport coordinate pairs representing the closest airport to the point (x,y) . Inefficient Solution Here is my inefficient

问题 The Problem Imagine I am stood in an airport. Given a geographic coordinate pair, how can one efficiently determine which airport I am stood in? Inputs A coordinate pair (x,y) representing the location I am stood at. A set of coordinate pairs [(a1,b1), (a2,b2)...] where each coordinate pair represents one airport. Desired Output A coordinate pair (a,b) from the set of airport coordinate pairs representing the closest airport to the point (x,y) . Inefficient Solution Here is my inefficient

问题 I have an input of 36,742 points which means if I wanted to calculate the lower triangle of a distance matrix (using the vincenty approximation) I would need to generate 36,742*36,741*0.5 = 1,349,974,563 distances. I want to keep the pair combinations which are within 50km of each other. My current set-up is as follows shops= [[id,lat,lon]...] def lower_triangle_mat(points): for i in range(len(shops)-1): for j in range(i+1,len(shops)): yield [shops[i],shops[j]] def return_stores_cutoff(points

问题 I'm working on a 2D game that has a huge amount of dynamic entities. For fun's sake, let's call them soldiers, and let's say there are 50000 of them (which I just randomly thought up, it might be much more or much less :)). All these soldiers are moving every frame according to rules - think boids / flocking / steering behaviour. For each soldier, to update it's movement I need the X soldiers that are closest to the one I'm processing. What would be the best spatial hierarchy to store them to