Documents about Euclidean Distance

  • 1 Pages

    Program1_A08

    Uni. Worcester, A 08

    Excerpt: ... CS2303 Systems Programming Concepts Program 1 Due: September 5, 2008 at 11:59 p.m. Functions and Basic Variable Types in C A08 12 points The purpose of this programming assignment is to familiarize the student with C syntax, the use of functions in C, and working with a variety of C variable types. You are to write two C functions where all the formal parameters and the returned values are doubles: 1. A function that returns the log3 (x). 2. A function that computes the Euclidean distance between two points in a three dimensional space. The function will take as input two points specified by the coordinates (x1,y1, z1) and (x2, y2, z2). Main Assignment This assignment is similar in flow to the Lab 1 program. Assume the first integer read with scanf specifies the number of coordinate pairs to read in. Your program should only read in one pair of coordinates each time scanf executes. However, the coordinates are to be read in as integers. For each pair of points, compute and print out (using printf) the Eucli ...

  • 53 Pages

    lec4-clustering

    Lehigh, CSE 450

    Excerpt: ... rity of two elements in a set is determined, e.g. Euclidean Distance Manhattan Distance Inner Product Space Maximum Norm Or any metric you define over the space. Types of Algorithms Hierarchical Clustering vs. Partitional Clustering Hierarchical Clustering Builds or breaks up a hierarchy of clusters. Partitional Clustering Partitions set into all clusters simultaneously. Partitional Clustering Partitions set into all clusters simultaneously. K-Means Clustering Super simple Partitional Clustering Choose the number of clusters, k Choose k points to be cluster centers Then. K-Means Clustering iterate { Compute distance from all points to all kcenters Assign each point to the nearest k-center Compute the average of all points assigned to all specific k-centers Replace the k-centers with the new averages } But! The complexity is pretty high: k * n * O ( distance metric ) * num (iterations) Moreover, it can be necessary ...

  • 3 Pages

    Lab3

    Wilfrid Laurier, ENEL 563

    Excerpt: ... result. Note that synchronized averaging is a type of ensemble averaging. Kamath et al. [1] applied synchronized averaging to improve the SNR of cortical evoked potentials related to electrical and mechanical stimulation of the esophagus. Although improvement in SNR was obtained in some experiments, they also observed that habituation 1 took place as the number of stimuli was increased beyond a certain limit, and that the use of the ERPs obtained after habituation in averaging led to a reduction in the SNR. Kamath et al. estimated the SNR as follows: Noise power: 2 1 = NT (M 1) 2 y = M N k=1 n=1 [yk (n) y (n)]2 . 2 . M (3) Signal power: 1 NT N n=1 [(n)]2 y 2 y . 2 (4) SNR = Here, T = 0.001 s is the sampling interval. (5) Kamath et al. also computed the Euclidean distance between the original ERP signals and the averaged signal obtained as 1 D= M M k=1 N n=1 [yk (n) y (n)]2 . (6) Laboratory Exercise Copy the data les E11 to E2424 and the MATLAB pro ...