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m l2_distance.m

function d = L2_distance(a,b,df) % L2_DISTANCE - computes Euclidean distance matrix % % E = L2_distance(A,B) % % A - (DxM) matrix % B - (DxN) matrix % df = 1, force diagonals to be ze
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cpp globalfunc.cpp

// GlobalFunc.cpp: implementation of the CGlobalFunc class. // ////////////////////////////////////////////////////////////////////// /* This file is created by Shiguang Shan at 01.06.2002 to
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html 145-148.html

Learn Encryption Techniques with BASIC and C++:Transposition-based Monoalphabetic Substitution
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m pca.m

%P returns the load matrix of x; %T returns the score matrix of x; %W is the convert matrix from X to T; function [Th,P,W] = pca(x,maxrank) [m,n] = size(x); if nargin == 2, i = min([n, m, max
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cpp lu_decompound_for_c++.cpp

#include #include using namespace std; #define N 100 #define OK 1 #define ERROR 0 #define OVERFLOW -1 typedef int Status; int n; int i,j,r,k; float a[N][N],b[N],l[
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m l2_distance.m

function d = L2_distance(a,b,df) % L2_DISTANCE - computes Euclidean distance matrix % % E = L2_distance(A,B) % % A - (DxM) matrix % B - (DxN) matrix % df = 1, force diagonals to be zero; 0 (
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asv l2_distance.asv

function d = L2_distance(a,b,df) % L2_DISTANCE - computes Euclidean distance matrix % % E = L2_distance(A,B) % % A - (DxM) matrix % B - (DxN) matrix % df = 1, force diagonals to be zero; 0 (
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cpp disfour.cpp

// DisFour.cpp: implementation of the CDisFour class. // ////////////////////////////////////////////////////////////////////// #include "stdafx.h" #include "DisFour.h" #include "../RSet.h" #i
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m 基于统计模型的人脸识别pca程序.m

% Face recognition by Santiago Serrano clear all close all clc % number of images on your training set. M=10; %Chosen std and mean. %It can be any number that it is close to the std and mean
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m bootrsp.m

function[out]=bootrsp(in,B) % out=bootrsp(in,B) % % Bootstrap resampling procedure. % % Inputs: % in - input data % B - number of bootstrap resamples (default B=1)
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