代码搜索：Matrix
找到约 10,000 项符合「Matrix」的源代码
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www.eeworm.com/read/441245/7672715
m iscolumn.m
%ISCOLUMN Checks whether the argument is a column array
%
% [OK,Y] = ISCOLUMN(X)
%
% INPUT
% X Array: an array of entities such as numbers, strings or cells
%
% OUTPUT
% OK 1 if X is a column
www.eeworm.com/read/441245/7673057
m ldc.m
%LDC Linear Bayes Normal Classifier (BayesNormal_1)
%
% [W.R,S,M] = LDC(A,R,S,M)
% W = A*LDC([],R,S,M);
%
% INPUT
% A Dataset
% R,S Regularization parameters, 0
www.eeworm.com/read/441245/7673066
m fisherm.m
%FISHERM Optimal discrimination linear mapping (Fisher mapping, LDA)
%
% W = FISHERM(A,N,ALF)
%
% INPUT
% A Dataset
% N Number of dimensions to map to, N < C, where C is the number of classes
www.eeworm.com/read/441245/7673228
m distm.m
%DISTM Compute square Euclidean distance matrix
%
% D = DISTM(A,B)
%
% INPUT
% A,B Datasets or matrices; B is optional, default B = A
%
% OUTPUT
% D Square Euclidean distance dataset or
www.eeworm.com/read/441245/7673292
m setcost.m
%SETCOST Reset classification cost matrix of mapping
%
% W = SETCOST(W,COST,LABLIST)
%
% The classification cost matrix of the dataset W is reset to COST.
% W has to be a trained classifier. CO
www.eeworm.com/read/441245/7673433
m covm.m
%COVM Compute covariance matrix for large datasets
%
% C = COVM(A)
%
% Similar to C = COV(A) this routine computes the covariance matrix
% for the datavectors stored in the rows of A. No large int
www.eeworm.com/read/441178/7675096
m arstepfit.m
function [w, A, C, sbc, fpe, th]=arfit(v, pmin, pmax, selector, no_const)
%ARFIT Stepwise least squares estimation of multivariate AR model.
%
% [w,A,C,SBC,FPE,th]=ARFIT(v,pmin,pmax) produces estimat
www.eeworm.com/read/441015/7677934
m parzenpnnlearn.m
function net = parzenPNNlearn(samples,classification,center)
% PARZENPNNLEARN Creates a Parzen probabilistic neural network
%
% This funcion generates a Parzen PNN (Probabilistic Neural Network) fro
www.eeworm.com/read/440842/7680335
m multilogit.m
function results = multilogit(y,x,beta0,maxit,tol);
% PURPOSE: implements multinomial logistic regression
% Pr(y_i=j) = exp(x_i'beta_j)/sum_l[exp(x_i'beta_l)]
% where:
% i = 1,2,...,nobs
www.eeworm.com/read/440750/7682132
m rotqc2mc.m
function mc=rotqc2mc(qc)
%ROTQC2MC converts a matrix of complex quaternion vectors to quaternion matrices
% Inputs:
%
% QC(2m,n) mxn matrix of real quaternion vectors (each 2x1)
%
% Outpu