代码搜索：Matrix
找到约 10,000 项符合「Matrix」的源代码
代码结果 10,000
www.eeworm.com/read/319604/13448341
m anal1.m
function [E,Nc,mCe,mC,T] = anal1(M,D,st,s);
%
% Ph.D. Thesis
% Copyright by Leandro Nunes de Castro
% March, 2000
% Immune Network (iNet) - Description in iNet.doc
% Function determines the Mi
www.eeworm.com/read/319604/13448353
m anal3.m
function [E,Nc,mC,T] = anal3(M,D,st,s);
%
% Ph.D. Thesis
% Copyright by Leandro Nunes de Castro
% March, 2000
% Immune Network (iNet) - Description in iNet.doc
% Function determines the Minima
www.eeworm.com/read/319604/13448361
m analysis.m
function [E,bE,Nc,mCe,mC,T,U] = analysis(M,D,st,s);
%
% Ph.D. Thesis
% Copyright by Leandro Nunes de Castro
% March, 2000
% Immune Network (iNet) - Description in iNet.doc
% Function determine
www.eeworm.com/read/319138/13459748
txt doub.txt
function[K,S]=doub(A,C,Q,R)
%DOUBLE.M
%function[K,S]=double(A,C,Q,R)
% This program uses the "doubling algorithm" to solve the
% Riccati matrix difference equations associa
www.eeworm.com/read/318947/13465965
m latentlssvm.m
function [zt,model] = latentlssvm(varargin)
% Calculate the latent variables of the LS-SVM classifier at the given test data
%
% >> Zt = latentlssvm({X,Y,'classifier',gam,sig2,kernel}, {alpha,b}, Xt)
www.eeworm.com/read/318488/13477559
m kernel.m
function krn = kernel (fwhm, style)
% KERNEL Create a 2D kernel with the specified full-width half-maximum
%
% kern = kernel (fwhm [, style])
%
% generates a square matrix containing a 2-D functio
www.eeworm.com/read/318365/13480379
m davis.m
function [Zp,Sp] = davis(data,x0,model,a,b,c,A)
% [Zp,Sp] = davis(data,x0,model,a,b,c)
%
% Punctual Kriging: Davis' method
%
% ref: Davis, J.C. (1986) Statistics and Data Analysis in Geology,
www.eeworm.com/read/317326/13505909
m sa_ex7_12.m
% MUSIC AOA estimation for a M = 6 element array with noise variance = .1
% use time averages instead of expected values by assuming ergodicity of the mean and
% ergodicity of the correlation.
www.eeworm.com/read/317326/13505925
m sa_ex7_14.m
% root-Min-Norm AOA estimation for a M = 4 element array with noise variance = .1
% use time averages instead of expected values by assuming ergodicity of the mean and
% ergodicity of the correlati
www.eeworm.com/read/316991/13512965
txt 支持向量机(svm)实现的分类算法源码[matlab].txt
支持向量机(SVM)实现的分类算法源码[matlab]
程序代码： (代码标记 [code]...[/code] )
function [iter, optCond, time, w, gamma] = lsvm(A,D,nu,tol,maxIter,alpha, ...
perturb,normalize);
% LSVM Langrangian Support Vector