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txt qrbksb.txt

Sub QRBKSB(A(), N, Q(), B(), X()) For I = 1 To N Sum = 0# For J = 1 To N Sum = Sum + Q(I, J) * B(J) Next J X(I) = Sum Next I For I = N T
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txt chobsb.txt

Sub CHOBSB(A(), N, D(), B()) For I = 1 To N Sum = B(I) For J = 1 To I - 1 Sum = Sum - A(I, J) * B(J) Next J B(I) = Sum Next I For I = N
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www.eeworm.com/read/141385/13013828

inl linearequation.inl

//LinearEquation.inl 线性方程(组)求解函数(方法)定义 // Ver 1.0.0.0 // 版权所有(C) 何渝, 2002 // 最后修改: 2002.5.31 #ifndef _LINEAREQUATION_INL #define _LINEAREQUATION_INL //全选主元高斯消去法 template int L
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www.eeworm.com/read/141202/13033960

m sima1.m

function [nw,a1,i] = sima1(w,p,lr,rho,pf) %SIMA1 ART1 simulation function. % Each input vector is presented to the network one at a time. % (See COMPET, HARDLIM) % % [NW,A1,
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www.eeworm.com/read/141142/13035951

cpp 算法(strassen和strassen混合算法).cpp

//作者：建麟 email blacken1008@163.com #include #include #include #include #include //*****************the declaration of method void Allot(flo
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m qda.m

function [f, c, post] = qda(X, k, prior, est, nu) %QDA Quadratic Descriminant Analysis. % F = QDA(X, K, PRIOR) returns a quadratic discriminant analysis % object F based on the feature matrix X, c
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m logda.m

function [f, iter, dev, hess] = logda(X, k, prior, maxit, est) %LOGDA Logistic Discriminant Analysis. % F = LOGDA(X, K, PRIOR) returns a logistic discriminant analysis % object F based on the feat
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m classify.m

function [c, post] = classify(f, X, opt) %LOGDA/CLASSIFY Categorise new data with logistic discriminants. % [C, POST] = CLASSIFY(F, X) classifies the rows of the n by p % feature matrix X given th
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m classifier.m

function [f, G, w] = classifier(X, k, prior) %CLASSIFIER Generic discriminant analysis object. % F = CLASSIFIER(X, K, PRIOR) returns a generic discriminant % analysis object based on the feature m
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m classify.m

function [c, post] = classify(f, X); %CLASSIFIER/CLASSIFY Categorise new data with CLASSIFIER object. % [C, POST] = CLASSIFY(F, X) classifies the rows of the n by p % feature matrix X given the CL
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