Neural Networks: A Comprehensive Foundation, 2e book Companion Software: y=proymayor(x,threshold,relax) - File Exchange

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Highlights from
Neural Networks: A Comprehensive Foundation, 2e book Companion Software

A=bpm_dec_bnds(W1, b1, W2, b2...
B=colmult(A, vec)
B=sgn(A) function B=sgn(A)
C=mk_data(pats) function C=mk_data(pats)
D=gha_chopstak(P,rm,cm) function D=gha_chopstak(P,rm,cm)
I=gha_recompose(coeffs,W,mval) I=recompose(coeffs,W,mval)
I=gha_unchopst(X,r,c,rm,cm) function I=gha_unchopst(X,r,c,rm,cm)
K=gha_dispwe(W,j) function K=gha_dispwe(W,j)
M=thresh(A,ll,ul)
W=gha(P,epochs,m,beta,W) function W=gha(P,epochs,m,beta,W)
W=gha_getweights(Image,epochs...
W=hop_stor(P) function W=hop_stor(P)
[P, flip]=hop_flip(P, prob, s...
[W1, b1, W2, b2, ep_err, init...
[W_out, p_out]=som_1d(P, nsof...
[W_out, p_out]=som_2d(P, nsof...
[cor, uncor, O, dec]=bpm_test...
[pesos,vect,b]=svm_rbf2(datos... % [pesos,vect,b]=svm_rbf(datos,escala,niter,cad,restric);
[s, count, M]=hop_test(W,x,up...
b=phi(a)
b=phi_d(a)
c=bsb(x,beta,multi) function c=bsb(x,beta)
coeffs=gha_getcoeffs(Orig,W,p... function coeffs=gha_getcoeffs(Orig,W,plot_flg)
gha_quantcoeffs(C,W,O,n)
hop_plotdig(P,r,c,stri) function hop_plotdig(P,r,c)
hop_plotpats(P) function hop_plotpats(P)
out=pl_circ(cen, radius, res,... function out=pl_circ(cen, radius, res, plot_flag)
p=rbf_correct(y,d) p - percent correct in two class problem
pim(Z) function pim(Z)
rbf(x, t, d, sigma, lam)
rbf_db(w, t, sigma, st_sz)
rbf_mkGF(x,t)
rbf_test(w, x, t, sigma)
shuffle(n1,n2,n3,n4,n5)
som_pl_map(A,p1,p2, P1, P2) function som_pl_map(A,p1,p2)
svm_dec_bnd(pesos, vect, b, e... function svm_dec_bnd(pesos, vect, b, escala)
svm_test(data, pesos, vect, b...
y=proymayor(x,threshold,relax)
y=proymenor(x,threshold,relax)
hop_demo.m
ica.m
som_2d_demo.m
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from Neural Networks: A Comprehensive Foundation, 2e book Companion Software by Simon Haykin
Companion Software for Neural Networks: A Comprehensive Foundation, 2e book

y=proymayor(x,threshold,relax)

function y=proymayor(x,threshold,relax)
%
% Relaxed projection onto x>=threshold : P+relax(I-P)=(1-relax)P+relaxI
%
% AAR, 1/8/97

y=x.*(x>=threshold)+threshold*ones(size(x)).*(x<threshold);
y=(1-relax)*y+relax*x;

Highlights from Neural Networks: A Comprehensive Foundation, 2e book Companion Software

Highlights from
Neural Networks: A Comprehensive Foundation, 2e book Companion Software