代码搜索：Gradient

找到约 2,951 项符合「Gradient」的源代码

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m gradlfixed.m

function [grad] = gradlfixed(Sigma,indsup,Alpsup,C,Xapp,yapp,pow); %GRADLFIXED Computes the gradient of an upper bound on SVM loss wrt SIGMA^POW % GRAD = GRADLFIXED(SIGMA,INDSUP,ALPSUP,C,XAPP,YAPP,
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www.eeworm.com/read/489040/6481893

m compute_grad.m

function grad = compute_grad(M,options) % compute_grad - compute the gradient of an image using central differences % % grad = compute_grad(M,options); % % 'options' is a structure: % - op
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www.eeworm.com/read/485544/6552650

m demgpot.m

function g = demgpot(x, mix) %DEMGPOT Computes the gradient of the negative log likelihood for a mixture model. % % Description % This function computes the gradient of the negative log of the % uncon
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www.eeworm.com/read/485544/6552656

m gradchek.m

function [gradient, delta] = gradchek(w, func, grad, varargin) %GRADCHEK Checks a user-defined gradient function using finite differences. % % Description % This function is intended as a utility for
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www.eeworm.com/read/485544/6552739

m gbayes.m

function [g, gdata, gprior] = gbayes(net, gdata) %GBAYES Evaluate gradient of Bayesian error function for network. % % Description % G = GBAYES(NET, GDATA) takes a network data structure NET together
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www.eeworm.com/read/485544/6552802

m rbfgrad.m

function [g, gdata, gprior] = rbfgrad(net, x, t) %RBFGRAD Evaluate gradient of error function for RBF network. % % Description % G = RBFGRAD(NET, X, T) takes a network data structure NET together % wi
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www.eeworm.com/read/484356/6585993

m gradlbfixed.m

function [grad] = gradlbfixed(Sigma,indsup,Alpsup,w0,C,Xapp,yapp,pow); %GRADLBFIXED Computes the gradient of an upper bound on SVM loss wrt SIGMA^POW % GRAD = GRADLBFIXED(SIGMA,INDSUP,ALPSUP,W0,C,X
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www.eeworm.com/read/484356/6585996

m gradlfixed.m

function [grad] = gradlfixed(Sigma,indsup,Alpsup,C,Xapp,yapp,pow); %GRADLFIXED Computes the gradient of an upper bound on SVM loss wrt SIGMA^POW % GRAD = GRADLFIXED(SIGMA,INDSUP,ALPSUP,C,XAPP,YAPP,
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www.eeworm.com/read/480059/6676074

m shili24.m

function shili24 subplot(221) z=peaks; ribbon(z) title('F1') subplot(222) [x,y,z]=peaks(15); [dx,dy]=gradient(z,0.5,0.5); contour(x,y,z,10) hold on quiver(x,y,dx,dy) hold off title('F2')
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www.eeworm.com/read/402420/11535195

m gazbgradeval.m

function [nsol, val] = gaZBGradEval(sol,options) % This evaluation function takes in a potential solution and two options % options(3) is the percent of time to perform the gradient heuristic to the
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