代码搜索：Gradient

找到约 2,951 项符合「Gradient」的源代码

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www.eeworm.com/read/337307/12377440

m definev.m

function [v,dv]= definev(g,x,l,u); %DEFINEV Scaling vector and derivative % % [v,dv]= DEFINEV(g,x,l,u) returns v, distances to the % bounds corresponding to the sign of the gradient g, where %
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www.eeworm.com/read/233016/14173541

m bpdn_obj.m

function [obj,grad,hess] = BPDN_obj( x ) % [obj,grad,hess] = BPDN_obj( x ) % computes the objective value, gradient and diagonal Hessian % of the linear function lambda e'x, where lamb
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www.eeworm.com/read/130383/14196228

m gradwfixed.m

function [grad] = gradwfixed(Sigma,indsup,Alpsup,C,Xapp,yapp,Sigmaold,pow); %GRADWFIXED Computes the gradient of an upper bound on SVM loss wrt SIGMA^POW % GRAD = GRADWFIXED(SIGMA,INDSUP,ALPSUP,C,X
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www.eeworm.com/read/220289/14843819

m olgd.m

function [net, options, errlog, pointlog] = olgd(net, options, x, t) %OLGD On-line gradient descent optimization. % % Description % [NET, OPTIONS, ERRLOG, POINTLOG] = OLGD(NET, OPTIONS, X, T) uses on
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www.eeworm.com/read/220289/14843839

m gpgrad.m

function g = gpgrad(net, x, t) %GPGRAD Evaluate error gradient for Gaussian Process. % % Description % G = GPGRAD(NET, X, T) takes a Gaussian Process data structure NET % together with a matrix X of
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www.eeworm.com/read/214095/15113202

m definev.m

function [v,dv]= definev(g,x,l,u); %DEFINEV Scaling vector and derivative % % [v,dv]= DEFINEV(g,x,l,u) returns v, distances to the % bounds corresponding to the sign of the gradient g, where %
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www.eeworm.com/read/212307/15160127

m olgd.m

function [net, options, errlog, pointlog] = olgd(net, options, x, t) %OLGD On-line gradient descent optimization. % % Description % [NET, OPTIONS, ERRLOG, POINTLOG] = OLGD(NET, OPTIONS, X, T) uses on
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www.eeworm.com/read/212307/15160147

m gpgrad.m

function g = gpgrad(net, x, t) %GPGRAD Evaluate error gradient for Gaussian Process. % % Description % G = GPGRAD(NET, X, T) takes a Gaussian Process data structure NET % together with a matrix X of
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www.eeworm.com/read/13871/284531

m netgrad.m

function g = netgrad(w, net, x, t) %NETGRAD Evaluate network error gradient for generic optimizers % % Description % % G = NETGRAD(W, NET, X, T) takes a weight vector W and a network data % stru
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www.eeworm.com/read/13871/284604

m olgd.m

function [net, options, errlog, pointlog] = olgd(net, options, x, t) %OLGD On-line gradient descent optimization. % % Description % [NET, OPTIONS, ERRLOG, POINTLOG] = OLGD(NET, OPTIONS, X, T) uses
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