代码搜索：Gradient
找到约 2,951 项符合「Gradient」的源代码
代码结果 2,951
www.eeworm.com/read/244945/12829473
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
%
www.eeworm.com/read/244800/12842932
m hill_obj.m
function [f,df]=hill_obj(x,dims,ii,dd,pars);
%
% computes the objective function and gradient of the non-convex formulation of MVU.
%
% copyright by Kilian Q. Weinberger, 2006
%
%
%
% This file is
www.eeworm.com/read/143706/12849798
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
www.eeworm.com/read/143706/12849852
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
www.eeworm.com/read/329331/12960436
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
%
www.eeworm.com/read/140851/13059143
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
www.eeworm.com/read/138798/13212208
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
www.eeworm.com/read/147529/5728639
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
%
www.eeworm.com/read/147529/5728849
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
%
www.eeworm.com/read/140847/5779135
m mixexp_graddesc.m
%%%%%%%%%%
function [theta, eta] = mixture_of_experts(q, data, num_iter, theta, eta)
% MIXTURE_OF_EXPERTS Fit a piecewise linear regression model using stochastic gradient descent.
% [theta, eta] =