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/225759/4792542
m glmgrad.m
function [g, gdata, gprior] = glmgrad(net, x, t)
%GLMGRAD Evaluate gradient of error function for generalized linear model.
%
% Description
% G = GLMGRAD(NET, X, T) takes a generalized linear model da
www.eeworm.com/read/225759/4792610
m glmgrad.m
function [g, gdata, gprior] = glmgrad(net, x, t)
%GLMGRAD Evaluate gradient of error function for generalized linear model.
%
% Description
% G = GLMGRAD(NET, X, T) takes a generalized linear model da
www.eeworm.com/read/215485/4903471
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] =
www.eeworm.com/read/215485/4903849
m maximize_params.m
function CPD = maximize_params(CPD, temp)
% MAXIMIZE_PARAMS Find ML params of an MLP using Scaled Conjugated Gradient (SCG)
% CPD = maximize_params(CPD, temperature)
% temperature parameter is igno
www.eeworm.com/read/197905/5090917
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] =
www.eeworm.com/read/197905/5091295
m maximize_params.m
function CPD = maximize_params(CPD, temp)
% MAXIMIZE_PARAMS Find ML params of an MLP using Scaled Conjugated Gradient (SCG)
% CPD = maximize_params(CPD, temperature)
% temperature parameter is igno
www.eeworm.com/read/165819/5476862
java gradientbar.java
package ai.decision.gui;
import java.awt.*;
import javax.swing.*;
/**
* A utility class that draw a gradient-filled bar on
* a supplied graphics context. Each shade of color on the
* bar
www.eeworm.com/read/162970/5511878
java gradientbar.java
package ai.decision.gui;
import java.awt.*;
import javax.swing.*;
/**
* A utility class that draw a gradient-filled bar on
* a supplied graphics context. Each shade of color on the
* bar
www.eeworm.com/read/346158/3189503
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] =
www.eeworm.com/read/346158/3189881
m maximize_params.m
function CPD = maximize_params(CPD, temp)
% MAXIMIZE_PARAMS Find ML params of an MLP using Scaled Conjugated Gradient (SCG)
% CPD = maximize_params(CPD, temperature)
% temperature parameter is igno