代码搜索：gdata

找到约 456 项符合「gdata」的源代码

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www.eeworm.com/read/413912/11137311

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
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www.eeworm.com/read/413912/11137415

m mlpgrad.m

function [g, gdata, gprior] = mlpgrad(net, x, t) %MLPGRAD Evaluate gradient of error function for 2-layer network. % % Description % G = MLPGRAD(NET, X, T) takes a network data structure NET together
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www.eeworm.com/read/291714/8402231

pas atxclipboard.pas

unit ATxClipboard; interface uses ATxCodepages; function SCopyToClipboard(const S: AnsiString; Enc: TATEncoding = vencANSI): Boolean; function SCopyToClipboardW(const S: WideString): Bool
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www.eeworm.com/read/388251/8623043

hpp deltaop.hpp

/** * @file DeltaOp.hpp * This is a class to apply the Delta operator (differences on ground-related data) to GNSS data structures. */ #ifndef DELTAOP_HPP #define DELTAOP_HPP //=================
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www.eeworm.com/read/388251/8623362

hpp processingclass.hpp

/** * @file ProcessingClass.hpp * This is an abstract base class for objects processing GNSS Data Structures. */ #ifndef PROCESSING_CLASS_GPSTK #define PROCESSING_CLASS_GPSTK //=================
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www.eeworm.com/read/429878/8783904

htm gbayes.htm

Netlab Reference Manual gbayes gbayes Purpose Evaluate gradient of Bayesian error function for network. Synopsis
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www.eeworm.com/read/177674/9442556

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/177674/9442676

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/176823/9483237

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/176823/9483368

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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