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/13871/284683

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
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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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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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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] =
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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
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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] =
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h tbgrad.h

// // tbgrad.h --- definition of the abstract two-electron gradient builder // // Copyright (C) 1996 Limit Point Systems, Inc. // // Author: Edward Seidl // Maintainer: LPS // // Thi
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wedding mint

Star 1314089316 31.000 1229874277 3.000 1332765556 100.000 1330013298 270.000 1330013299 0.00 inches 1398042482 68.000 1398043233 1.37 inches 1935963971 Gradient: 3/0.000 0 3 1 178 255 169 255
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sparkle

Star 1314089316 13.000 1229874277 86.000 1332765556 100.000 1330013298 166.000 1330013299 0.00 inches 1398042482 330.000 1398043233 1.44 inches 1935963971 Gradient: 1/0.000 0 3 1 255 255 255 2
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the sun

Star 1314089316 12.000 1229874277 22.000 1332765556 100.000 1330013298 166.000 1330013299 0.00 inches 1398042482 224.000 1398043233 1.44 inches 1935963971 Gradient: 2/0.000 0 3 1 255 191 0 255