代码搜索：evaluate

找到约 3,619 项符合「evaluate」的源代码

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m gaussian_prob.m

function p = gaussian_prob(x, m, C, use_log) % GAUSSIAN_PROB Evaluate a multivariate Gaussian density. % p = gaussian_prob(X, m, C) % p(i) = N(X(:,i), m, C) where C = covariance matrix and each COL
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www.eeworm.com/read/153407/12035560

m nand.m

function y=nand(x1,x2) %NAND Equivalent to the NOT(AND) functions. % NAND(X1,X2) returns NOT(AND(X1,X2)). % % Input arguments: % X1,X2 - the pair of numbers to evaluate (double) %
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www.eeworm.com/read/341346/12090116

m gcvfctn.m

function G = gcvfctn(h, s2, fc, rss0, dof0) %GCVFCTN Evaluate generalized cross-validation function. % % gcvfctn(h, s2, fc, rss0, dof0) is the value of the GCV function % for ridge regres
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www.eeworm.com/read/253950/12173880

htm glmhess.htm

Netlab Reference Manual glmhess glmhess Purpose Evaluate the Hessian matrix for a generalised linear model. Synopsis
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www.eeworm.com/read/253950/12174174

htm netgrad.htm

Netlab Reference Manual netgrad netgrad Purpose Evaluate network error gradient for generic optimizers Synopsis
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www.eeworm.com/read/150905/12250091

htm glmhess.htm

Netlab Reference Manual glmhess glmhess Purpose Evaluate the Hessian matrix for a generalised linear model. Synopsis
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www.eeworm.com/read/150905/12250368

htm netgrad.htm

Netlab Reference Manual netgrad netgrad Purpose Evaluate network error gradient for generic optimizers Synopsis
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www.eeworm.com/read/251835/12317334

m eval.m

function w = eval(f,z) %EVAL Evaluate a composite map. % EVAL(F,Z) evaluates the composite map F at Z. % Copyright 2001 by Toby Driscoll. % $Id: eval.m 169 2001-07-20 15:19:13Z driscoll $
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www.eeworm.com/read/224759/14568404

m nand.m

function y=nand(x1,x2) %NAND Equivalent to the NOT(AND) functions. % NAND(X1,X2) returns NOT(AND(X1,X2)). % % Input arguments: % X1,X2 - the pair of numbers to evaluate (double) %
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www.eeworm.com/read/13871/284256

m gaussian_prob.m

function p = gaussian_prob(x, m, C, use_log) % GAUSSIAN_PROB Evaluate a multivariate Gaussian density. % p = gaussian_prob(X, m, C) % p(i) = N(X(:,i), m, C) where C = covariance matrix and each COL
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