代码搜索：multi-dimensional

找到约 164 项符合「multi-dimensional」的源代码

代码结果 164
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readme

This is the portable cwp library. It needs only cwp.h to compile. Among other things it contains: prime factor fft routines allocation of multi-dimensional arrays complex number manipulation big
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www.eeworm.com/read/305566/3772111

readme

This is the portable cwp library. It needs only cwp.h to compile. Among other things it contains: prime factor fft routines allocation of multi-dimensional arrays complex number manipulation big
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www.eeworm.com/read/305566/3773094

readme

This is the portable cwp library. It needs only cwp.h to compile. Among other things it contains: prime factor fft routines allocation of multi-dimensional arrays complex number manipulation big
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www.eeworm.com/read/303435/3811717

readme

This is the portable cwp library. It needs only cwp.h to compile. Among other things it contains: prime factor fft routines allocation of multi-dimensional arrays complex number manipulation big
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www.eeworm.com/read/202908/15369809

h arch.h

#ifndef ARCH_H #define ARCH_H /* Integer Multi-Dimensional Interpolation */ /* * Copyright 2000 Graeme W. Gill * * This material is licenced under the GNU GENERAL PUBLIC LICENCE :- * see the Lice
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www.eeworm.com/read/13871/284221

m sampgauss.m

function [x]=sampgauss(m,C,N) % % x=SAMPGAUSS(m,C,N) % % samples N-times from an multi-dimensional gaussian distribution % with covariance matrix C and mean m. Dimensionality is implied % in the
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www.eeworm.com/read/16731/685878

m sampgauss.m

function [x]=sampgauss(m,C,N) % % x=SAMPGAUSS(m,C,N) % % samples N-times from an multi-dimensional gaussian distribution % with covariance matrix C and mean m. Dimensionality is implied % in the
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www.eeworm.com/read/444599/7611016

m mdsfast.m

function [points]=mdsFast(d,dim) % --- function mdsFast for Multi-Dimensional Scaling % Written by Michael D. Lee. % Lee recommends metric=2, iterations=50, learnrate=0.05. [n, check] = size(d);
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www.eeworm.com/read/243093/12964939

m mmds.m

function [X,s,U,V]=mmds(D) % %function [X,s,U,V]=mmds(D) % %PURPOSE % %To compute principal coordinates (linear Metric Multi-Dimensional Scaling) % %INPUTS % % D (matrix) NxN matrix of dissimilaritie
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www.eeworm.com/read/150225/12304151

m gaussrnd.m

function [x]=gaussrnd(m,C,N) % % x=GAUSSRND(m,C,N) % % samples N-times from an multi-dimensional gaussian distribution % with covariance matrix C and mean m. Dimensionality is implied % in the me
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