代码搜索：multidimensional
找到约 559 项符合「multidimensional」的源代码
代码结果 559
www.eeworm.com/read/470693/1459323
c p789a.c
// global and local multidimensional array objects are not getting
// constructors called on any dimension, other than the first. Also,
// the destructors are not being called. Seems odd, they proba
www.eeworm.com/read/190666/5175169
c p789a.c
// global and local multidimensional array objects are not getting
// constructors called on any dimension, other than the first. Also,
// the destructors are not being called. Seems odd, they proba
www.eeworm.com/read/340665/3275824
c p789a.c
// global and local multidimensional array objects are not getting
// constructors called on any dimension, other than the first. Also,
// the destructors are not being called. Seems odd, they proba
www.eeworm.com/read/440906/1777456
c p789a.c
// global and local multidimensional array objects are not getting
// constructors called on any dimension, other than the first. Also,
// the destructors are not being called. Seems odd, they proba
www.eeworm.com/read/396844/2407593
m argmax.m
function indices = argmax(v)
% ARGMAX Return as a subscript vector the location of the largest element of a multidimensional array v.
% indices = argmax(v)
%
% Returns the first maximum in the case of
www.eeworm.com/read/388600/2548955
bib paper.bib
@book{cwp,
author = {N[orman] Bleistein and J[] W[] Stockwell and J[ack] K[] Cohen},
title = {Mathematics of Multidimensional Seismic Imaging,
Migration, and Inversion},
publisher = {Sprin
www.eeworm.com/read/386597/2570219
m mds.m
function [new_patterns, targets] = MDS(patterns, targets, params)
% Reshape the data using the multidimensional scaling algorithm
% Inputs:
% patterns - Train patterns
% targets - Train targets
www.eeworm.com/read/474600/6813568
m mds.m
function [new_patterns, targets] = MDS(patterns, targets, params)
% Reshape the data using the multidimensional scaling algorithm
% Inputs:
% patterns - Train patterns
% targets - Train targets
www.eeworm.com/read/415311/11077306
m mds.m
function [new_features, targets] = MDS(features, targets, params, region)
% Reshape the data using the multidimensional scaling algorithm
% Inputs:
% features - Train features
% targets - Train
www.eeworm.com/read/102885/15754255
txt pref_out.txt
M D P R E F
MULTIDIMENSIONAL ANALYSIS OF PREFERENCE DATA
PROGRAM WRITTEN BY DR. J. D. CARROLL AND JIH JIE CHANG