代码搜索：Transformations
找到约 1,265 项符合「Transformations」的源代码
代码结果 1,265
www.eeworm.com/read/251835/12317525
m crspread.m
function [ul,dl] = crspread(u,quadnum,cr,Q)
%CRSPREAD Transform points to every embedding in CR formulation.
% Each quadrilateral has an associated embedding of the
% prevertices. These embeddi
www.eeworm.com/read/213240/15139934
m dissim.m
function w = dissim(a,ttype,p)
%DISSIM Dissimilarity transformations
%
% W = DISSIM([],TTYPE,PAR)
%
% Define a mapping for a 1-to-1 transformation.
% Possible transformations TTYPE are:
%
% T
www.eeworm.com/read/370047/9621442
m nrbtform.m
function nurbs = nrbtform(nurbs,tmat)
%
% Function Name:
%
% nrbtform - Apply transformation matrix to the NURBS.
%
% Calling Sequence:
%
% tnurbs = nrbtform(nurbs,tmatrix);
%
% Par
www.eeworm.com/read/235380/14072940
m nrbtform.m
function nurbs = nrbtform(nurbs,tmat)
%
% Function Name:
%
% nrbtform - Apply transformation matrix to the NURBS.
%
% Calling Sequence:
%
% tnurbs = nrbtform(nurbs,tmatrix);
%
% Par
www.eeworm.com/read/204456/15339235
m dissim.m
function w = dissim(a,ttype,p)
%DISSIM Dissimilarity transformations
%
% W = DISSIM([],TTYPE,PAR)
%
% Define a mapping for a 1-to-1 transformation.
% Possible transformations TTYPE are:
%
% T
www.eeworm.com/read/432664/8583676
mht usaco 1_2_2 transformations 题解_leokan的blog.mht
From:
Subject: =?gb2312?B?VVNBQ08gMS4yLjIgVHJhbnNmb3JtYXRpb25zIMziveJfbGVva2FutcRibG9n?=
Date: Wed, 30 Jan 2008 16:34:19 +0800
MIME-Version: 1.0
Content-Type: mu
www.eeworm.com/read/428849/8834901
m~ contents.m~
% Linear transformations for feature extraction.
%
% lda - Linear Discriminant Analysis.
% linproj - Linear data projection.
% pca - Principal Component Analysis.
%
% About: Statistica
www.eeworm.com/read/382131/9047017
txt transform.txt
The matrices used to describe model transformations are affine 4x4 matrices
which are D3D style, row major with translations in the 4th row.
1 2 3 4
5 6 7 8
9 10 11 12
13 14 15 16
The tran
www.eeworm.com/read/362246/10010431
m~ contents.m~
% Linear transformations for feature extraction.
%
% lda - Linear Discriminant Analysis.
% linproj - Linear data projection.
% pca - Principal Component Analysis.
%
% About: Statistica