代码搜索：eigenvector

找到约 273 项符合「eigenvector」的源代码

代码结果 273
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www.eeworm.com/read/356171/10235542

m flda.m

function [F,WC,BC,wilks,V,e,vexp,z,zc]=flda(data,nclass,U,centroid,phi,scale) % Fuzzy linear discriminant analysis % Input: % data : data matix data(ndata,ndim) % nclass : no. cla
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www.eeworm.com/read/356058/10237665

res p103.res

Global coordinates Node 1 0.0000E+00 0.0000E+00 Node 2 0.0000E+00 -0.1000E+01 Node 3 0.8000E+00 0.0000E+00 Node 4 0.8000E+00 -0.1000E+01 Node 5
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www.eeworm.com/read/356058/10237828

res p104.res

Global coordinates Node 1 0.0000E+00 0.0000E+00 Node 2 0.0000E+00 -0.1000E+01 Node 3 0.8000E+00 0.0000E+00 Node 4 0.8000E+00 -0.1000E+01 Node 5
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www.eeworm.com/read/424538/10440088

asv discretisationeigenvectordata.asv

function Y = discretisationEigenVectorData(EigenVector) % % Timothee Cour, Stella Yu, Jianbo Shi, 2004 [n,k]=size(EigenVector); [Maximum,J]=max(EigenVector'); Y=sparse(1:n,J',1,n,k);
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www.eeworm.com/read/424538/10440093

m discretisationeigenvectordata.m

function Y = discretisationEigenVectorData(EigenVector) % Y = discretisationEigenVectorData(EigenVector) % % discretizes previously rotated eigenvectors in discretisation % Timothee Cour, Stella Y
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www.eeworm.com/read/350382/10745642

m 5-12.m

%例程5－12 利用特征向量法估计功率谱 % e.g.5-12.m for example5-12; % to test function peig; clf; clear all; % Generate the signal plus white noise and show N=1024; % number of sampling dat
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www.eeworm.com/read/271037/11011239

input-algo-modal-analysis

/* [a] : Parameters for Modal Analysis and embedded Newmark Integration */ no_eigen = 2; dt = 0.03 sec; nsteps = 200; beta = 0.25; gamma = 0.50; /* [b] : Form M
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www.eeworm.com/read/466289/7041725

res p103.res

Global coordinates Node 1 0.0000E+00 0.0000E+00 Node 2 0.0000E+00 -0.1000E+01 Node 3 0.8000E+00 0.0000E+00 Node 4 0.8000E+00 -0.1000E+01 Node 5
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www.eeworm.com/read/466289/7041888

res p104.res

Global coordinates Node 1 0.0000E+00 0.0000E+00 Node 2 0.0000E+00 -0.1000E+01 Node 3 0.8000E+00 0.0000E+00 Node 4 0.8000E+00 -0.1000E+01 Node 5
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www.eeworm.com/read/455967/7360631

asv discretisationeigenvectordata.asv

function Y = discretisationEigenVectorData(EigenVector) % % Timothee Cour, Stella Yu, Jianbo Shi, 2004 [n,k]=size(EigenVector); [Maximum,J]=max(EigenVector'); Y=sparse(1:n,J',1,n,k);
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