代码搜索:Matrix

找到约 10,000 项符合「Matrix」的源代码

代码结果 10,000
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m desa.m

function [W,A]=desa(x, dt) % The function DESA calculates frequency and amplitude using Teager Energy Operator % for the data x(n,m), where n is the number of points, and m is % the number of
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m respsp.m

function ra=respsp(x1,x2) % The function RESPSP computes the Hilbert response function % from one input, x2, and one output, x1, both are Hilbert spectra % of the same dimensions. % The basic
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m cnstm.m

function z=cnstm(p,nf1,nf2,nn) % % z=cnstm(p,nf1,nf2,nn): % % Function to construct a matrix of constant horizontal value. % % Input- % p - vector p(nf2), representing the power spectrum % nf1
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m nfame.m

function [nf, na, ex, E, N] = nfame(data,dt) % % [nf,na,ex,E,N]=nfame(data,dt) : % % Function to calculate the modified Hilbert frequency and amplitude % of data(n,k), where n specifies
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m sqrtsim2dis.m

function D=sqrtsim2dis(S) %function D=sqrtsim2dis(S); % %PURPOSE % %To transform elements of a similarity matrix S into %dissimilarities D by D=sqrt(1-S) % %INPUT % %S (matrix) MxM similarity matrix w
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m redscale.m

function map=redscale(N,B) %function map=redscale(N,B) % %PURPOSE % %Makes a colormap of shades of red from white to red. % %INPUT % %[An argument in brackets is optional. If it isn't given or it's %
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m sim2dis.m

function D=sim2dis(S) %function D=sim2dis(S); % %PURPOSE % %To transform elements of a similarity matrix S into %dissimilarities D by D=1-S. % %INPUT % %S (matrix) MxM similarity matrix whose element
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m centrotype.m

function i=centrotype(S) %function i=centrotype(S) % %PURPOSE % %To compute the centrotype of objects whose relations are %defined by similarity matrix S. Centrotype means the object that %is most sim
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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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m corrw.m

function R=corrw(W,D) %function R=corrw(W,D) % %PURPOSE % %To compute mutual linear correlation coefficients between M %independent component estimates using the demixing matrix W and %the dewhitening