代码搜索：Matrix
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www.eeworm.com/read/255925/12046088
m ssainv.m
function xr=SSAinv(pc,v,tau,k)
%Syntax: xr=SSAinv(pc,v,tau,k)
%_____________________________
%
% The inverse of Singular Spectrum Analysis for a time series.
%
% xr is the reconstructed time ser
www.eeworm.com/read/342008/12046881
m distmaha.m
%DISTMAHA Mahalanobis distance
%
% D = distmaha(A,U,G)
%
% Computation of the Mahanalobis distances of all vectors in the
% dataset A to a dataset of points U, using the covariance matrix G.
% G
www.eeworm.com/read/342008/12047239
m gendatc.m
%GENDATC Generation of two circular classes with different
% variances
%
% A = gendatc(na,nb,k,ma)
%
% Generation of two sets of k dimensional Gaussian distributed data
% vectors. Class a has the
www.eeworm.com/read/342008/12047281
m gendatp.m
%GENDATP Parzen density data generation
%
% B = gendatp(A,m,s)
%
% Generation of m points using the Parzen estimate of the density of
% the dataset A using a smoothing parameter s. Default s or s
www.eeworm.com/read/255755/12057373
m iscolumn.m
%ISCOLUMN Checks whether the argument is a column array
%
% [OK,Y] = ISCOLUMN(X)
%
% INPUT
% X Array: an array of entities such as numbers, strings or cells
%
% OUTPUT
% OK 1 if X is a column
www.eeworm.com/read/255755/12057969
m fisherm.m
%FISHERM Optimal discrimination linear mapping (Fisher mapping)
%
% W = FISHERM(A,N,ALF)
%
% INPUT
% A Dataset
% N Number of dimensions to map to, N < C, where C is the number of classes
%
www.eeworm.com/read/255755/12057986
m distm.m
%DISTM Compute square Euclidean distance matrix
%
% D = DISTM(A,B)
%
% INPUT
% A,B Datasets or matrices; B is optional, default B = A
%
% OUTPUT
% D Square Euclidean distance dataset or
www.eeworm.com/read/255755/12058104
m setcost.m
%SETCOST Reset classification cost matrix of mapping
%
% W = SETCOST(W,COST,LABLIST)
%
% The classification cost matrix of the dataset W is reset to COST.
% W has to be a trained classifier. CO
www.eeworm.com/read/255755/12058379
m covm.m
%COVM Compute covariance matrix for large datasets
%
% C = COVM(A)
%
% Similar to C = COV(A) this routine computes the covariance matrix
% for the datavectors stored in the rows of A. No large int
www.eeworm.com/read/153014/12067139
m lmeval.m
%#
%# function [y1,y2,h1,h2] = lmeval(topo,w1,w2,x)
%#
%# AIM: Computes the output of a backpropagation neural network.
%#
%# PRINCIPLE: The topology of the ne