代码搜索：Matrix
找到约 10,000 项符合「Matrix」的源代码
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www.eeworm.com/read/386066/8766289
m errorpattern.m
function out=errorpattern(syndrome)
%function to get the error pattern given the syndrome..A matrix with three
%columns and any number of rows
%Works only for parity check matrix
%H=[eye(3),transp
www.eeworm.com/read/386050/8767555
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/386050/8768350
m ldc.m
%LDC Linear Bayes Normal Classifier (BayesNormal_1)
%
% [W.R,S,M] = LDC(A,R,S,M)
% W = A*LDC([],R,S,M);
%
% INPUT
% A Dataset
% R,S Regularization parameters, 0
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m fisherm.m
%FISHERM Optimal discrimination linear mapping (Fisher mapping, LDA)
%
% 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/386050/8768916
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
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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/386050/8769612
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/429878/8783801
htm rbffwd.htm
Netlab Reference Manual rbffwd
rbffwd
Purpose
Forward propagation through RBF network with linear outputs.
Synopsis
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htm mdnfwd.htm
Netlab Reference Manual mdnfwd
mdnfwd
Purpose
Forward propagation through Mixture Density Network.
Synopsis
mix
www.eeworm.com/read/429878/8783874
htm glmhess.htm
Netlab Reference Manual glmhess
glmhess
Purpose
Evaluate the Hessian matrix for a generalised linear model.
Synopsis