代码搜索：Generates
找到约 10,000 项符合「Generates」的源代码
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www.eeworm.com/read/450608/7480530
m isuntrained.m
%ISUNTRAINED Test on untrained mapping
%
% I = ISUNTRAINED(W)
% ISUNTRAINED(W)
%
% True if the mapping type of W is 'untrained' (see HELP MAPPINGS).
% If called without an output argument ISUNTR
www.eeworm.com/read/445831/7589499
m gngauss.m
function [gsrv1,gsrv2]=gngauss(m,sgma)
% [gsrv1,gsrv2]=gngauss(m,sgma)
% [gsrv1,gsrv2]=gngauss(sgma)
% [gsrv1,gsrv2]=gngauss
% GNGAUSS generates two independent Gaussian random variables with me
www.eeworm.com/read/445827/7589596
m gngauss.m
function [gsrv1,gsrv2]=gngauss(m,sgma)
% [gsrv1,gsrv2]=gngauss(m,sgma)
% [gsrv1,gsrv2]=gngauss(sgma)
% [gsrv1,gsrv2]=gngauss
% GNGAUSS generates two independent Gaussian random variables w
www.eeworm.com/read/442444/7651674
m gngauss.m
function [gsrv1,gsrv2]=gngauss(m,sgma)
% [gsrv1,gsrv2]=gngauss(m,sgma)
% [gsrv1,gsrv2]=gngauss(sgma)
% [gsrv1,gsrv2]=gngauss
% GNGAUSS generates two independent Gaussian random variables w
www.eeworm.com/read/441245/7673274
m isfixed.m
%ISFIXED Test on fixed mapping
%
% I = ISFIXED(W)
% ISFIXED(W)
%
% True if the mapping type of W is 'fixed' (see HELP MAPPINGS). If called
% without an output argument ISFIXED generates an erro
www.eeworm.com/read/441245/7673309
m iscombiner.m
%ISCOMBINER Test whether the argument is a combiner mapping
%
% OK = ISCOMBINER(W)
% ISCOMBINER(W)
%
% INPUT
% W Mapping
%
% OUTPUT
% OK 1/0 indicating if the mapping type of W is COMBINER
www.eeworm.com/read/441245/7673324
m istrained.m
%ISTRAINED Test on trained mapping
%
% I = ISTRAINED(W)
% ISTRAINED(W)
%
% True if the mapping type of W is 'trained' (see HELP MAPPINGS). If
% called without an output argument ISTRAINED gener
www.eeworm.com/read/441245/7673347
m isuntrained.m
%ISUNTRAINED Test on untrained mapping
%
% I = ISUNTRAINED(W)
% ISUNTRAINED(W)
%
% True if the mapping type of W is 'untrained' (see HELP MAPPINGS).
% If called without an output argument ISUNTR
www.eeworm.com/read/441245/7673399
m gendatsin.m
%GENREGSIN Generate sinusoidal regression data
%
% X = GENDATSIN(N,SIGMA)
%
% INPUT
% N Number of objects to generate
% SIGMA Standard deviation of the noise
%
% OUTPUT
% X Reg