代码搜索：Generates

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m a2dr.m

function beq = a2dR(d,n) % BEQ = A2DR(D, N) generates the decimal % equivalent beq of the binary representation % of a decimal number D with N bits for the % magnitude part obtained by rounding %
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txt readme.txt

This is just an example to show how to use the MPEGDEC.lib with custom bitstream access functions instead of standard file I/O provided by delfault. 17-Oct-1999 (rscott) Converted from Amiga toolcha
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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
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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
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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
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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
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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
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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
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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
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m contents.m

% Data sets used by the STPRtool. % % andersons_task - (dir) Input for demo on Generalized Anderson's task. % binary_separable - (dir) Input for demo on Linear classification. % gmm_sample - (
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