代码搜索：deviation

找到约 1,443 项符合「deviation」的源代码

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m da_lsqs.m

% % da_lsqs % % Least squares regression entry point % % % Clear the screen % da_front; drawnow; % % Make sure that none of the variables have a zero % standard deviation % s=std(dat
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www.eeworm.com/read/400577/11572649

m rnnc.m

%RNNC Random Neural Net classifier % % W = RNNC(A,N,S) % % INPUT % A Input dataset % N Number of neurons in the hidden layer % S Standard deviation of weights in an input layer (default: 1
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www.eeworm.com/read/124842/14534525

m da_lsqs.m

% % da_lsqs % % Least squares regression entry point % % % Clear the screen % da_front; drawnow; % % Make sure that none of the variables have a zero % standard deviation % s=std(dat
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www.eeworm.com/read/223154/14652221

m normpdf.m

function p = normpdf(x,m,s); % NORMPDF returns normal probability density % % pdf = normpdf(x,m,s); % % Computes the PDF of a the normal distribution % with mean m and standard deviation s
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www.eeworm.com/read/223154/14652310

m medabsdev.m

function [D, M] = medAbsDev(X, DIM) % medAbsDev calculates the median absolute deviation % % Usage: D = medAbsDev(X, DIM) % or: [D, M] = medAbsDev(X, DIM) % Input: X : data % D
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www.eeworm.com/read/217691/14953336

m sdann.m

function[t, v, lab] = sdann(times, vals, labels, seglength) % function[t, v, lab] = sdann(times, vals, labels, seglength) % calculates: % 1) the sdann: the standard deviation of the averages of values
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www.eeworm.com/read/214740/15090350

m da_lsqs.m

% % da_lsqs % % Least squares regression entry point % % % Clear the screen % da_front; drawnow; % % Make sure that none of the variables have a zero % standard deviation % s=std(dat
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www.eeworm.com/read/467516/1500130

as stddev.as

package flare.query.methods { import flare.query.Variance; /** * Creates a new Variance query operator that computes * the population standard deviation. * @param expr th
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www.eeworm.com/read/455463/1614444

m autoreject.m

function [cvpi,cvpj]=autoreject(cvpi,cvpj,nshots,dev,standard) % Function used to reject some of the cross over point based on the standard % deviation or a constant difference from the cross over po
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www.eeworm.com/read/309003/3708826

m rigorthresh.m

function xhat = RigorThresh(y) % RigorThresh -- Adaptive Threshold Selection using SURE % Usage % xhat = RigorThresh(y) % Inputs % y Noisy Data with Std. Deviation = 1 % Outputs % xhat Esti
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