代码搜索：Variance

找到约 2,271 项符合「Variance」的源代码

代码结果 2,271
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m reducedeof.m

function [V,EOFs,EC,error]=ReducedEOF(D, p, minVar, r) % This function filters out any points with a variance that % is smaller than the variance minVar. % Is a fourth optional parameter r is given
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www.eeworm.com/read/427216/8965805

m gaussianmask.m

function M = gaussianMask(k,s) % k: the scaling factor % s: standard variance R = ceil(3*s); % cutoff radius of the gaussian kernal for i = -R:R, for j = -R:R, M(i+ R+1,j+R+1) =
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www.eeworm.com/read/379730/9180052

py config.py

# -*- coding: iso-8859-1 -*- # # Copyright (C) 2001-2006 Markus Harva, Antti Honkela, Alexander # Ilin, Tapani Raiko, Harri Valpola and Tomas 謘tman. # # This program is free software; you can redistri
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www.eeworm.com/read/182174/9213574

m hybridsir.m

function [x, q, m] = hybridsir(input,y,s1,s2,S,Q,initVar1,initVar2,R,KalmanR,KalmanQ,KalmanP,tsteps); % PURPOSE : To train an MLP with the hybrid SIR algorithm. % INPUTS : - input = The input observ
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www.eeworm.com/read/179705/9342105

c stat.c

#include #include int main(void) { double data[5] = {17.2, 18.1, 16.5, 18.3, 12.6}; double mean, variance, largest, smallest; mean = gsl_stats_mean(data, 1
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www.eeworm.com/read/375212/9368918

asv osccalc.asv

function [x,nw,np,nt] = osccalc(x,y,nocomp,iter,tol) %OSCCALC Calculates orthogonal signal correction % The inputs are the matrix of predictor variables (x) % and predicted variable(s) (y), scale
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www.eeworm.com/read/375212/9369228

m osccalc.m

function [x,nw,np,nt] = osccalc(x,y,nocomp,iter,tol) %OSCCALC Calculates orthogonal signal correction % The inputs are the matrix of predictor variables (x) % and predicted variable(s) (y), scale
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www.eeworm.com/read/371255/9559112

m normalise.m

% NORMALISE - Normalises image values to 0-1, or to desired mean and variance % % Usage: % n = normalise(im) % % Offsets and rescales image so that the minimum value is 0 % and the maximum
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www.eeworm.com/read/362500/9996011

m osccalc.m

function [x,nw,np,nt] = osccalc(x,y,nocomp,iter,tol) %OSCCALC Calculates orthogonal signal correction % The inputs are the matrix of predictor variables (x) % and predicted variable(s) (y), scale
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www.eeworm.com/read/361765/10036856

rd mcnormalnormal.rd

\name{MCnormalnormal} \alias{MCnormalnormal} \title{Monte Carlo Simulation from a Normal Likelihood (with known variance) with a Normal Prior} \description{ This function generates a sample from th
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