代码搜索：Variance
找到约 2,271 项符合「Variance」的源代码
代码结果 2,271
www.eeworm.com/read/220187/14847351
txt 描述.txt
Functions for Rice/Rician PDF, mean and variance, and generating random samples.
Similar to e.g. normpdf, normrnd, normstat from the MATLAB statistics toolbox, but for the Rice distribution, which
www.eeworm.com/read/114869/15035116
pas testmc2.pas
{ **********************************************************************
* Program TESTMC2.PAS *
* Version 1.2d
www.eeworm.com/read/213320/15137085
c mrandom.c
#include
#include
#include
#include "msp.h"
float randnu(long *iseed)
{
float z;
*iseed=2045*(*iseed)+1;
*iseed=*iseed-(*iseed/1048576)*10
www.eeworm.com/read/212273/15160709
m scale.m
%#
%# function [sdata,me,standev,snewdata] = scale(data,scaltype,newdata)
%#
%# AIM: Performs scaling to unit variance or autoscaling
%# (centering + scaling to unit
www.eeworm.com/read/211981/15168864
c mrandom.c
#include
#include
#include
#include "msp.h"
float randnu(long *iseed)
{
float z;
*iseed=2045*(*iseed)+1;
*iseed=*iseed-(*iseed/1048576)*10
www.eeworm.com/read/209599/15216911
java agentvariants.java
package asm;
/**
* Title: Artificial Stock Market
* Description: 人工模拟股市（来源：SFI的Swarm版本）的Java版本
* Copyright: Copyright (c) 2003
* Company: http://agents.yeah.net
* @author jake
*
www.eeworm.com/read/208393/15247542
txt log.txt
**** Entry point ****
-> Engine started in 0.3 seconds. Rendering...
No variance cache found. Updating.
Saved variances.
www.eeworm.com/read/475585/6773611
cpp clusttool.cpp
/******************************************************************************
** Filename: clustertool.c
** Purpose: Misc. tools for use with the clustering routines
** Author: Dan Johnson
** H
www.eeworm.com/read/474600/6813463
m mean_jackknife.m
function [mu, bias, varjack] = mean_jackknife(data)
%Find the estimate of the mean, it's bias and variance using the jackknife estimator method
%Inputs:
% data - The data from which to estimate
www.eeworm.com/read/474600/6813471
m mean_bootstrap.m
function [mu, bias, varjack] = mean_bootstrap(data, B)
%Find the estimate of the mean, it's bias and variance using the bootstrap estimator method
%Inputs:
% data - The data from which to estimat