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/305632/13764283
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/152629/5672967
java univariate.java
/* Copyright (C) 2002 Univ. of Massachusetts Amherst, Computer Science Dept.
This file is part of "MALLET" (MAchine Learning for LanguagE Toolkit).
http://www.cs.umass.edu/~mccallum/mallet
Th
www.eeworm.com/read/149652/5698179
h newran.h
// newran.h ------------------------------------------------------------
// NEWRAN02B - 22 July 2002
#ifndef NEWRAN_LIB
#define NEWRAN_LIB 0
//******************* utilities and definitions *
www.eeworm.com/read/144624/5749305
conf ts.conf
module variance xlimit=50 ylimit=50 pthreshold=1
module dejitter xdelta=10 ydelta=10 pthreshold=1
module linear
www.eeworm.com/read/136989/5828408
c statistics.c
/* statistics.c,v 1.3 2000/09/22 19:30:37 brunsch Exp */
/**************************************************************************
* *
* Copyright (C) 1995 Silicon Graphics, Inc.
www.eeworm.com/read/133885/5898981
java segmenter.java
/** Class responsible for applying the dynamic programming approach to
* get the line segmentation.
*/
package tclass.pep;
import tclass.*;
import tclass.util.*;
import java.util.*;
public
www.eeworm.com/read/133885/5898986
java randomsegmenter.java
/** Class responsible for applying the dynamic programming approach to
* get the line segmentation.
*/
package tclass.pep;
import tclass.*;
import tclass.util.*;
import java.util.*;
public
www.eeworm.com/read/100926/6264526
c statistics.c
/* statistics.c,v 1.3 2000/09/22 19:30:37 brunsch Exp */
/**************************************************************************
* *
* Copyright (C) 1995 Silicon Graphics, Inc.
www.eeworm.com/read/493463/6393914
m assignment_bearings_only_measurement.m
% ASSIGNMENT --- Detection and Estimation %
% % %%% Non-Linear Least Squares --- Bearings-Only Measurement %%% %
%
www.eeworm.com/read/493206/6398512
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