/*
 This is a proprietary part of DataMelt package.
 License: the Creative Commons Attribution-Share Alike License; either version 3.0 of the License, 
 or any later version.
 http://creativecommons.org/licenses/by-sa/3.0/
 

Permission to use, copy and modify this software and its documentation for NON-COMMERCIAL purposes is granted, without fee, provided that an acknowledgment to the author, Dr Sergei Chekanov appears in all copies and associated documentation or publications.

Redistributions of the source code, or parts of the source codes, must retain the copyright notice, this list of conditions and the following disclaimer (all at the top of each source code) and requires the written permission of Sergei Chekanov

Redistribution in binary form of all or parts of a class must reproduce the copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution and requires the written permission of Sergei Chekanov

Details about license agreement or commercial use are: http://jwork.org/scavis */ package jhpro.stat; import jhplot.P1D; import jhplot.gui.*; import org.apache.commons.math3.util.FastMath; /** * Calculations of normalized reduced moments of a distribution. * The normalised reduced moments are calculated as * up to an orbitrary order n, where n is the oder of the moment. * The factorial moments are defined as
* F_1=1 (1st order),
F_2=/^2 (second order),
F_2=/N^3 (3rd order)
* and so on. The general form is /^q. * The code works only for integer random variables. * Statistical * errors are also evaluated. If they are set to 0, * this means that there is no enough events for their calculation. * @author S.Chekanov * */ public class MomentsNorm { private int oder=0; private double[] IL, IE, IEE, VAL, AVAL; private int nev; /** * Initialization of the normalised factorial moments. * * @param order * Max oder of the normalised factorial moment */ public MomentsNorm(int order) { nev = 0; this.oder = order; IL = new double[oder]; IE = new double[oder]; IEE = new double[oder]; VAL = new double[oder]; AVAL = new double[oder]; for (int i = 0; i < oder; i++) { IL[i] = 0; IE[i] = 0; IEE[i] = 0; VAL[i] = 0; AVAL[i] = 0; } } /** * Process current event for an integer random variable * * @param n * input variable for the moment calculations */ public void process(int n) { nev++; for (int i = 0; i < oder; i++) { int h=n; for (int j=0; j 0) IER[i] = VAR / events; } // calculate covariances double[] COV = new double[oder]; for (int i = 0; i < oder; i++) { double x1 = IEE[i] / events; double x2 = (IL[i]/events) * (IL[0]/events); COV[i]= (x1 - x2 ) / events; } // error propogation double[] ERROR = new double[oder]; for (int i = 0; i < oder; i++) { int n=i+1; double x1 = IER[i]/FastMath.pow(AVAL[0],2*n); double x2 = n*n* ((AVAL[i]*AVAL[i])/FastMath.pow(AVAL[0], 2*n+2)) *IER[0]; double x3 = 2*n*(AVAL[i]/FastMath.pow(AVAL[0], 2*n+1))*COV[i]; double VARTOT=x1+x2-x3; ERROR[i]=0; if (VARTOT > 0) ERROR[i] = FastMath.sqrt(VARTOT); } return ERROR; } /** * Get results in form of P1D. * * * @return p1d with values and errors */ public P1D getResults() { P1D tmp = new P1D("Factorial Moments"); double[] val=getValues(); double[] err=getErrors(); for (int i = 0; i < oder; i++) { tmp.add(i+1,val[i], err[i]); } return tmp; } /** * Show online documentation. */ public void doc() { String a = this.getClass().getName(); a = a.replace(".", "/") + ".html"; new HelpBrowser(HelpBrowser.JHPLOT_HTTP + a); } }

Ads help maintain this website.