JKQtPlotter/lib/jkqtcommon/jkqtpstatisticstools.cpp
jkriege2 356cc34349 new: Statistics library with functions to calculate histograms, regression, kernel density estimates, ... including a new example
new: iterator interface and improved documentation for JKQTPDatastore
reorganization of library (better separation of common code in jkqtpcommon and other code e.g. in jkqtplotter or jkqtmathtext)
2019-05-29 22:40:02 +02:00

107 lines
2.7 KiB
C++

/*
Copyright (c) 2008-2019 Jan W. Krieger (<jan@jkrieger.de>)
last modification: $LastChangedDate$ (revision $Rev$)
This software is free software: you can redistribute it and/or modify
it under the terms of the GNU Lesser General Public License (LGPL) as published by
the Free Software Foundation, either version 2.1 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Lesser General Public License (LGPL) for more details.
You should have received a copy of the GNU Lesser General Public License (LGPL)
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
#include "jkqtpstatisticstools.h"
double jkqtpstatKernel1DGaussian(double t) {
return exp(-0.5*t*t)/JKQTPSTATISTICS_SQRT_2PI;
}
double jkqtpstatKernel1DCauchy(double t) {
return 1.0/(M_PI*(1.0+t*t));
}
double jkqtpstatKernel1DPicard(double t) {
return exp(-0.5*fabs(t))/2.0;
}
double jkqtpstatKernel1DEpanechnikov(double t) {
return (fabs(t)<1.0)?(0.75*(1.0-t*t)):0.0;
}
double jkqtpstatKernel1DUniform(double t) {
return (fabs(t)<=1.0)?0.5:0.0;
}
double jkqtpstatKernel1DTriangle(double t) {
return (fabs(t)<=1.0)?(1.0-fabs(t)):0.0;
}
double jkqtpstatKernel1DQuartic(double t) {
return (fabs(t)<=1.0)?(15.0/16.0*jkqtp_sqr(1.0-t*t)):0.0;
}
double jkqtpstatKernel1DTriweight(double t) {
return (fabs(t)<1.0)?(35.0/32.0*jkqtp_cube(1.0-t*t)):0.0;
}
double jkqtpstatKernel1DTricube(double t) {
return (fabs(t)<1.0)?(70.0/81.0*jkqtp_cube(1.0-jkqtp_cube(fabs(t)))):0.0;
}
double jkqtpstatKernel1DCosine(double t) {
return (fabs(t)<1.0)?(M_PI/4.0*cos(t*M_PI/2.0)):0.0;
}
double jkqtpstatKernel2DGaussian(double tx, double ty)
{
return exp(-0.5*(tx*tx+ty*ty))/(2.0*M_PI);
}
double jkqtpstatKernel2DUniform(double tx, double ty) {
return (fabs(tx)<1.0 && fabs(ty)<=1.0)?0.25:0.0;
}
JKQTPStat5NumberStatistics::JKQTPStat5NumberStatistics():
minimum(JKQTP_DOUBLE_NAN),
minimumQuantile(0),
quantile1(JKQTP_DOUBLE_NAN),
quantile1Spec(0.25),
median(JKQTP_DOUBLE_NAN),
quantile2(JKQTP_DOUBLE_NAN),
quantile2Spec(0.75),
maximum(JKQTP_DOUBLE_NAN),
maximumQuantile(1),
N(0)
{}
double JKQTPStat5NumberStatistics::IQR() const {
return quantile2-quantile1;
}
double JKQTPStat5NumberStatistics::IQRSignificanceEstimate() const {
return 2.0*(1.58*(IQR()))/sqrt(static_cast<double>(N));
}