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package de.lmu.ifi.dbs.elki.math.statistics;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2012
Ludwig-Maximilians-Universität München
Lehr- und Forschungseinheit für Datenbanksysteme
ELKI Development Team
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 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 Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
/**
* Estimate density given an array of points.
*
* Estimates a density using a kernel density estimator. Multiple common Kernel
* functions are supported.
*
* @author Erich Schubert
*
* @apiviz.uses de.lmu.ifi.dbs.elki.math.statistics.KernelDensityFunction
*/
public class KernelDensityEstimator {
/**
* Result storage: density
*/
private double[] dens;
/**
* Result storage: variance / quality
*/
private double[] var;
/**
* Initialize and execute kernel density estimation.
*
* @param data data to use
* @param min minimum value
* @param max maximum value
* @param kernel Kernel function to use
* @param windows window size
*/
public KernelDensityEstimator(double[] data, double min, double max, KernelDensityFunction kernel, int windows) {
process(data, min, max, kernel, windows);
}
/**
* Process a new array
*
* @param data data to use
* @param min minimum value
* @param max maximum value
* @param kernel Kernel function to use
* @param windows window size
*/
private void process(double[] data, double min, double max, KernelDensityFunction kernel, int windows) {
dens = new double[data.length];
var = new double[data.length];
double halfwidth = ((max - min) / windows) / 2;
// collect data points
for(int current = 0; current < data.length; current++) {
double value = 0.0;
// TODO: is there any way we can skip through some of the data (at least if its sorted?)
// Since we know that all kKernels return 0 outside of [-1:1]?
for(int i = 0; i < data.length; i++) {
double delta = Math.abs(data[i] - data[current]) / halfwidth;
value += kernel.density(delta);
}
double realwidth = (Math.min(data[current] + halfwidth, max) - Math.max(min, data[current] - halfwidth));
double weight = realwidth / (2 * halfwidth);
dens[current] = value / (data.length * realwidth / 2);
var[current] = 1 / weight;
}
}
/**
* Process an array of data
*
* @param data data to process
* @param kernel Kernel function to use.
*/
public KernelDensityEstimator(double[] data, KernelDensityFunction kernel) {
double min = Double.MAX_VALUE;
double max = Double.MIN_VALUE;
for(double d : data) {
if(d < min) {
min = d;
}
if(d > max) {
max = d;
}
}
// Heuristics.
int windows = 1 + (int) (Math.log(data.length));
process(data, min, max, kernel, windows);
}
/**
* Retrieve density array (NO copy)
*
* @return density array
*/
public double[] getDensity() {
return dens;
}
/**
* Retrieve variance/quality array (NO copy)
*
* @return variance array
*/
public double[] getVariance() {
return var;
}
}
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