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package de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2015
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/>.
*/
import de.lmu.ifi.dbs.elki.utilities.datastructures.arraylike.NumberArrayAdapter;
import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
/**
* Methods of moments estimator, using the first moment (i.e. average).
*
* This could be generalized to higher order moments, but the variance increases
* with the order, and we need this to work well with small sample sizes.
*
* Reference:
* <p>
* L. Amsaleg and O. Chelly and T. Furon and S. Girard and M. E. Houle and K.
* Kawarabayashi and M. Nett<br />
* Estimating Local Intrinsic Dimensionality<br />
* Proc. SIGKDD International Conference on Knowledge Discovery and Data Mining
* 2015
* </p>
*
* @author Erich Schubert
* @since 0.7.0
*/
@Reference(authors = "L. Amsaleg and O. Chelly and T. Furon and S. Girard and M. E. Houle and K. Kawarabayashi and M. Nett", //
title = "Estimating Local Intrinsic Dimensionality", //
booktitle = "Proc. SIGKDD International Conference on Knowledge Discovery and Data Mining 2015", //
url = "http://dx.doi.org/10.1145/2783258.2783405")
public class MOMEstimator extends AbstractIntrinsicDimensionalityEstimator {
/**
* Static instance.
*/
public static final MOMEstimator STATIC = new MOMEstimator();
@Override
public <A> double estimate(A data, NumberArrayAdapter<?, A> adapter, final int len) {
if(len < 2) {
throw new ArithmeticException("ID estimates require at least 2 non-zero distances");
}
double v1 = 0.;
final int num = len - 1;
for(int i = 0; i < num; i++) {
v1 += adapter.getDouble(data, i);
}
v1 /= num * adapter.getDouble(data, num);
return v1 / (1 - v1);
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer extends AbstractParameterizer {
@Override
protected MOMEstimator makeInstance() {
return STATIC;
}
}
}
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