/*
* Encog(tm) Core v3.4 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2016 Heaton Research, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
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*/
package org.encog.ml.fitting.gaussian;
import org.encog.mathutil.matrices.Matrix;
import org.encog.mathutil.matrices.MatrixMath;
import org.encog.ml.MLRegression;
import org.encog.ml.data.MLData;
import org.encog.ml.data.basic.BasicMLData;
public class GaussianFitting implements MLRegression {
private double[] weights;
private int inputCount;
private final Matrix sigma;
private final Matrix mu;
private Matrix sigmaInverse;
private double dimFactor;
private double normConst;
public GaussianFitting(int theInputCount) {
this.mu = new Matrix(1,theInputCount);
this.sigma = new Matrix( theInputCount,theInputCount );
this.inputCount = theInputCount;
this.weights = new double[theInputCount+1];
}
public double[] getWeights() {
return weights;
}
@Override
public int getInputCount() {
return this.inputCount;
}
@Override
public int getOutputCount() {
return 1;
}
@Override
public MLData compute(MLData input) {
BasicMLData result = new BasicMLData(1);
Matrix m1 = Matrix.createRowMatrix(input.getData());
Matrix m2 = MatrixMath.subtract(m1, this.mu);
Matrix m3 = MatrixMath.transpose(m2);
Matrix m4 = MatrixMath.multiply(sigmaInverse, m3);
Matrix m5 = MatrixMath.multiply(m4, m2);
result.setData(0, m5.get(0, 0));
/* double d1 = x.minus(mu).transpose().times
(sigmaInverse).times(x.minus(mu)).get(0,0);
double d2 = Math.exp(-0.5*d1) / normConst;
*/
return result;
}
/**
* @return the sigma
*/
public Matrix getSigma() {
return sigma;
}
/**
* @return the mu
*/
public Matrix getMu() {
return mu;
}
public void finalizeTraining() {
this.sigmaInverse = this.sigma.inverse();
this.dimFactor = Math.pow(2 * Math.PI, ((double)this.getInputCount()) / 2.0);
this.normConst = Math.sqrt(MatrixMath.determinant(sigma)) * dimFactor;
}
}