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oj! Algorithms - ojAlgo - is Open Source Java code that has to do with mathematics, linear algebra and optimisation.
/*
* Copyright 1997-2024 Optimatika
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
package org.ojalgo.random.process;
import org.ojalgo.array.Array1D;
import org.ojalgo.array.Array2D;
import org.ojalgo.random.Distribution;
import org.ojalgo.random.SampleSet;
import org.ojalgo.series.primitive.PrimitiveSeries;
/**
* A random/stochastic process (a series of random variables indexed by time or space) representing the
* evolution of some random value. The key thing you can do with a random process is to ask for its
* distribution at a given (future) time.
*
* @author apete
*/
public interface RandomProcess {
public static final class SimulationResults {
private final double myInitialValue;
private final Array2D myResults;
/**
* @param initialValue Initial value
* @param results (Random values) scenarios/realisations/series in rows, and sample sets in columns.
*/
public SimulationResults(final double initialValue, final Array2D results) {
super();
myInitialValue = initialValue;
myResults = results;
}
public int countSampleSets() {
return myResults.getColDim();
}
public int countScenarios() {
return myResults.getRowDim();
}
public double getInitialValue() {
return myInitialValue;
}
public SampleSet getSampleSet(final int sampleSetIndex) {
return SampleSet.wrap(myResults.sliceColumn(sampleSetIndex));
}
/**
* A series representing one scenario. Each series has length "number of simulation steps" + 1 as the
* series includes the initial value.
*/
public PrimitiveSeries getScenario(final int scenarioIndex) {
Array1D slicedRow = myResults.sliceRow(scenarioIndex);
return new PrimitiveSeries() {
@Override
public int size() {
return slicedRow.size() + 1;
}
@Override
public double value(final int index) {
if (index == 0) {
return myInitialValue;
} else {
return slicedRow.doubleValue(index - 1);
}
}
};
}
}
/**
* @param evaluationPoint How far into the future?
* @return The distribution for the process value at that future time.
*/
D getDistribution(double evaluationPoint);
/**
* Returns an collection of sample sets. The array has numberOfSteps elements, and each sample set has
* numberOfRealisations samples.
*/
RandomProcess.SimulationResults simulate(int numberOfRealisations, int numberOfSteps, double stepSize);
}