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Stochastice Performance Logic is a formalism for capturing performance assumptions. It is, for example, possible to capture assumption that newer version of a function bar is faster than the previous version or that library foobar is faster than library barfoo when rendering antialiased text. The purpose of this framework is to allow evaluation of SPL formulas inside Java applications.

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/*
 * Copyright 2015 Charles University in Prague
 * Copyright 2015 Vojtech Horky
 * 
 * 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.
 */
package cz.cuni.mff.d3s.spl.interpretation;

import cz.cuni.mff.d3s.spl.data.BenchmarkRun;
import cz.cuni.mff.d3s.spl.data.BenchmarkRunUtils;
import cz.cuni.mff.d3s.spl.data.DataSnapshot;
import org.apache.commons.math3.distribution.NormalDistribution;
import org.apache.commons.math3.stat.inference.MannWhitneyUTest;

/** SPL interpretation based on Mann-Whitney test.
 * 
 * This code is greatly inspired by
 * org.apache.commons.math3.stat.inference.MannWhitneyUTest implementation
 * that could not be used directly in the compare() method.
 *
 */
public class MannWhitneyInterpretation implements Interpretation {
	private final MannWhitneyUTest utest = new MannWhitneyUTest();
	
	/** {@inheritDoc} */
	@Override
	public ComparisonResult compare(DataSnapshot left, DataSnapshot right) {
		double[] leftSamples = mergeSamples(left);
		double[] rightSamples = mergeSamples(right);
		
		double uStatMax = utest.mannWhitneyU(leftSamples, rightSamples);
		
		long lengthsMultiplied = (long) leftSamples.length * rightSamples.length;

		double uStatMin = lengthsMultiplied - uStatMax;
		
		/* https://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test#Normal_approximation */
		double meanU = lengthsMultiplied / 2.0;
		double varU = lengthsMultiplied * (leftSamples.length + rightSamples.length + 1) / 12.0;
		
		double z = (uStatMin - meanU) / Math.sqrt(varU);
		
		NormalDistribution distribution = new NormalDistribution(0.0, 1.0);
		
		return new DistributionBasedComparisonResult(z, distribution);
	}

	/** {@inheritDoc} */
	@Override
	public ComparisonResult compare(DataSnapshot data, double value) {
		throw new UnsupportedOperationException("This is not yet implemented.");
	}

	private double[] mergeSamples(DataSnapshot data) {
		BenchmarkRun merged = BenchmarkRunUtils.merge(data.getRuns());
		
		return BenchmarkRunUtils.toDoubleArray(merged);
	}
}