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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.utils;

import org.apache.commons.math3.random.EmpiricalDistribution;
import org.apache.commons.math3.random.JDKRandomGenerator;
import org.apache.commons.math3.random.RandomGenerator;

/** Helper method for creating mathematical distributions.
 */
public class DistributionUtils  {
		
	/** Create empirical distribution from given samples.
	 * 
	 * @param samples Samples (does not need to be distinct) to use.
	 * @return Empirical distribution built from the samples.
	 */
	public static EmpiricalDistribution makeEmpirical(double[] samples) {
		/* Be deterministic for now. */
		RandomGenerator gen = new JDKRandomGenerator();
		gen.setSeed(0);
		
		EmpiricalDistribution result = new EmpiricalDistribution(samples.length / 10 + 1, gen);
		result.load(samples);
		
		return result;
	}
}