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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.stat.descriptive.moment.Mean;
import org.apache.commons.math3.stat.descriptive.moment.Variance;

import java.util.Random;

/** Helper methods for common statistics.
 */
public class StatisticsUtils  {
	/** Compute arithmetic mean of given data.
	 * 
	 * @param values Array of values to compute the mean from.
	 * @return Mean of the provided values.
	 */
	public static double mean(double... values) {
		Mean mean = new Mean();
		return mean.evaluate(values);
	}
	
	/** Compute variance of given data with bias correction.
	 * 
	 * @param values Array of values to compute the variance from.
	 * @return Varince of the provided values.
	 */
	public static double variance(double... values) {
		Variance var = new Variance();
		return var.evaluate(values);
	}
	
	/** Compute variance of given data without bias correction.
	 * 
	 * @param values Array of values to compute the variance from.
	 * @return Varince of the provided values.
	 */
	public static double varianceN(double... values) {
		Variance var = new Variance(false);
		return var.evaluate(values);
	}
	
	/** Bootstrap from already known values.
	 * 
	 * 
	 * The bootstrapping procedure is simple as we randomly select values from
	 * the source array to store in the destination one.
	 * Obviously, the individual values can be repeated in the bootstrapped
	 * array.
	 * 
	 * @param source Array with original values to bootstrap from.
	 * @param dest Array where to store the bootstrapped values.
	 * @param rnd Random number generator to use.
	 */
	public static void bootstrap(double[] source, double dest[], Random rnd) {
		for (int i = 0; i < dest.length; i++) {
			dest[i] = source[rnd.nextInt(source.length)];
		}
	}
}