/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You 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 org.apache.commons.math.stat.descriptive.moment;

import java.io.Serializable;

import org.apache.commons.math.stat.descriptive.AbstractStorelessUnivariateStatistic;
import org.apache.commons.math.stat.descriptive.summary.Sum;

/**
 * <p>
 * Computes the arithmetic mean of a set of values. Uses the definitional
 * formula:
 * </p>
 * <p>
 * mean = sum(x_i) / n
 * </p>
 * <p>
 * where <code>n</code> is the number of observations.
 * </p>
 * <p>
 * When {@link #increment(double)} is used to add data incrementally from a
 * stream of (unstored) values, the value of the statistic that
 * {@link #getResult()} returns is computed using the following recursive
 * updating algorithm:
 * </p>
 * <ol>
 * <li>Initialize <code>m = </code> the first value</li>
 * <li>For each additional value, update using <br>
 * <code>m = m + (new value - m) / (number of observations)</code></li>
 * </ol>
 * <p>
 * If {@link #evaluate(double[])} is used to compute the mean of an array of
 * stored values, a two-pass, corrected algorithm is used, starting with the
 * definitional formula computed using the array of stored values and then
 * correcting this by adding the mean deviation of the data values from the
 * arithmetic mean. See, e.g. "Comparison of Several Algorithms for Computing
 * Sample Means and Variances," Robert F. Ling, Journal of the American
 * Statistical Association, Vol. 69, No. 348 (Dec., 1974), pp. 859-866.
 * </p>
 * <p>
 * Returns <code>Double.NaN</code> if the dataset is empty.
 * </p>
 * <strong>Note that this implementation is not synchronized.</strong> If
 * multiple threads access an instance of this class concurrently, and at least
 * one of the threads invokes the <code>increment()</code> or
 * <code>clear()</code> method, it must be synchronized externally.
 * 
 * @version $Revision$ $Date$
 */
public class Mean extends AbstractStorelessUnivariateStatistic implements Serializable {

	/** Serializable version identifier */
	private static final long serialVersionUID = -1296043746617791564L;

	/** First moment on which this statistic is based. */
	protected FirstMoment moment;

	/**
	 * Determines whether or not this statistic can be incremented or cleared.
	 * <p>
	 * Statistics based on (constructed from) external moments cannot be
	 * incremented or cleared.
	 * </p>
	 */
	protected boolean incMoment;

	/** Constructs a Mean. */
	public Mean() {
		incMoment = true;
		moment = new FirstMoment();
	}

	/**
	 * Constructs a Mean with an External Moment.
	 * 
	 * @param m1
	 *            the moment
	 */
	public Mean(final FirstMoment m1) {
		this.moment = m1;
		incMoment = false;
	}

	/**
	 * Copy constructor, creates a new {@code Mean} identical to the
	 * {@code original}
	 * 
	 * @param original
	 *            the {@code Mean} instance to copy
	 */
	public Mean(Mean original) {
		copy(original, this);
	}

	/**
	 * {@inheritDoc}
	 */
	@Override
	public void increment(final double d) {
		if (incMoment) {
			moment.increment(d);
		}
	}

	/**
	 * {@inheritDoc}
	 */
	@Override
	public void clear() {
		if (incMoment) {
			moment.clear();
		}
	}

	/**
	 * {@inheritDoc}
	 */
	@Override
	public double getResult() {
		return moment.m1;
	}

	/**
	 * {@inheritDoc}
	 */
	public long getN() {
		return moment.getN();
	}

	/**
	 * Returns the arithmetic mean of the entries in the specified portion of
	 * the input array, or <code>Double.NaN</code> if the designated subarray is
	 * empty.
	 * <p>
	 * Throws <code>IllegalArgumentException</code> if the array is null.
	 * </p>
	 * <p>
	 * See {@link Mean} for details on the computing algorithm.
	 * </p>
	 * 
	 * @param values
	 *            the input array
	 * @param begin
	 *            index of the first array element to include
	 * @param length
	 *            the number of elements to include
	 * @return the mean of the values or Double.NaN if length = 0
	 * @throws IllegalArgumentException
	 *             if the array is null or the array index parameters are not
	 *             valid
	 */
	@Override
	public double evaluate(final double[] values, final int begin, final int length) {
		if (test(values, begin, length)) {
			Sum sum = new Sum();
			double sampleSize = length;

			// Compute initial estimate using definitional formula
			double xbar = sum.evaluate(values, begin, length) / sampleSize;

			// Compute correction factor in second pass
			double correction = 0;
			for (int i = begin; i < begin + length; i++) {
				correction += (values[i] - xbar);
			}
			return xbar + (correction / sampleSize);
		}
		return Double.NaN;
	}

	/**
	 * {@inheritDoc}
	 */
	@Override
	public Mean copy() {
		Mean result = new Mean();
		copy(this, result);
		return result;
	}

	/**
	 * Copies source to dest.
	 * <p>
	 * Neither source nor dest can be null.
	 * </p>
	 * 
	 * @param source
	 *            Mean to copy
	 * @param dest
	 *            Mean to copy to
	 * @throws NullPointerException
	 *             if either source or dest is null
	 */
	public static void copy(Mean source, Mean dest) {
		dest.incMoment = source.incMoment;
		dest.moment = source.moment.copy();
	}
}
