/*
 * 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;

/**
 * Computes the sample standard deviation. The standard deviation is the
 * positive square root of the variance. This implementation wraps a
 * {@link Variance} instance. The <code>isBiasCorrected</code> property of the
 * wrapped Variance instance is exposed, so that this class can be used to
 * compute both the "sample standard deviation" (the square root of the
 * bias-corrected "sample variance") or the "population standard deviation" (the
 * square root of the non-bias-corrected "population variance"). See
 * {@link Variance} for more information.
 * <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.
 * </p>
 * 
 * @version $Revision$ $Date$
 */
public class StandardDeviation extends AbstractStorelessUnivariateStatistic implements Serializable {

	/** Serializable version identifier */
	private static final long serialVersionUID = 5728716329662425188L;

	/** Wrapped Variance instance */
	private Variance variance = null;

	/**
	 * Constructs a StandardDeviation. Sets the underlying {@link Variance}
	 * instance's <code>isBiasCorrected</code> property to true.
	 */
	public StandardDeviation() {
		variance = new Variance();
	}

	/**
	 * Constructs a StandardDeviation from an external second moment.
	 * 
	 * @param m2
	 *            the external moment
	 */
	public StandardDeviation(final SecondMoment m2) {
		variance = new Variance(m2);
	}

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

	/**
	 * Contructs a StandardDeviation with the specified value for the
	 * <code>isBiasCorrected</code> property. If this property is set to
	 * <code>true</code>, the {@link Variance} used in computing results will
	 * use the bias-corrected, or "sample" formula. See {@link Variance} for
	 * details.
	 * 
	 * @param isBiasCorrected
	 *            whether or not the variance computation will use the
	 *            bias-corrected formula
	 */
	public StandardDeviation(boolean isBiasCorrected) {
		variance = new Variance(isBiasCorrected);
	}

	/**
	 * Contructs a StandardDeviation with the specified value for the
	 * <code>isBiasCorrected</code> property and the supplied external moment.
	 * If <code>isBiasCorrected</code> is set to <code>true</code>, the
	 * {@link Variance} used in computing results will use the bias-corrected,
	 * or "sample" formula. See {@link Variance} for details.
	 * 
	 * @param isBiasCorrected
	 *            whether or not the variance computation will use the
	 *            bias-corrected formula
	 * @param m2
	 *            the external moment
	 */
	public StandardDeviation(boolean isBiasCorrected, SecondMoment m2) {
		variance = new Variance(isBiasCorrected, m2);
	}

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

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

	/**
	 * {@inheritDoc}
	 */
	@Override
	public double getResult() {
		return Math.sqrt(variance.getResult());
	}

	/**
	 * {@inheritDoc}
	 */
	@Override
	public void clear() {
		variance.clear();
	}

	/**
	 * Returns the Standard Deviation of the entries in the input array, or
	 * <code>Double.NaN</code> if the array is empty.
	 * <p>
	 * Returns 0 for a single-value (i.e. length = 1) sample.
	 * </p>
	 * <p>
	 * Throws <code>IllegalArgumentException</code> if the array is null.
	 * </p>
	 * <p>
	 * Does not change the internal state of the statistic.
	 * </p>
	 * 
	 * @param values
	 *            the input array
	 * @return the standard deviation of the values or Double.NaN if length = 0
	 * @throws IllegalArgumentException
	 *             if the array is null
	 */
	@Override
	public double evaluate(final double[] values) {
		return Math.sqrt(variance.evaluate(values));
	}

	/**
	 * Returns the Standard Deviation of the entries in the specified portion of
	 * the input array, or <code>Double.NaN</code> if the designated subarray is
	 * empty.
	 * <p>
	 * Returns 0 for a single-value (i.e. length = 1) sample.
	 * </p>
	 * <p>
	 * Throws <code>IllegalArgumentException</code> if the array is null.
	 * </p>
	 * <p>
	 * Does not change the internal state of the statistic.
	 * </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 standard deviation 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) {
		return Math.sqrt(variance.evaluate(values, begin, length));
	}

	/**
	 * Returns the Standard Deviation of the entries in the specified portion of
	 * the input array, using the precomputed mean value. Returns
	 * <code>Double.NaN</code> if the designated subarray is empty.
	 * <p>
	 * Returns 0 for a single-value (i.e. length = 1) sample.
	 * </p>
	 * <p>
	 * The formula used assumes that the supplied mean value is the arithmetic
	 * mean of the sample data, not a known population parameter. This method is
	 * supplied only to save computation when the mean has already been
	 * computed.
	 * </p>
	 * <p>
	 * Throws <code>IllegalArgumentException</code> if the array is null.
	 * </p>
	 * <p>
	 * Does not change the internal state of the statistic.
	 * </p>
	 * 
	 * @param values
	 *            the input array
	 * @param mean
	 *            the precomputed mean value
	 * @param begin
	 *            index of the first array element to include
	 * @param length
	 *            the number of elements to include
	 * @return the standard deviation of the values or Double.NaN if length = 0
	 * @throws IllegalArgumentException
	 *             if the array is null or the array index parameters are not
	 *             valid
	 */
	public double evaluate(final double[] values, final double mean, final int begin, final int length) {
		return Math.sqrt(variance.evaluate(values, mean, begin, length));
	}

	/**
	 * Returns the Standard Deviation of the entries in the input array, using
	 * the precomputed mean value. Returns <code>Double.NaN</code> if the
	 * designated subarray is empty.
	 * <p>
	 * Returns 0 for a single-value (i.e. length = 1) sample.
	 * </p>
	 * <p>
	 * The formula used assumes that the supplied mean value is the arithmetic
	 * mean of the sample data, not a known population parameter. This method is
	 * supplied only to save computation when the mean has already been
	 * computed.
	 * </p>
	 * <p>
	 * Throws <code>IllegalArgumentException</code> if the array is null.
	 * </p>
	 * <p>
	 * Does not change the internal state of the statistic.
	 * </p>
	 * 
	 * @param values
	 *            the input array
	 * @param mean
	 *            the precomputed mean value
	 * @return the standard deviation of the values or Double.NaN if length = 0
	 * @throws IllegalArgumentException
	 *             if the array is null
	 */
	public double evaluate(final double[] values, final double mean) {
		return Math.sqrt(variance.evaluate(values, mean));
	}

	/**
	 * @return Returns the isBiasCorrected.
	 */
	public boolean isBiasCorrected() {
		return variance.isBiasCorrected();
	}

	/**
	 * @param isBiasCorrected
	 *            The isBiasCorrected to set.
	 */
	public void setBiasCorrected(boolean isBiasCorrected) {
		variance.setBiasCorrected(isBiasCorrected);
	}

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

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

}
