| /* |
| * 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.exception.NullArgumentException; |
| import org.apache.commons.math.exception.util.LocalizedFormats; |
| import org.apache.commons.math.stat.descriptive.WeightedEvaluation; |
| import org.apache.commons.math.stat.descriptive.AbstractStorelessUnivariateStatistic; |
| |
| /** |
| * Computes the variance of the available values. By default, the unbiased |
| * "sample variance" definitional formula is used: |
| * <p> |
| * variance = sum((x_i - mean)^2) / (n - 1) </p> |
| * <p> |
| * where mean is the {@link Mean} and <code>n</code> is the number |
| * of sample observations.</p> |
| * <p> |
| * The definitional formula does not have good numerical properties, so |
| * this implementation does not compute the statistic using the definitional |
| * formula. <ul> |
| * <li> The <code>getResult</code> method computes the variance using |
| * updating formulas based on West's algorithm, as described in |
| * <a href="http://doi.acm.org/10.1145/359146.359152"> Chan, T. F. and |
| * J. G. Lewis 1979, <i>Communications of the ACM</i>, |
| * vol. 22 no. 9, pp. 526-531.</a></li> |
| * <li> The <code>evaluate</code> methods leverage the fact that they have the |
| * full array of values in memory to execute a two-pass algorithm. |
| * Specifically, these methods use the "corrected two-pass algorithm" from |
| * Chan, Golub, Levesque, <i>Algorithms for Computing the Sample Variance</i>, |
| * American Statistician, vol. 37, no. 3 (1983) pp. 242-247.</li></ul> |
| * Note that adding values using <code>increment</code> or |
| * <code>incrementAll</code> and then executing <code>getResult</code> will |
| * sometimes give a different, less accurate, result than executing |
| * <code>evaluate</code> with the full array of values. The former approach |
| * should only be used when the full array of values is not available.</p> |
| * <p> |
| * The "population variance" ( sum((x_i - mean)^2) / n ) can also |
| * be computed using this statistic. The <code>isBiasCorrected</code> |
| * property determines whether the "population" or "sample" value is |
| * returned by the <code>evaluate</code> and <code>getResult</code> methods. |
| * To compute population variances, set this property to <code>false.</code> |
| * </p> |
| * <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: 1006299 $ $Date: 2010-10-10 16:47:17 +0200 (dim. 10 oct. 2010) $ |
| */ |
| public class Variance extends AbstractStorelessUnivariateStatistic implements Serializable, WeightedEvaluation { |
| |
| /** Serializable version identifier */ |
| private static final long serialVersionUID = -9111962718267217978L; |
| |
| /** SecondMoment is used in incremental calculation of Variance*/ |
| protected SecondMoment moment = null; |
| |
| /** |
| * Boolean test to determine if this Variance should also increment |
| * the second moment, this evaluates to false when this Variance is |
| * constructed with an external SecondMoment as a parameter. |
| */ |
| protected boolean incMoment = true; |
| |
| /** |
| * Determines whether or not bias correction is applied when computing the |
| * value of the statisic. True means that bias is corrected. See |
| * {@link Variance} for details on the formula. |
| */ |
| private boolean isBiasCorrected = true; |
| |
| /** |
| * Constructs a Variance with default (true) <code>isBiasCorrected</code> |
| * property. |
| */ |
| public Variance() { |
| moment = new SecondMoment(); |
| } |
| |
| /** |
| * Constructs a Variance based on an external second moment. |
| * |
| * @param m2 the SecondMoment (Third or Fourth moments work |
| * here as well.) |
| */ |
| public Variance(final SecondMoment m2) { |
| incMoment = false; |
| this.moment = m2; |
| } |
| |
| /** |
| * Constructs a Variance with the specified <code>isBiasCorrected</code> |
| * property |
| * |
| * @param isBiasCorrected setting for bias correction - true means |
| * bias will be corrected and is equivalent to using the argumentless |
| * constructor |
| */ |
| public Variance(boolean isBiasCorrected) { |
| moment = new SecondMoment(); |
| this.isBiasCorrected = isBiasCorrected; |
| } |
| |
| /** |
| * Constructs a Variance with the specified <code>isBiasCorrected</code> |
| * property and the supplied external second moment. |
| * |
| * @param isBiasCorrected setting for bias correction - true means |
| * bias will be corrected |
| * @param m2 the SecondMoment (Third or Fourth moments work |
| * here as well.) |
| */ |
| public Variance(boolean isBiasCorrected, SecondMoment m2) { |
| incMoment = false; |
| this.moment = m2; |
| this.isBiasCorrected = isBiasCorrected; |
| } |
| |
| /** |
| * Copy constructor, creates a new {@code Variance} identical |
| * to the {@code original} |
| * |
| * @param original the {@code Variance} instance to copy |
| */ |
| public Variance(Variance original) { |
| copy(original, this); |
| } |
| |
| /** |
| * {@inheritDoc} |
| * <p>If all values are available, it is more accurate to use |
| * {@link #evaluate(double[])} rather than adding values one at a time |
| * using this method and then executing {@link #getResult}, since |
| * <code>evaluate</code> leverages the fact that is has the full |
| * list of values together to execute a two-pass algorithm. |
| * See {@link Variance}.</p> |
| */ |
| @Override |
| public void increment(final double d) { |
| if (incMoment) { |
| moment.increment(d); |
| } |
| } |
| |
| /** |
| * {@inheritDoc} |
| */ |
| @Override |
| public double getResult() { |
| if (moment.n == 0) { |
| return Double.NaN; |
| } else if (moment.n == 1) { |
| return 0d; |
| } else { |
| if (isBiasCorrected) { |
| return moment.m2 / (moment.n - 1d); |
| } else { |
| return moment.m2 / (moment.n); |
| } |
| } |
| } |
| |
| /** |
| * {@inheritDoc} |
| */ |
| public long getN() { |
| return moment.getN(); |
| } |
| |
| /** |
| * {@inheritDoc} |
| */ |
| @Override |
| public void clear() { |
| if (incMoment) { |
| moment.clear(); |
| } |
| } |
| |
| /** |
| * Returns the variance of the entries in the input array, or |
| * <code>Double.NaN</code> if the array is empty. |
| * <p> |
| * See {@link Variance} for details on the computing algorithm.</p> |
| * <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 variance of the values or Double.NaN if length = 0 |
| * @throws IllegalArgumentException if the array is null |
| */ |
| @Override |
| public double evaluate(final double[] values) { |
| if (values == null) { |
| throw new NullArgumentException(LocalizedFormats.INPUT_ARRAY); |
| } |
| return evaluate(values, 0, values.length); |
| } |
| |
| /** |
| * Returns the variance of the entries in the specified portion of |
| * the input array, or <code>Double.NaN</code> if the designated subarray |
| * is empty. |
| * <p> |
| * See {@link Variance} for details on the computing algorithm.</p> |
| * <p> |
| * Returns 0 for a single-value (i.e. length = 1) sample.</p> |
| * <p> |
| * Does not change the internal state of the statistic.</p> |
| * <p> |
| * Throws <code>IllegalArgumentException</code> if the array is null.</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 variance 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) { |
| |
| double var = Double.NaN; |
| |
| if (test(values, begin, length)) { |
| clear(); |
| if (length == 1) { |
| var = 0.0; |
| } else if (length > 1) { |
| Mean mean = new Mean(); |
| double m = mean.evaluate(values, begin, length); |
| var = evaluate(values, m, begin, length); |
| } |
| } |
| return var; |
| } |
| |
| /** |
| * <p>Returns the weighted variance of the entries in the specified portion of |
| * the input array, or <code>Double.NaN</code> if the designated subarray |
| * is empty.</p> |
| * <p> |
| * Uses the formula <pre> |
| * Σ(weights[i]*(values[i] - weightedMean)<sup>2</sup>)/(Σ(weights[i]) - 1) |
| * </pre> |
| * where weightedMean is the weighted mean</p> |
| * <p> |
| * This formula will not return the same result as the unweighted variance when all |
| * weights are equal, unless all weights are equal to 1. The formula assumes that |
| * weights are to be treated as "expansion values," as will be the case if for example |
| * the weights represent frequency counts. To normalize weights so that the denominator |
| * in the variance computation equals the length of the input vector minus one, use <pre> |
| * <code>evaluate(values, MathUtils.normalizeArray(weights, values.length)); </code> |
| * </pre> |
| * <p> |
| * Returns 0 for a single-value (i.e. length = 1) sample.</p> |
| * <p> |
| * Throws <code>IllegalArgumentException</code> if any of the following are true: |
| * <ul><li>the values array is null</li> |
| * <li>the weights array is null</li> |
| * <li>the weights array does not have the same length as the values array</li> |
| * <li>the weights array contains one or more infinite values</li> |
| * <li>the weights array contains one or more NaN values</li> |
| * <li>the weights array contains negative values</li> |
| * <li>the start and length arguments do not determine a valid array</li> |
| * </ul></p> |
| * <p> |
| * Does not change the internal state of the statistic.</p> |
| * <p> |
| * Throws <code>IllegalArgumentException</code> if either array is null.</p> |
| * |
| * @param values the input array |
| * @param weights the weights array |
| * @param begin index of the first array element to include |
| * @param length the number of elements to include |
| * @return the weighted variance of the values or Double.NaN if length = 0 |
| * @throws IllegalArgumentException if the parameters are not valid |
| * @since 2.1 |
| */ |
| public double evaluate(final double[] values, final double[] weights, |
| final int begin, final int length) { |
| |
| double var = Double.NaN; |
| |
| if (test(values, weights,begin, length)) { |
| clear(); |
| if (length == 1) { |
| var = 0.0; |
| } else if (length > 1) { |
| Mean mean = new Mean(); |
| double m = mean.evaluate(values, weights, begin, length); |
| var = evaluate(values, weights, m, begin, length); |
| } |
| } |
| return var; |
| } |
| |
| /** |
| * <p> |
| * Returns the weighted variance of the entries in the the input array.</p> |
| * <p> |
| * Uses the formula <pre> |
| * Σ(weights[i]*(values[i] - weightedMean)<sup>2</sup>)/(Σ(weights[i]) - 1) |
| * </pre> |
| * where weightedMean is the weighted mean</p> |
| * <p> |
| * This formula will not return the same result as the unweighted variance when all |
| * weights are equal, unless all weights are equal to 1. The formula assumes that |
| * weights are to be treated as "expansion values," as will be the case if for example |
| * the weights represent frequency counts. To normalize weights so that the denominator |
| * in the variance computation equals the length of the input vector minus one, use <pre> |
| * <code>evaluate(values, MathUtils.normalizeArray(weights, values.length)); </code> |
| * </pre> |
| * <p> |
| * Returns 0 for a single-value (i.e. length = 1) sample.</p> |
| * <p> |
| * Throws <code>IllegalArgumentException</code> if any of the following are true: |
| * <ul><li>the values array is null</li> |
| * <li>the weights array is null</li> |
| * <li>the weights array does not have the same length as the values array</li> |
| * <li>the weights array contains one or more infinite values</li> |
| * <li>the weights array contains one or more NaN values</li> |
| * <li>the weights array contains negative values</li> |
| * </ul></p> |
| * <p> |
| * Does not change the internal state of the statistic.</p> |
| * <p> |
| * Throws <code>IllegalArgumentException</code> if either array is null.</p> |
| * |
| * @param values the input array |
| * @param weights the weights array |
| * @return the weighted variance of the values |
| * @throws IllegalArgumentException if the parameters are not valid |
| * @since 2.1 |
| */ |
| public double evaluate(final double[] values, final double[] weights) { |
| return evaluate(values, weights, 0, values.length); |
| } |
| |
| /** |
| * Returns the variance 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> |
| * See {@link Variance} for details on the computing algorithm.</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> |
| * 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 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 variance 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) { |
| |
| double var = Double.NaN; |
| |
| if (test(values, begin, length)) { |
| if (length == 1) { |
| var = 0.0; |
| } else if (length > 1) { |
| double accum = 0.0; |
| double dev = 0.0; |
| double accum2 = 0.0; |
| for (int i = begin; i < begin + length; i++) { |
| dev = values[i] - mean; |
| accum += dev * dev; |
| accum2 += dev; |
| } |
| double len = length; |
| if (isBiasCorrected) { |
| var = (accum - (accum2 * accum2 / len)) / (len - 1.0); |
| } else { |
| var = (accum - (accum2 * accum2 / len)) / len; |
| } |
| } |
| } |
| return var; |
| } |
| |
| /** |
| * Returns the variance of the entries in the input array, using the |
| * precomputed mean value. Returns <code>Double.NaN</code> if the array |
| * is empty. |
| * <p> |
| * See {@link Variance} for details on the computing algorithm.</p> |
| * <p> |
| * If <code>isBiasCorrected</code> is <code>true</code> the formula used |
| * assumes that the supplied mean value is the arithmetic mean of the |
| * sample data, not a known population parameter. If the mean is a known |
| * population parameter, or if the "population" version of the variance is |
| * desired, set <code>isBiasCorrected</code> to <code>false</code> before |
| * invoking this method.</p> |
| * <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 mean the precomputed mean value |
| * @return the variance of the values or Double.NaN if the array is empty |
| * @throws IllegalArgumentException if the array is null |
| */ |
| public double evaluate(final double[] values, final double mean) { |
| return evaluate(values, mean, 0, values.length); |
| } |
| |
| /** |
| * Returns the weighted variance of the entries in the specified portion of |
| * the input array, using the precomputed weighted mean value. Returns |
| * <code>Double.NaN</code> if the designated subarray is empty. |
| * <p> |
| * Uses the formula <pre> |
| * Σ(weights[i]*(values[i] - mean)<sup>2</sup>)/(Σ(weights[i]) - 1) |
| * </pre></p> |
| * <p> |
| * The formula used assumes that the supplied mean value is the weighted 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> |
| * This formula will not return the same result as the unweighted variance when all |
| * weights are equal, unless all weights are equal to 1. The formula assumes that |
| * weights are to be treated as "expansion values," as will be the case if for example |
| * the weights represent frequency counts. To normalize weights so that the denominator |
| * in the variance computation equals the length of the input vector minus one, use <pre> |
| * <code>evaluate(values, MathUtils.normalizeArray(weights, values.length), mean); </code> |
| * </pre> |
| * <p> |
| * Returns 0 for a single-value (i.e. length = 1) sample.</p> |
| * <p> |
| * Throws <code>IllegalArgumentException</code> if any of the following are true: |
| * <ul><li>the values array is null</li> |
| * <li>the weights array is null</li> |
| * <li>the weights array does not have the same length as the values array</li> |
| * <li>the weights array contains one or more infinite values</li> |
| * <li>the weights array contains one or more NaN values</li> |
| * <li>the weights array contains negative values</li> |
| * <li>the start and length arguments do not determine a valid array</li> |
| * </ul></p> |
| * <p> |
| * Does not change the internal state of the statistic.</p> |
| * |
| * @param values the input array |
| * @param weights the weights array |
| * @param mean the precomputed weighted mean value |
| * @param begin index of the first array element to include |
| * @param length the number of elements to include |
| * @return the variance of the values or Double.NaN if length = 0 |
| * @throws IllegalArgumentException if the parameters are not valid |
| * @since 2.1 |
| */ |
| public double evaluate(final double[] values, final double[] weights, |
| final double mean, final int begin, final int length) { |
| |
| double var = Double.NaN; |
| |
| if (test(values, weights, begin, length)) { |
| if (length == 1) { |
| var = 0.0; |
| } else if (length > 1) { |
| double accum = 0.0; |
| double dev = 0.0; |
| double accum2 = 0.0; |
| for (int i = begin; i < begin + length; i++) { |
| dev = values[i] - mean; |
| accum += weights[i] * (dev * dev); |
| accum2 += weights[i] * dev; |
| } |
| |
| double sumWts = 0; |
| for (int i = 0; i < weights.length; i++) { |
| sumWts += weights[i]; |
| } |
| |
| if (isBiasCorrected) { |
| var = (accum - (accum2 * accum2 / sumWts)) / (sumWts - 1.0); |
| } else { |
| var = (accum - (accum2 * accum2 / sumWts)) / sumWts; |
| } |
| } |
| } |
| return var; |
| } |
| |
| /** |
| * <p>Returns the weighted variance of the values in the input array, using |
| * the precomputed weighted mean value.</p> |
| * <p> |
| * Uses the formula <pre> |
| * Σ(weights[i]*(values[i] - mean)<sup>2</sup>)/(Σ(weights[i]) - 1) |
| * </pre></p> |
| * <p> |
| * The formula used assumes that the supplied mean value is the weighted 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> |
| * This formula will not return the same result as the unweighted variance when all |
| * weights are equal, unless all weights are equal to 1. The formula assumes that |
| * weights are to be treated as "expansion values," as will be the case if for example |
| * the weights represent frequency counts. To normalize weights so that the denominator |
| * in the variance computation equals the length of the input vector minus one, use <pre> |
| * <code>evaluate(values, MathUtils.normalizeArray(weights, values.length), mean); </code> |
| * </pre> |
| * <p> |
| * Returns 0 for a single-value (i.e. length = 1) sample.</p> |
| * <p> |
| * Throws <code>IllegalArgumentException</code> if any of the following are true: |
| * <ul><li>the values array is null</li> |
| * <li>the weights array is null</li> |
| * <li>the weights array does not have the same length as the values array</li> |
| * <li>the weights array contains one or more infinite values</li> |
| * <li>the weights array contains one or more NaN values</li> |
| * <li>the weights array contains negative values</li> |
| * </ul></p> |
| * <p> |
| * Does not change the internal state of the statistic.</p> |
| * |
| * @param values the input array |
| * @param weights the weights array |
| * @param mean the precomputed weighted mean value |
| * @return the variance of the values or Double.NaN if length = 0 |
| * @throws IllegalArgumentException if the parameters are not valid |
| * @since 2.1 |
| */ |
| public double evaluate(final double[] values, final double[] weights, final double mean) { |
| return evaluate(values, weights, mean, 0, values.length); |
| } |
| |
| /** |
| * @return Returns the isBiasCorrected. |
| */ |
| public boolean isBiasCorrected() { |
| return isBiasCorrected; |
| } |
| |
| /** |
| * @param biasCorrected The isBiasCorrected to set. |
| */ |
| public void setBiasCorrected(boolean biasCorrected) { |
| this.isBiasCorrected = biasCorrected; |
| } |
| |
| /** |
| * {@inheritDoc} |
| */ |
| @Override |
| public Variance copy() { |
| Variance result = new Variance(); |
| copy(this, result); |
| return result; |
| } |
| |
| /** |
| * Copies source to dest. |
| * <p>Neither source nor dest can be null.</p> |
| * |
| * @param source Variance to copy |
| * @param dest Variance to copy to |
| * @throws NullPointerException if either source or dest is null |
| */ |
| public static void copy(Variance source, Variance dest) { |
| if (source == null || |
| dest == null) { |
| throw new NullArgumentException(); |
| } |
| dest.setData(source.getDataRef()); |
| dest.moment = source.moment.copy(); |
| dest.isBiasCorrected = source.isBiasCorrected; |
| dest.incMoment = source.incMoment; |
| } |
| } |
