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Diffstat (limited to 'src/main/java/org/apache/commons/math/stat/StatUtils.java')
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diff --git a/src/main/java/org/apache/commons/math/stat/StatUtils.java b/src/main/java/org/apache/commons/math/stat/StatUtils.java new file mode 100644 index 0000000..7ae1e17 --- /dev/null +++ b/src/main/java/org/apache/commons/math/stat/StatUtils.java @@ -0,0 +1,663 @@ +/* + * 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; + +import org.apache.commons.math.MathRuntimeException; +import org.apache.commons.math.exception.util.LocalizedFormats; +import org.apache.commons.math.stat.descriptive.DescriptiveStatistics; +import org.apache.commons.math.stat.descriptive.UnivariateStatistic; +import org.apache.commons.math.stat.descriptive.moment.GeometricMean; +import org.apache.commons.math.stat.descriptive.moment.Mean; +import org.apache.commons.math.stat.descriptive.moment.Variance; +import org.apache.commons.math.stat.descriptive.rank.Max; +import org.apache.commons.math.stat.descriptive.rank.Min; +import org.apache.commons.math.stat.descriptive.rank.Percentile; +import org.apache.commons.math.stat.descriptive.summary.Product; +import org.apache.commons.math.stat.descriptive.summary.Sum; +import org.apache.commons.math.stat.descriptive.summary.SumOfLogs; +import org.apache.commons.math.stat.descriptive.summary.SumOfSquares; + +/** + * StatUtils provides static methods for computing statistics based on data + * stored in double[] arrays. + * + * @version $Revision: 1073276 $ $Date: 2011-02-22 10:34:52 +0100 (mar. 22 févr. 2011) $ + */ +public final class StatUtils { + + /** sum */ + private static final UnivariateStatistic SUM = new Sum(); + + /** sumSq */ + private static final UnivariateStatistic SUM_OF_SQUARES = new SumOfSquares(); + + /** prod */ + private static final UnivariateStatistic PRODUCT = new Product(); + + /** sumLog */ + private static final UnivariateStatistic SUM_OF_LOGS = new SumOfLogs(); + + /** min */ + private static final UnivariateStatistic MIN = new Min(); + + /** max */ + private static final UnivariateStatistic MAX = new Max(); + + /** mean */ + private static final UnivariateStatistic MEAN = new Mean(); + + /** variance */ + private static final Variance VARIANCE = new Variance(); + + /** percentile */ + private static final Percentile PERCENTILE = new Percentile(); + + /** geometric mean */ + private static final GeometricMean GEOMETRIC_MEAN = new GeometricMean(); + + /** + * Private Constructor + */ + private StatUtils() { + } + + /** + * Returns the sum of the values in the input array, or + * <code>Double.NaN</code> if the array is empty. + * <p> + * Throws <code>IllegalArgumentException</code> if the input array + * is null.</p> + * + * @param values array of values to sum + * @return the sum of the values or <code>Double.NaN</code> if the array + * is empty + * @throws IllegalArgumentException if the array is null + */ + public static double sum(final double[] values) { + return SUM.evaluate(values); + } + + /** + * Returns the sum 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> + * + * @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 sum 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 static double sum(final double[] values, final int begin, + final int length) { + return SUM.evaluate(values, begin, length); + } + + /** + * Returns the sum of the squares of the entries in the input array, or + * <code>Double.NaN</code> if the array is empty. + * <p> + * Throws <code>IllegalArgumentException</code> if the array is null.</p> + * + * @param values input array + * @return the sum of the squared values or <code>Double.NaN</code> if the + * array is empty + * @throws IllegalArgumentException if the array is null + */ + public static double sumSq(final double[] values) { + return SUM_OF_SQUARES.evaluate(values); + } + + /** + * Returns the sum of the squares 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> + * + * @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 sum of the squares 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 static double sumSq(final double[] values, final int begin, + final int length) { + return SUM_OF_SQUARES.evaluate(values, begin, length); + } + + /** + * Returns the product of the entries in the input array, or + * <code>Double.NaN</code> if the array is empty. + * <p> + * Throws <code>IllegalArgumentException</code> if the array is null.</p> + * + * @param values the input array + * @return the product of the values or Double.NaN if the array is empty + * @throws IllegalArgumentException if the array is null + */ + public static double product(final double[] values) { + return PRODUCT.evaluate(values); + } + + /** + * Returns the product 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> + * + * @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 product 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 static double product(final double[] values, final int begin, + final int length) { + return PRODUCT.evaluate(values, begin, length); + } + + /** + * Returns the sum of the natural logs of the entries in the input array, or + * <code>Double.NaN</code> if the array is empty. + * <p> + * Throws <code>IllegalArgumentException</code> if the array is null.</p> + * <p> + * See {@link org.apache.commons.math.stat.descriptive.summary.SumOfLogs}. + * </p> + * + * @param values the input array + * @return the sum of the natural logs of the values or Double.NaN if + * the array is empty + * @throws IllegalArgumentException if the array is null + */ + public static double sumLog(final double[] values) { + return SUM_OF_LOGS.evaluate(values); + } + + /** + * Returns the sum of the natural logs 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 org.apache.commons.math.stat.descriptive.summary.SumOfLogs}. + * </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 sum of the natural logs 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 static double sumLog(final double[] values, final int begin, + final int length) { + return SUM_OF_LOGS.evaluate(values, begin, length); + } + + /** + * Returns the arithmetic mean of the entries in the input array, or + * <code>Double.NaN</code> if the array is empty. + * <p> + * Throws <code>IllegalArgumentException</code> if the array is null.</p> + * <p> + * See {@link org.apache.commons.math.stat.descriptive.moment.Mean} for + * details on the computing algorithm.</p> + * + * @param values the input array + * @return the mean of the values or Double.NaN if the array is empty + * @throws IllegalArgumentException if the array is null + */ + public static double mean(final double[] values) { + return MEAN.evaluate(values); + } + + /** + * 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 org.apache.commons.math.stat.descriptive.moment.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 + */ + public static double mean(final double[] values, final int begin, + final int length) { + return MEAN.evaluate(values, begin, length); + } + + /** + * Returns the geometric mean of the entries in the input array, or + * <code>Double.NaN</code> if the array is empty. + * <p> + * Throws <code>IllegalArgumentException</code> if the array is null.</p> + * <p> + * See {@link org.apache.commons.math.stat.descriptive.moment.GeometricMean} + * for details on the computing algorithm.</p> + * + * @param values the input array + * @return the geometric mean of the values or Double.NaN if the array is empty + * @throws IllegalArgumentException if the array is null + */ + public static double geometricMean(final double[] values) { + return GEOMETRIC_MEAN.evaluate(values); + } + + /** + * Returns the geometric 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 org.apache.commons.math.stat.descriptive.moment.GeometricMean} + * 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 geometric 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 + */ + public static double geometricMean(final double[] values, final int begin, + final int length) { + return GEOMETRIC_MEAN.evaluate(values, begin, length); + } + + + /** + * Returns the variance of the entries in the input array, or + * <code>Double.NaN</code> if the array is empty. + * <p> + * See {@link org.apache.commons.math.stat.descriptive.moment.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> + * + * @param values the input array + * @return the variance of the values or Double.NaN if the array is empty + * @throws IllegalArgumentException if the array is null + */ + public static double variance(final double[] values) { + return VARIANCE.evaluate(values); + } + + /** + * 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 org.apache.commons.math.stat.descriptive.moment.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 or the + * array index parameters are not valid.</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 + */ + public static double variance(final double[] values, final int begin, + final int length) { + return VARIANCE.evaluate(values, begin, 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 org.apache.commons.math.stat.descriptive.moment.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 or the + * array index parameters are not valid.</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 static double variance(final double[] values, final double mean, + final int begin, final int length) { + return VARIANCE.evaluate(values, mean, begin, length); + } + + /** + * 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 org.apache.commons.math.stat.descriptive.moment.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> + * + * @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 static double variance(final double[] values, final double mean) { + return VARIANCE.evaluate(values, mean); + } + + /** + * Returns the maximum of the entries in the input array, or + * <code>Double.NaN</code> if the array is empty. + * <p> + * Throws <code>IllegalArgumentException</code> if the array is null.</p> + * <p> + * <ul> + * <li>The result is <code>NaN</code> iff all values are <code>NaN</code> + * (i.e. <code>NaN</code> values have no impact on the value of the statistic).</li> + * <li>If any of the values equals <code>Double.POSITIVE_INFINITY</code>, + * the result is <code>Double.POSITIVE_INFINITY.</code></li> + * </ul></p> + * + * @param values the input array + * @return the maximum of the values or Double.NaN if the array is empty + * @throws IllegalArgumentException if the array is null + */ + public static double max(final double[] values) { + return MAX.evaluate(values); + } + + /** + * Returns the maximum 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 or + * the array index parameters are not valid.</p> + * <p> + * <ul> + * <li>The result is <code>NaN</code> iff all values are <code>NaN</code> + * (i.e. <code>NaN</code> values have no impact on the value of the statistic).</li> + * <li>If any of the values equals <code>Double.POSITIVE_INFINITY</code>, + * the result is <code>Double.POSITIVE_INFINITY.</code></li> + * </ul></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 maximum 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 static double max(final double[] values, final int begin, + final int length) { + return MAX.evaluate(values, begin, length); + } + + /** + * Returns the minimum of the entries in the input array, or + * <code>Double.NaN</code> if the array is empty. + * <p> + * Throws <code>IllegalArgumentException</code> if the array is null.</p> + * <p> + * <ul> + * <li>The result is <code>NaN</code> iff all values are <code>NaN</code> + * (i.e. <code>NaN</code> values have no impact on the value of the statistic).</li> + * <li>If any of the values equals <code>Double.NEGATIVE_INFINITY</code>, + * the result is <code>Double.NEGATIVE_INFINITY.</code></li> + * </ul> </p> + * + * @param values the input array + * @return the minimum of the values or Double.NaN if the array is empty + * @throws IllegalArgumentException if the array is null + */ + public static double min(final double[] values) { + return MIN.evaluate(values); + } + + /** + * Returns the minimum 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 or + * the array index parameters are not valid.</p> + * <p> + * <ul> + * <li>The result is <code>NaN</code> iff all values are <code>NaN</code> + * (i.e. <code>NaN</code> values have no impact on the value of the statistic).</li> + * <li>If any of the values equals <code>Double.NEGATIVE_INFINITY</code>, + * the result is <code>Double.NEGATIVE_INFINITY.</code></li> + * </ul></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 minimum 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 static double min(final double[] values, final int begin, + final int length) { + return MIN.evaluate(values, begin, length); + } + + /** + * Returns an estimate of the <code>p</code>th percentile of the values + * in the <code>values</code> array. + * <p> + * <ul> + * <li>Returns <code>Double.NaN</code> if <code>values</code> has length + * <code>0</code></li></p> + * <li>Returns (for any value of <code>p</code>) <code>values[0]</code> + * if <code>values</code> has length <code>1</code></li> + * <li>Throws <code>IllegalArgumentException</code> if <code>values</code> + * is null or p is not a valid quantile value (p must be greater than 0 + * and less than or equal to 100)</li> + * </ul></p> + * <p> + * See {@link org.apache.commons.math.stat.descriptive.rank.Percentile} for + * a description of the percentile estimation algorithm used.</p> + * + * @param values input array of values + * @param p the percentile value to compute + * @return the percentile value or Double.NaN if the array is empty + * @throws IllegalArgumentException if <code>values</code> is null + * or p is invalid + */ + public static double percentile(final double[] values, final double p) { + return PERCENTILE.evaluate(values,p); + } + + /** + * Returns an estimate of the <code>p</code>th percentile of the values + * in the <code>values</code> array, starting with the element in (0-based) + * position <code>begin</code> in the array and including <code>length</code> + * values. + * <p> + * <ul> + * <li>Returns <code>Double.NaN</code> if <code>length = 0</code></li> + * <li>Returns (for any value of <code>p</code>) <code>values[begin]</code> + * if <code>length = 1 </code></li> + * <li>Throws <code>IllegalArgumentException</code> if <code>values</code> + * is null , <code>begin</code> or <code>length</code> is invalid, or + * <code>p</code> is not a valid quantile value (p must be greater than 0 + * and less than or equal to 100)</li> + * </ul></p> + * <p> + * See {@link org.apache.commons.math.stat.descriptive.rank.Percentile} for + * a description of the percentile estimation algorithm used.</p> + * + * @param values array of input values + * @param p the percentile to compute + * @param begin the first (0-based) element to include in the computation + * @param length the number of array elements to include + * @return the percentile value + * @throws IllegalArgumentException if the parameters are not valid or the + * input array is null + */ + public static double percentile(final double[] values, final int begin, + final int length, final double p) { + return PERCENTILE.evaluate(values, begin, length, p); + } + + /** + * Returns the sum of the (signed) differences between corresponding elements of the + * input arrays -- i.e., sum(sample1[i] - sample2[i]). + * + * @param sample1 the first array + * @param sample2 the second array + * @return sum of paired differences + * @throws IllegalArgumentException if the arrays do not have the same + * (positive) length + */ + public static double sumDifference(final double[] sample1, final double[] sample2) + throws IllegalArgumentException { + int n = sample1.length; + if (n != sample2.length) { + throw MathRuntimeException.createIllegalArgumentException( + LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE, n, sample2.length); + } + if (n < 1) { + throw MathRuntimeException.createIllegalArgumentException( + LocalizedFormats.INSUFFICIENT_DIMENSION, sample2.length, 1); + } + double result = 0; + for (int i = 0; i < n; i++) { + result += sample1[i] - sample2[i]; + } + return result; + } + + /** + * Returns the mean of the (signed) differences between corresponding elements of the + * input arrays -- i.e., sum(sample1[i] - sample2[i]) / sample1.length. + * + * @param sample1 the first array + * @param sample2 the second array + * @return mean of paired differences + * @throws IllegalArgumentException if the arrays do not have the same + * (positive) length + */ + public static double meanDifference(final double[] sample1, final double[] sample2) + throws IllegalArgumentException { + return sumDifference(sample1, sample2) / sample1.length; + } + + /** + * Returns the variance of the (signed) differences between corresponding elements of the + * input arrays -- i.e., var(sample1[i] - sample2[i]). + * + * @param sample1 the first array + * @param sample2 the second array + * @param meanDifference the mean difference between corresponding entries + * @see #meanDifference(double[],double[]) + * @return variance of paired differences + * @throws IllegalArgumentException if the arrays do not have the same + * length or their common length is less than 2. + */ + public static double varianceDifference(final double[] sample1, final double[] sample2, + double meanDifference) throws IllegalArgumentException { + double sum1 = 0d; + double sum2 = 0d; + double diff = 0d; + int n = sample1.length; + if (n != sample2.length) { + throw MathRuntimeException.createIllegalArgumentException( + LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE, n, sample2.length); + } + if (n < 2) { + throw MathRuntimeException.createIllegalArgumentException( + LocalizedFormats.INSUFFICIENT_DIMENSION, n, 2); + } + for (int i = 0; i < n; i++) { + diff = sample1[i] - sample2[i]; + sum1 += (diff - meanDifference) *(diff - meanDifference); + sum2 += diff - meanDifference; + } + return (sum1 - (sum2 * sum2 / n)) / (n - 1); + } + + + /** + * Normalize (standardize) the series, so in the end it is having a mean of 0 and a standard deviation of 1. + * + * @param sample sample to normalize + * @return normalized (standardized) sample + * @since 2.2 + */ + public static double[] normalize(final double[] sample) { + DescriptiveStatistics stats = new DescriptiveStatistics(); + + // Add the data from the series to stats + for (int i = 0; i < sample.length; i++) { + stats.addValue(sample[i]); + } + + // Compute mean and standard deviation + double mean = stats.getMean(); + double standardDeviation = stats.getStandardDeviation(); + + // initialize the standardizedSample, which has the same length as the sample + double[] standardizedSample = new double[sample.length]; + + for (int i = 0; i < sample.length; i++) { + // z = (x- mean)/standardDeviation + standardizedSample[i] = (sample[i] - mean) / standardDeviation; + } + return standardizedSample; + } + +} |