http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/inference/InferenceTestUtils.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/stat/inference/InferenceTestUtils.java b/src/main/java/org/apache/commons/math4/stat/inference/InferenceTestUtils.java index a0da22f..a06e3d1 100644 --- a/src/main/java/org/apache/commons/math4/stat/inference/InferenceTestUtils.java +++ b/src/main/java/org/apache/commons/math4/stat/inference/InferenceTestUtils.java @@ -65,6 +65,9 @@ public class InferenceTestUtils { // CHECKSTYLE: stop JavadocMethodCheck /** + * @param sample1 array of sample data values + * @param sample2 array of sample data values + * @return t statistic * @see org.apache.commons.math4.stat.inference.TTest#homoscedasticT(double[], double[]) */ public static double homoscedasticT(final double[] sample1, final double[] sample2) @@ -73,6 +76,9 @@ public class InferenceTestUtils { } /** + * @param sampleStats1 StatisticalSummary describing data from the first sample + * @param sampleStats2 StatisticalSummary describing data from the second sample + * @return t statistic * @see org.apache.commons.math4.stat.inference.TTest#homoscedasticT(org.apache.commons.math4.stat.descriptive.StatisticalSummary, org.apache.commons.math4.stat.descriptive.StatisticalSummary) */ public static double homoscedasticT(final StatisticalSummary sampleStats1, @@ -82,6 +88,11 @@ public class InferenceTestUtils { } /** + * @param sample1 array of sample data values + * @param sample2 array of sample data values + * @param alpha significance level of the test + * @return true if the null hypothesis can be rejected with + * confidence 1 - alpha * @see org.apache.commons.math4.stat.inference.TTest#homoscedasticTTest(double[], double[], double) */ public static boolean homoscedasticTTest(final double[] sample1, final double[] sample2, @@ -92,6 +103,9 @@ public class InferenceTestUtils { } /** + * @param sample1 array of sample data values + * @param sample2 array of sample data values + * @return p-value for t-test * @see org.apache.commons.math4.stat.inference.TTest#homoscedasticTTest(double[], double[]) */ public static double homoscedasticTTest(final double[] sample1, final double[] sample2) @@ -100,6 +114,9 @@ public class InferenceTestUtils { } /** + * @param sampleStats1 StatisticalSummary describing data from the first sample + * @param sampleStats2 StatisticalSummary describing data from the second sample + * @return p-value for t-test * @see org.apache.commons.math4.stat.inference.TTest#homoscedasticTTest(org.apache.commons.math4.stat.descriptive.StatisticalSummary, org.apache.commons.math4.stat.descriptive.StatisticalSummary) */ public static double homoscedasticTTest(final StatisticalSummary sampleStats1, @@ -109,6 +126,9 @@ public class InferenceTestUtils { } /** + * @param sample1 array of sample data values + * @param sample2 array of sample data values + * @return t statistic * @see org.apache.commons.math4.stat.inference.TTest#pairedT(double[], double[]) */ public static double pairedT(final double[] sample1, final double[] sample2) @@ -118,6 +138,11 @@ public class InferenceTestUtils { } /** + * @param sample1 array of sample data values + * @param sample2 array of sample data values + * @param alpha significance level of the test + * @return true if the null hypothesis can be rejected with + * confidence 1 - alpha * @see org.apache.commons.math4.stat.inference.TTest#pairedTTest(double[], double[], double) */ public static boolean pairedTTest(final double[] sample1, final double[] sample2, @@ -128,6 +153,9 @@ public class InferenceTestUtils { } /** + * @param sample1 array of sample data values + * @param sample2 array of sample data values + * @return p-value for t-test * @see org.apache.commons.math4.stat.inference.TTest#pairedTTest(double[], double[]) */ public static double pairedTTest(final double[] sample1, final double[] sample2) @@ -137,6 +165,9 @@ public class InferenceTestUtils { } /** + * @param mu comparison constant + * @param observed array of values + * @return t statistic * @see org.apache.commons.math4.stat.inference.TTest#t(double, double[]) */ public static double t(final double mu, final double[] observed) @@ -145,6 +176,9 @@ public class InferenceTestUtils { } /** + * @param mu comparison constant + * @param sampleStats DescriptiveStatistics holding sample summary statitstics + * @return t statistic * @see org.apache.commons.math4.stat.inference.TTest#t(double, org.apache.commons.math4.stat.descriptive.StatisticalSummary) */ public static double t(final double mu, final StatisticalSummary sampleStats) @@ -153,6 +187,9 @@ public class InferenceTestUtils { } /** + * @param sample1 array of sample data values + * @param sample2 array of sample data values + * @return t statistic * @see org.apache.commons.math4.stat.inference.TTest#t(double[], double[]) */ public static double t(final double[] sample1, final double[] sample2) @@ -161,6 +198,9 @@ public class InferenceTestUtils { } /** + * @param sampleStats1 StatisticalSummary describing data from the first sample + * @param sampleStats2 StatisticalSummary describing data from the second sample + * @return t statistic * @see org.apache.commons.math4.stat.inference.TTest#t(org.apache.commons.math4.stat.descriptive.StatisticalSummary, org.apache.commons.math4.stat.descriptive.StatisticalSummary) */ public static double t(final StatisticalSummary sampleStats1, @@ -170,6 +210,10 @@ public class InferenceTestUtils { } /** + * @param mu constant value to compare sample mean against + * @param sample array of sample data values + * @param alpha significance level of the test + * @return p-value * @see org.apache.commons.math4.stat.inference.TTest#tTest(double, double[], double) */ public static boolean tTest(final double mu, final double[] sample, final double alpha) @@ -179,6 +223,9 @@ public class InferenceTestUtils { } /** + * @param mu constant value to compare sample mean against + * @param sample array of sample data values + * @return p-value * @see org.apache.commons.math4.stat.inference.TTest#tTest(double, double[]) */ public static double tTest(final double mu, final double[] sample) @@ -188,6 +235,10 @@ public class InferenceTestUtils { } /** + * @param mu constant value to compare sample mean against + * @param sampleStats StatisticalSummary describing sample data values + * @param alpha significance level of the test + * @return p-value * @see org.apache.commons.math4.stat.inference.TTest#tTest(double, org.apache.commons.math4.stat.descriptive.StatisticalSummary, double) */ public static boolean tTest(final double mu, final StatisticalSummary sampleStats, @@ -198,6 +249,9 @@ public class InferenceTestUtils { } /** + * @param mu constant value to compare sample mean against + * @param sampleStats StatisticalSummary describing sample data + * @return p-value * @see org.apache.commons.math4.stat.inference.TTest#tTest(double, org.apache.commons.math4.stat.descriptive.StatisticalSummary) */ public static double tTest(final double mu, final StatisticalSummary sampleStats) @@ -207,6 +261,11 @@ public class InferenceTestUtils { } /** + * @param sample1 array of sample data values + * @param sample2 array of sample data values + * @param alpha significance level of the test + * @return true if the null hypothesis can be rejected with + * confidence 1 - alpha * @see org.apache.commons.math4.stat.inference.TTest#tTest(double[], double[], double) */ public static boolean tTest(final double[] sample1, final double[] sample2, @@ -217,6 +276,9 @@ public class InferenceTestUtils { } /** + * @param sample1 array of sample data values + * @param sample2 array of sample data values + * @return p-value for t-test * @see org.apache.commons.math4.stat.inference.TTest#tTest(double[], double[]) */ public static double tTest(final double[] sample1, final double[] sample2) @@ -226,6 +288,11 @@ public class InferenceTestUtils { } /** + * @param sampleStats1 StatisticalSummary describing sample data values + * @param sampleStats2 StatisticalSummary describing sample data values + * @param alpha significance level of the test + * @return true if the null hypothesis can be rejected with + * confidence 1 - alpha * @see org.apache.commons.math4.stat.inference.TTest#tTest(org.apache.commons.math4.stat.descriptive.StatisticalSummary, org.apache.commons.math4.stat.descriptive.StatisticalSummary, double) */ public static boolean tTest(final StatisticalSummary sampleStats1, @@ -237,6 +304,9 @@ public class InferenceTestUtils { } /** + * @param sampleStats1 StatisticalSummary describing data from the first sample + * @param sampleStats2 StatisticalSummary describing data from the second sample + * @return p-value for t-test * @see org.apache.commons.math4.stat.inference.TTest#tTest(org.apache.commons.math4.stat.descriptive.StatisticalSummary, org.apache.commons.math4.stat.descriptive.StatisticalSummary) */ public static double tTest(final StatisticalSummary sampleStats1, @@ -247,7 +317,10 @@ public class InferenceTestUtils { } /** - * @see org.apache.commons.math4.stat.inference.ChiSquareTest#chiSquare(double[], long[]) + * @param observed array of observed frequency counts + * @param expected array of expected frequency counts + * @return chiSquare test statistic +* @see org.apache.commons.math4.stat.inference.ChiSquareTest#chiSquare(double[], long[]) */ public static double chiSquare(final double[] expected, final long[] observed) throws NotPositiveException, NotStrictlyPositiveException, @@ -256,6 +329,8 @@ public class InferenceTestUtils { } /** + * @param counts array representation of 2-way table + * @return chiSquare test statistic * @see org.apache.commons.math4.stat.inference.ChiSquareTest#chiSquare(long[][]) */ public static double chiSquare(final long[][] counts) @@ -265,6 +340,11 @@ public class InferenceTestUtils { } /** + * @param observed array of observed frequency counts + * @param expected array of expected frequency counts + * @param alpha significance level of the test + * @return true iff null hypothesis can be rejected with confidence + * 1 - alpha * @see org.apache.commons.math4.stat.inference.ChiSquareTest#chiSquareTest(double[], long[], double) */ public static boolean chiSquareTest(final double[] expected, final long[] observed, @@ -275,6 +355,9 @@ public class InferenceTestUtils { } /** + * @param observed array of observed frequency counts + * @param expected array of expected frequency counts + * @return p-value * @see org.apache.commons.math4.stat.inference.ChiSquareTest#chiSquareTest(double[], long[]) */ public static double chiSquareTest(final double[] expected, final long[] observed) @@ -284,6 +367,10 @@ public class InferenceTestUtils { } /** + * @param counts array representation of 2-way table + * @param alpha significance level of the test + * @return true iff null hypothesis can be rejected with confidence + * 1 - alpha * @see org.apache.commons.math4.stat.inference.ChiSquareTest#chiSquareTest(long[][], double) */ public static boolean chiSquareTest(final long[][] counts, final double alpha) @@ -293,6 +380,8 @@ public class InferenceTestUtils { } /** + * @param counts array representation of 2-way table + * @return p-value * @see org.apache.commons.math4.stat.inference.ChiSquareTest#chiSquareTest(long[][]) */ public static double chiSquareTest(final long[][] counts) @@ -302,6 +391,9 @@ public class InferenceTestUtils { } /** + * @param observed1 array of observed frequency counts of the first data set + * @param observed2 array of observed frequency counts of the second data set + * @return chiSquare test statistic * @see org.apache.commons.math4.stat.inference.ChiSquareTest#chiSquareDataSetsComparison(long[], long[]) * * @since 1.2 @@ -313,6 +405,9 @@ public class InferenceTestUtils { } /** + * @param observed1 array of observed frequency counts of the first data set + * @param observed2 array of observed frequency counts of the second data set + * @return p-value * @see org.apache.commons.math4.stat.inference.ChiSquareTest#chiSquareTestDataSetsComparison(long[], long[]) * * @since 1.2 @@ -325,6 +420,11 @@ public class InferenceTestUtils { } /** + * @param observed1 array of observed frequency counts of the first data set + * @param observed2 array of observed frequency counts of the second data set + * @param alpha significance level of the test + * @return true iff null hypothesis can be rejected with confidence + * 1 - alpha * @see org.apache.commons.math4.stat.inference.ChiSquareTest#chiSquareTestDataSetsComparison(long[], long[], double) * * @since 1.2 @@ -338,6 +438,9 @@ public class InferenceTestUtils { } /** + * @param categoryData <code>Collection</code> of <code>double[]</code> + * arrays each containing data for one category + * @return Fvalue * @see org.apache.commons.math4.stat.inference.OneWayAnova#anovaFValue(Collection) * * @since 1.2 @@ -348,6 +451,9 @@ public class InferenceTestUtils { } /** + * @param categoryData <code>Collection</code> of <code>double[]</code> + * arrays each containing data for one category + * @return Pvalue * @see org.apache.commons.math4.stat.inference.OneWayAnova#anovaPValue(Collection) * * @since 1.2 @@ -359,6 +465,11 @@ public class InferenceTestUtils { } /** + * @param categoryData <code>Collection</code> of <code>double[]</code> + * arrays each containing data for one category + * @param alpha significance level of the test + * @return true if the null hypothesis can be rejected with + * confidence 1 - alpha * @see org.apache.commons.math4.stat.inference.OneWayAnova#anovaTest(Collection,double) * * @since 1.2 @@ -371,6 +482,9 @@ public class InferenceTestUtils { } /** + * @param observed array of observed frequency counts + * @param expected array of expected frequency counts + * @return G-Test statistic * @see org.apache.commons.math4.stat.inference.GTest#g(double[], long[]) * @since 3.1 */ @@ -381,6 +495,9 @@ public class InferenceTestUtils { } /** + * @param observed array of observed frequency counts + * @param expected array of expected frequency counts + * @return p-value * @see org.apache.commons.math4.stat.inference.GTest#gTest( double[], long[] ) * @since 3.1 */ @@ -391,6 +508,9 @@ public class InferenceTestUtils { } /** + * @param observed array of observed frequency counts + * @param expected array of expected frequency counts + * @return p-value * @see org.apache.commons.math4.stat.inference.GTest#gTestIntrinsic(double[], long[] ) * @since 3.1 */ @@ -401,6 +521,11 @@ public class InferenceTestUtils { } /** + * @param observed array of observed frequency counts + * @param expected array of expected frequency counts + * @param alpha significance level of the test + * @return true iff null hypothesis can be rejected with confidence 1 - + * alpha * @see org.apache.commons.math4.stat.inference.GTest#gTest( double[],long[],double) * @since 3.1 */ @@ -412,6 +537,10 @@ public class InferenceTestUtils { } /** + * @param observed1 array of observed frequency counts of the first data set + * @param observed2 array of observed frequency counts of the second data + * set + * @return G-Test statistic * @see org.apache.commons.math4.stat.inference.GTest#gDataSetsComparison(long[], long[]) * @since 3.1 */ @@ -422,6 +551,14 @@ public class InferenceTestUtils { } /** + * @param k11 number of times the two events occurred together (AB) + * @param k12 number of times the second event occurred WITHOUT the + * first event (notA,B) + * @param k21 number of times the first event occurred WITHOUT the + * second event (A, notB) + * @param k22 number of times something else occurred (i.e. was neither + * of these events (notA, notB) + * @return root log-likelihood ratio * @see org.apache.commons.math4.stat.inference.GTest#rootLogLikelihoodRatio(long, long, long, long) * @since 3.1 */ @@ -432,6 +569,10 @@ public class InferenceTestUtils { /** + * @param observed1 array of observed frequency counts of the first data set + * @param observed2 array of observed frequency counts of the second data + * set + * @return p-value * @see org.apache.commons.math4.stat.inference.GTest#gTestDataSetsComparison(long[], long[]) * @since 3.1 */ @@ -443,6 +584,12 @@ public class InferenceTestUtils { } /** + * @param observed1 array of observed frequency counts of the first data set + * @param observed2 array of observed frequency counts of the second data + * set + * @param alpha significance level of the test + * @return true iff null hypothesis can be rejected with confidence 1 - + * alpha * @see org.apache.commons.math4.stat.inference.GTest#gTestDataSetsComparison(long[],long[],double) * @since 3.1 */ @@ -455,6 +602,9 @@ public class InferenceTestUtils { } /** + * @param dist reference distribution + * @param data sample being evaluated + * @return Kolmogorov-Smirnov statistic \(D_n\) * @see org.apache.commons.math4.stat.inference.KolmogorovSmirnovTest#kolmogorovSmirnovStatistic(RealDistribution, double[]) * @since 3.3 */ @@ -464,6 +614,10 @@ public class InferenceTestUtils { } /** + * @param dist reference distribution + * @param data sample being being evaluated + * @return the p-value associated with the null hypothesis that {@code data} is a sample from + * {@code distribution} * @see org.apache.commons.math4.stat.inference.KolmogorovSmirnovTest#kolmogorovSmirnovTest(RealDistribution, double[]) * @since 3.3 */ @@ -473,6 +627,11 @@ public class InferenceTestUtils { } /** + * @param dist reference distribution + * @param data sample being being evaluated + * @param strict whether or not to force exact computation of the p-value + * @return the p-value associated with the null hypothesis that {@code data} is a sample from + * {@code distribution} * @see org.apache.commons.math4.stat.inference.KolmogorovSmirnovTest#kolmogorovSmirnovTest(RealDistribution, double[], boolean) * @since 3.3 */ @@ -482,6 +641,11 @@ public class InferenceTestUtils { } /** + * @param dist reference distribution + * @param data sample being being evaluated + * @param alpha significance level of the test + * @return true iff the null hypothesis that {@code data} is a sample from {@code distribution} + * can be rejected with confidence 1 - {@code alpha} * @see org.apache.commons.math4.stat.inference.KolmogorovSmirnovTest#kolmogorovSmirnovTest(RealDistribution, double[], double) * @since 3.3 */ @@ -491,6 +655,10 @@ public class InferenceTestUtils { } /** + * @param x first sample + * @param y second sample + * @return test statistic \(D_{n,m}\) used to evaluate the null hypothesis that {@code x} and + * {@code y} represent samples from the same underlying distribution * @see org.apache.commons.math4.stat.inference.KolmogorovSmirnovTest#kolmogorovSmirnovStatistic(double[], double[]) * @since 3.3 */ @@ -500,6 +668,10 @@ public class InferenceTestUtils { } /** + * @param x first sample dataset + * @param y second sample dataset + * @return p-value associated with the null hypothesis that {@code x} and {@code y} represent + * samples from the same distribution * @see org.apache.commons.math4.stat.inference.KolmogorovSmirnovTest#kolmogorovSmirnovTest(double[], double[]) * @since 3.3 */ @@ -509,6 +681,12 @@ public class InferenceTestUtils { } /** + * @param x first sample dataset. + * @param y second sample dataset. + * @param strict whether or not the probability to compute is expressed as + * a strict inequality (ignored for large samples). + * @return p-value associated with the null hypothesis that {@code x} and + * {@code y} represent samples from the same distribution. * @see org.apache.commons.math4.stat.inference.KolmogorovSmirnovTest#kolmogorovSmirnovTest(double[], double[], boolean) * @since 3.3 */ @@ -518,6 +696,12 @@ public class InferenceTestUtils { } /** + * @param d D-statistic value + * @param n first sample size + * @param m second sample size + * @param strict whether or not the probability to compute is expressed as a strict inequality + * @return probability that a randomly selected m-n partition of m + n generates \(D_{n,m}\) + * greater than (resp. greater than or equal to) {@code d} * @see org.apache.commons.math4.stat.inference.KolmogorovSmirnovTest#exactP(double, int, int, boolean) * @since 3.3 */ @@ -526,6 +710,11 @@ public class InferenceTestUtils { } /** + * @param d D-statistic value + * @param n first sample size + * @param m second sample size + * @return approximate probability that a randomly selected m-n partition of m + n generates + * \(D_{n,m}\) greater than {@code d} * @see org.apache.commons.math4.stat.inference.KolmogorovSmirnovTest#approximateP(double, int, int) * @since 3.3 */ @@ -534,6 +723,13 @@ public class InferenceTestUtils { } /** + * @param d D-statistic value + * @param n first sample size + * @param m second sample size + * @param iterations number of random partitions to generate + * @param strict whether or not the probability to compute is expressed as a strict inequality + * @return proportion of randomly generated m-n partitions of m + n that result in \(D_{n,m}\) + * greater than (resp. greater than or equal to) {@code d} * @see org.apache.commons.math4.stat.inference.KolmogorovSmirnovTest#monteCarloP(double, int, int, boolean, int) * @since 3.3 */
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/inference/KolmogorovSmirnovTest.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/stat/inference/KolmogorovSmirnovTest.java b/src/main/java/org/apache/commons/math4/stat/inference/KolmogorovSmirnovTest.java index c79e644..53a6162 100644 --- a/src/main/java/org/apache/commons/math4/stat/inference/KolmogorovSmirnovTest.java +++ b/src/main/java/org/apache/commons/math4/stat/inference/KolmogorovSmirnovTest.java @@ -73,7 +73,7 @@ import org.apache.commons.math4.util.MathUtils; * <li>When the product of the sample sizes exceeds {@value #LARGE_SAMPLE_PRODUCT}, the asymptotic * distribution of \(D_{n,m}\) is used. See {@link #approximateP(double, int, int)} for details on * the approximation.</li> - * </ul></p><p> + * </ul><p> * If the product of the sample sizes is less than {@value #LARGE_SAMPLE_PRODUCT} and the sample * data contains ties, random jitter is added to the sample data to break ties before applying * the algorithm above. Alternatively, the {@link #bootstrap(double[], double[], int, boolean)} @@ -82,7 +82,7 @@ import org.apache.commons.math4.util.MathUtils; * </p> * <p> * In the two-sample case, \(D_{n,m}\) has a discrete distribution. This makes the p-value - * associated with the null hypothesis \(H_0 : D_{n,m} \ge d \) differ from \(H_0 : D_{n,m} > d \) + * associated with the null hypothesis \(H_0 : D_{n,m} \ge d \) differ from \(H_0 : D_{n,m} \ge d \) * by the mass of the observed value \(d\). To distinguish these, the two-sample tests use a boolean * {@code strict} parameter. This parameter is ignored for large samples. * </p> @@ -95,7 +95,6 @@ import org.apache.commons.math4.util.MathUtils; * expressed using strict or non-strict inequality. See * {@link #kolmogorovSmirnovTest(double[], double[], boolean)}.</li> * </ul> - * </p> * <p> * References: * <ul> @@ -109,10 +108,9 @@ import org.apache.commons.math4.util.MathUtils; * <li>[4] Wilcox, Rand. 2012. Introduction to Robust Estimation and Hypothesis Testing, * Chapter 5, 3rd Ed. Academic Press.</li> * </ul> - * <br/> + * <br> * Note that [1] contains an error in computing h, refer to <a * href="https://issues.apache.org/jira/browse/MATH-437";>MATH-437</a> for details. - * </p> * * @since 3.3 */ @@ -234,7 +232,7 @@ public class KolmogorovSmirnovTest { * asymptotic distribution of \(D_{n,m}\) is used. See {@link #approximateP(double, int, int)} * for details on the approximation.</li> * </ul><p> - * If {@code x.length * y.length} < {@value #LARGE_SAMPLE_PRODUCT} and the combined set of values in + * If {@code x.length * y.length <} {@value #LARGE_SAMPLE_PRODUCT} and the combined set of values in * {@code x} and {@code y} contains ties, random jitter is added to {@code x} and {@code y} to * break ties before computing \(D_{n,m}\) and the p-value. The jitter is uniformly distributed * on (-minDelta / 2, minDelta / 2) where minDelta is the smallest pairwise difference between @@ -457,17 +455,17 @@ public class KolmogorovSmirnovTest { } /** - * Calculates \(P(D_n < d)\) using the method described in [1] with quick decisions for extreme + * Calculates \(P(D_n < d)\) using the method described in [1] with quick decisions for extreme * values given in [2] (see above). The result is not exact as with * {@link #cdfExact(double, int)} because calculations are based on * {@code double} rather than {@link org.apache.commons.math4.fraction.BigFraction}. * * @param d statistic * @param n sample size - * @return \(P(D_n < d)\) + * @return \(P(D_n < d)\) * @throws MathArithmeticException if algorithm fails to convert {@code h} to a * {@link org.apache.commons.math4.fraction.BigFraction} in expressing {@code d} as \((k - * - h) / m\) for integer {@code k, m} and \(0 \le h < 1\) + * - h) / m\) for integer {@code k, m} and \(0 \le h < 1\) */ public double cdf(double d, int n) throws MathArithmeticException { @@ -483,10 +481,10 @@ public class KolmogorovSmirnovTest { * * @param d statistic * @param n sample size - * @return \(P(D_n < d)\) + * @return \(P(D_n < d)\) * @throws MathArithmeticException if the algorithm fails to convert {@code h} to a * {@link org.apache.commons.math4.fraction.BigFraction} in expressing {@code d} as \((k - * - h) / m\) for integer {@code k, m} and \(0 \le h < 1\) + * - h) / m\) for integer {@code k, m} and \(0 \le h < 1\) */ public double cdfExact(double d, int n) throws MathArithmeticException { @@ -504,10 +502,10 @@ public class KolmogorovSmirnovTest { * very slow execution time, or if {@code double} should be used convenient places to * gain speed. Almost never choose {@code true} in real applications unless you are very * sure; {@code true} is almost solely for verification purposes. - * @return \(P(D_n < d)\) + * @return \(P(D_n < d)\) * @throws MathArithmeticException if algorithm fails to convert {@code h} to a * {@link org.apache.commons.math4.fraction.BigFraction} in expressing {@code d} as \((k - * - h) / m\) for integer {@code k, m} and \(0 \le h < 1\). + * - h) / m\) for integer {@code k, m} and \(0 \le h < 1\). */ public double cdf(double d, int n, boolean exact) throws MathArithmeticException { @@ -546,10 +544,10 @@ public class KolmogorovSmirnovTest { * * @param d statistic * @param n sample size - * @return the two-sided probability of \(P(D_n < d)\) + * @return the two-sided probability of \(P(D_n < d)\) * @throws MathArithmeticException if algorithm fails to convert {@code h} to a * {@link org.apache.commons.math4.fraction.BigFraction} in expressing {@code d} as \((k - * - h) / m\) for integer {@code k, m} and \(0 \le h < 1\). + * - h) / m\) for integer {@code k, m} and \(0 \le h < 1\). */ private double exactK(double d, int n) throws MathArithmeticException { @@ -578,7 +576,7 @@ public class KolmogorovSmirnovTest { * * @param d statistic * @param n sample size - * @return \(P(D_n < d)\) + * @return \(P(D_n < d)\) */ private double roundedK(double d, int n) { @@ -595,11 +593,11 @@ public class KolmogorovSmirnovTest { } /** - * Computes the Pelz-Good approximation for \(P(D_n < d)\) as described in [2] in the class javadoc. + * Computes the Pelz-Good approximation for \(P(D_n < d)\) as described in [2] in the class javadoc. * * @param d value of d-statistic (x in [2]) * @param n sample size - * @return \(P(D_n < d)\) + * @return \(P(D_n < d)\) * @since 3.4 */ public double pelzGood(double d, int n) { @@ -986,7 +984,7 @@ public class KolmogorovSmirnovTest { } /** - * Computes \(P(D_{n,m} > d)\) if {@code strict} is {@code true}; otherwise \(P(D_{n,m} \ge + * Computes \(P(D_{n,m} > d)\) if {@code strict} is {@code true}; otherwise \(P(D_{n,m} \ge * d)\), where \(D_{n,m}\) is the 2-sample Kolmogorov-Smirnov statistic. See * {@link #kolmogorovSmirnovStatistic(double[], double[])} for the definition of \(D_{n,m}\). * <p> @@ -1007,7 +1005,7 @@ public class KolmogorovSmirnovTest { } /** - * Uses the Kolmogorov-Smirnov distribution to approximate \(P(D_{n,m} > d)\) where \(D_{n,m}\) + * Uses the Kolmogorov-Smirnov distribution to approximate \(P(D_{n,m} > d)\) where \(D_{n,m}\) * is the 2-sample Kolmogorov-Smirnov statistic. See * {@link #kolmogorovSmirnovStatistic(double[], double[])} for the definition of \(D_{n,m}\). * <p> @@ -1052,7 +1050,7 @@ public class KolmogorovSmirnovTest { } /** - * Uses Monte Carlo simulation to approximate \(P(D_{n,m} > d)\) where \(D_{n,m}\) is the + * Uses Monte Carlo simulation to approximate \(P(D_{n,m} > d)\) where \(D_{n,m}\) is the * 2-sample Kolmogorov-Smirnov statistic. See * {@link #kolmogorovSmirnovStatistic(double[], double[])} for the definition of \(D_{n,m}\). * <p> http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/inference/MannWhitneyUTest.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/stat/inference/MannWhitneyUTest.java b/src/main/java/org/apache/commons/math4/stat/inference/MannWhitneyUTest.java index 4452816..f938c17 100644 --- a/src/main/java/org/apache/commons/math4/stat/inference/MannWhitneyUTest.java +++ b/src/main/java/org/apache/commons/math4/stat/inference/MannWhitneyUTest.java @@ -114,7 +114,6 @@ public class MannWhitneyUTest { * <li>All observations in the two samples are independent.</li> * <li>The observations are at least ordinal (continuous are also ordinal).</li> * </ul> - * </p> * * @param x the first sample * @param y the second sample @@ -203,8 +202,7 @@ public class MannWhitneyUTest { * <ul> * <li>All observations in the two samples are independent.</li> * <li>The observations are at least ordinal (continuous are also ordinal).</li> - * </ul> - * </p><p> + * </ul><p> * Ties give rise to biased variance at the moment. See e.g. <a * href="http://mlsc.lboro.ac.uk/resources/statistics/Mannwhitney.pdf"; * >http://mlsc.lboro.ac.uk/resources/statistics/Mannwhitney.pdf</a>.</p> http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/inference/OneWayAnova.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/stat/inference/OneWayAnova.java b/src/main/java/org/apache/commons/math4/stat/inference/OneWayAnova.java index 3c322c9..e54daf1 100644 --- a/src/main/java/org/apache/commons/math4/stat/inference/OneWayAnova.java +++ b/src/main/java/org/apache/commons/math4/stat/inference/OneWayAnova.java @@ -66,7 +66,7 @@ public class OneWayAnova { * <code>double[]</code> arrays.</li> * <li> There must be at least two <code>double[]</code> arrays in the * <code>categoryData</code> collection and each of these arrays must - * contain at least two values.</li></ul></p><p> + * contain at least two values.</li></ul><p> * This implementation computes the F statistic using the definitional * formula<pre> * F = msbg/mswg</pre> @@ -74,7 +74,7 @@ public class OneWayAnova { * msbg = between group mean square * mswg = within group mean square</pre> * are as defined <a href="http://faculty.vassar.edu/lowry/ch13pt1.html";> - * here</a></p> + * here</a> * * @param categoryData <code>Collection</code> of <code>double[]</code> * arrays each containing data for one category @@ -101,14 +101,14 @@ public class OneWayAnova { * <code>double[]</code> arrays.</li> * <li> There must be at least two <code>double[]</code> arrays in the * <code>categoryData</code> collection and each of these arrays must - * contain at least two values.</li></ul></p><p> + * contain at least two values.</li></ul><p> * This implementation uses the * {@link org.apache.commons.math4.distribution.FDistribution * commons-math F Distribution implementation} to estimate the exact * p-value, using the formula<pre> * p = 1 - cumulativeProbability(F)</pre> * where <code>F</code> is the F value and <code>cumulativeProbability</code> - * is the commons-math implementation of the F distribution.</p> + * is the commons-math implementation of the F distribution. * * @param categoryData <code>Collection</code> of <code>double[]</code> * arrays each containing data for one category @@ -140,14 +140,14 @@ public class OneWayAnova { * {@link SummaryStatistics}.</li> * <li> There must be at least two {@link SummaryStatistics} in the * <code>categoryData</code> collection and each of these statistics must - * contain at least two values.</li></ul></p><p> + * contain at least two values.</li></ul><p> * This implementation uses the * {@link org.apache.commons.math4.distribution.FDistribution * commons-math F Distribution implementation} to estimate the exact * p-value, using the formula<pre> * p = 1 - cumulativeProbability(F)</pre> * where <code>F</code> is the F value and <code>cumulativeProbability</code> - * is the commons-math implementation of the F distribution.</p> + * is the commons-math implementation of the F distribution. * * @param categoryData <code>Collection</code> of {@link SummaryStatistics} * each containing data for one category @@ -221,14 +221,14 @@ public class OneWayAnova { * <code>categoryData</code> collection and each of these arrays must * contain at least two values.</li> * <li>alpha must be strictly greater than 0 and less than or equal to 0.5. - * </li></ul></p><p> + * </li></ul><p> * This implementation uses the * {@link org.apache.commons.math4.distribution.FDistribution * commons-math F Distribution implementation} to estimate the exact * p-value, using the formula<pre> * p = 1 - cumulativeProbability(F)</pre> * where <code>F</code> is the F value and <code>cumulativeProbability</code> - * is the commons-math implementation of the F distribution.</p> + * is the commons-math implementation of the F distribution. * <p>True is returned iff the estimated p-value is less than alpha.</p> * * @param categoryData <code>Collection</code> of <code>double[]</code> http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/inference/TTest.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/stat/inference/TTest.java b/src/main/java/org/apache/commons/math4/stat/inference/TTest.java index 577ac29..45bb9f3 100644 --- a/src/main/java/org/apache/commons/math4/stat/inference/TTest.java +++ b/src/main/java/org/apache/commons/math4/stat/inference/TTest.java @@ -40,7 +40,7 @@ import org.apache.commons.math4.util.FastMath; * <li>Homoscedastic (equal variance assumption) or heteroscedastic * (for two sample tests)</li> * <li>Fixed significance level (boolean-valued) or returning p-values. - * </li></ul></p> + * </li></ul> * <p> * Test statistics are available for all tests. Methods including "Test" in * in their names perform tests, all other methods return t-statistics. Among @@ -67,7 +67,7 @@ public class TTest { * <strong>Preconditions</strong>: <ul> * <li>The input arrays must have the same length and their common length * must be at least 2. - * </li></ul></p> + * </li></ul> * * @param sample1 array of sample data values * @param sample2 array of sample data values @@ -115,7 +115,7 @@ public class TTest { * <strong>Preconditions</strong>: <ul> * <li>The input array lengths must be the same and their common length must * be at least 2. - * </li></ul></p> + * </li></ul> * * @param sample1 array of sample data values * @param sample2 array of sample data values @@ -159,7 +159,7 @@ public class TTest { * must be at least 2. * </li> * <li> <code> 0 < alpha < 0.5 </code> - * </li></ul></p> + * </li></ul> * * @param sample1 array of sample data values * @param sample2 array of sample data values @@ -191,7 +191,7 @@ public class TTest { * </p><p> * <strong>Preconditions</strong>: <ul> * <li>The observed array length must be at least 2. - * </li></ul></p> + * </li></ul> * * @param mu comparison constant * @param observed array of values @@ -218,7 +218,7 @@ public class TTest { * </p><p> * <strong>Preconditions</strong>: <ul> * <li><code>observed.getN() ≥ 2</code>. - * </li></ul></p> + * </li></ul> * * @param mu comparison constant * @param sampleStats DescriptiveStatistics holding sample summary statitstics @@ -250,8 +250,7 @@ public class TTest { * where <strong><code>n1</code></strong> is the size of first sample; * <strong><code> n2</code></strong> is the size of second sample; * <strong><code> m1</code></strong> is the mean of first sample; - * <strong><code> m2</code></strong> is the mean of second sample</li> - * </ul> + * <strong><code> m2</code></strong> is the mean of second sample * and <strong><code>var</code></strong> is the pooled variance estimate: * </p><p> * <code>var = sqrt(((n1 - 1)var1 + (n2 - 1)var2) / ((n1-1) + (n2-1)))</code> @@ -261,7 +260,7 @@ public class TTest { * </p><p> * <strong>Preconditions</strong>: <ul> * <li>The observed array lengths must both be at least 2. - * </li></ul></p> + * </li></ul> * * @param sample1 array of sample data values * @param sample2 array of sample data values @@ -302,7 +301,7 @@ public class TTest { * </p><p> * <strong>Preconditions</strong>: <ul> * <li>The observed array lengths must both be at least 2. - * </li></ul></p> + * </li></ul> * * @param sample1 array of sample data values * @param sample2 array of sample data values @@ -323,7 +322,7 @@ public class TTest { } /** - * Computes a 2-sample t statistic </a>, comparing the means of the datasets + * Computes a 2-sample t statistic, comparing the means of the datasets * described by two {@link StatisticalSummary} instances, without the * assumption of equal subpopulation variances. Use * {@link #homoscedasticT(StatisticalSummary, StatisticalSummary)} to @@ -346,7 +345,7 @@ public class TTest { * <strong>Preconditions</strong>: <ul> * <li>The datasets described by the two Univariates must each contain * at least 2 observations. - * </li></ul></p> + * </li></ul> * * @param sampleStats1 StatisticalSummary describing data from the first sample * @param sampleStats2 StatisticalSummary describing data from the second sample @@ -394,7 +393,7 @@ public class TTest { * <strong>Preconditions</strong>: <ul> * <li>The datasets described by the two Univariates must each contain * at least 2 observations. - * </li></ul></p> + * </li></ul> * * @param sampleStats1 StatisticalSummary describing data from the first sample * @param sampleStats2 StatisticalSummary describing data from the second sample @@ -432,7 +431,7 @@ public class TTest { * </p><p> * <strong>Preconditions</strong>: <ul> * <li>The observed array length must be at least 2. - * </li></ul></p> + * </li></ul> * * @param mu constant value to compare sample mean against * @param sample array of sample data values @@ -465,11 +464,11 @@ public class TTest { * <li>To test the (2-sided) hypothesis <code>sample mean = mu </code> at * the 95% level, use <br><code>tTest(mu, sample, 0.05) </code> * </li> - * <li>To test the (one-sided) hypothesis <code> sample mean < mu </code> + * <li>To test the (one-sided) hypothesis <code> sample mean < mu </code> * at the 99% level, first verify that the measured sample mean is less * than <code>mu</code> and then use * <br><code>tTest(mu, sample, 0.02) </code> - * </li></ol></p> + * </li></ol> * <p> * <strong>Usage Note:</strong><br> * The validity of the test depends on the assumptions of the one-sample @@ -478,7 +477,7 @@ public class TTest { * </p><p> * <strong>Preconditions</strong>: <ul> * <li>The observed array length must be at least 2. - * </li></ul></p> + * </li></ul> * * @param mu constant value to compare sample mean against * @param sample array of sample data values @@ -518,7 +517,7 @@ public class TTest { * <p> * <strong>Preconditions</strong>: <ul> * <li>The sample must contain at least 2 observations. - * </li></ul></p> + * </li></ul> * * @param mu constant value to compare sample mean against * @param sampleStats StatisticalSummary describing sample data @@ -551,11 +550,11 @@ public class TTest { * <li>To test the (2-sided) hypothesis <code>sample mean = mu </code> at * the 95% level, use <br><code>tTest(mu, sampleStats, 0.05) </code> * </li> - * <li>To test the (one-sided) hypothesis <code> sample mean < mu </code> + * <li>To test the (one-sided) hypothesis <code> sample mean < mu </code> * at the 99% level, first verify that the measured sample mean is less * than <code>mu</code> and then use * <br><code>tTest(mu, sampleStats, 0.02) </code> - * </li></ol></p> + * </li></ol> * <p> * <strong>Usage Note:</strong><br> * The validity of the test depends on the assumptions of the one-sample @@ -564,7 +563,7 @@ public class TTest { * </p><p> * <strong>Preconditions</strong>: <ul> * <li>The sample must include at least 2 observations. - * </li></ul></p> + * </li></ul> * * @param mu constant value to compare sample mean against * @param sampleStats StatisticalSummary describing sample data values @@ -613,7 +612,7 @@ public class TTest { * <p> * <strong>Preconditions</strong>: <ul> * <li>The observed array lengths must both be at least 2. - * </li></ul></p> + * </li></ul> * * @param sample1 array of sample data values * @param sample2 array of sample data values @@ -641,7 +640,7 @@ public class TTest { * comparing the means of the input arrays, under the assumption that * the two samples are drawn from subpopulations with equal variances. * To perform the test without the equal variances assumption, use - * {@link #tTest(double[], double[])}.</p> + * {@link #tTest(double[], double[])}. * <p> * The number returned is the smallest significance level * at which one can reject the null hypothesis that the two means are @@ -660,7 +659,7 @@ public class TTest { * <p> * <strong>Preconditions</strong>: <ul> * <li>The observed array lengths must both be at least 2. - * </li></ul></p> + * </li></ul> * * @param sample1 array of sample data values * @param sample2 array of sample data values @@ -708,11 +707,11 @@ public class TTest { * the 95% level, use * <br><code>tTest(sample1, sample2, 0.05). </code> * </li> - * <li>To test the (one-sided) hypothesis <code> mean 1 < mean 2 </code>, + * <li>To test the (one-sided) hypothesis <code> mean 1 < mean 2 </code>, * at the 99% level, first verify that the measured mean of <code>sample 1</code> * is less than the mean of <code>sample 2</code> and then use * <br><code>tTest(sample1, sample2, 0.02) </code> - * </li></ol></p> + * </li></ol> * <p> * <strong>Usage Note:</strong><br> * The validity of the test depends on the assumptions of the parametric @@ -723,8 +722,8 @@ public class TTest { * <strong>Preconditions</strong>: <ul> * <li>The observed array lengths must both be at least 2. * </li> - * <li> <code> 0 < alpha < 0.5 </code> - * </li></ul></p> + * <li> <code> 0 < alpha < 0.5 </code> + * </li></ul> * * @param sample1 array of sample data values * @param sample2 array of sample data values @@ -770,12 +769,12 @@ public class TTest { * <li>To test the (2-sided) hypothesis <code>mean 1 = mean 2 </code> at * the 95% level, use <br><code>tTest(sample1, sample2, 0.05). </code> * </li> - * <li>To test the (one-sided) hypothesis <code> mean 1 < mean 2, </code> + * <li>To test the (one-sided) hypothesis <code> mean 1 < mean 2, </code> * at the 99% level, first verify that the measured mean of * <code>sample 1</code> is less than the mean of <code>sample 2</code> * and then use * <br><code>tTest(sample1, sample2, 0.02) </code> - * </li></ol></p> + * </li></ol> * <p> * <strong>Usage Note:</strong><br> * The validity of the test depends on the assumptions of the parametric @@ -786,8 +785,8 @@ public class TTest { * <strong>Preconditions</strong>: <ul> * <li>The observed array lengths must both be at least 2. * </li> - * <li> <code> 0 < alpha < 0.5 </code> - * </li></ul></p> + * <li> <code> 0 < alpha < 0.5 </code> + * </li></ul> * * @param sample1 array of sample data values * @param sample2 array of sample data values @@ -835,7 +834,7 @@ public class TTest { * <strong>Preconditions</strong>: <ul> * <li>The datasets described by the two Univariates must each contain * at least 2 observations. - * </li></ul></p> + * </li></ul> * * @param sampleStats1 StatisticalSummary describing data from the first sample * @param sampleStats2 StatisticalSummary describing data from the second sample @@ -882,7 +881,7 @@ public class TTest { * <strong>Preconditions</strong>: <ul> * <li>The datasets described by the two Univariates must each contain * at least 2 observations. - * </li></ul></p> + * </li></ul> * * @param sampleStats1 StatisticalSummary describing data from the first sample * @param sampleStats2 StatisticalSummary describing data from the second sample @@ -931,12 +930,12 @@ public class TTest { * the 95%, use * <br><code>tTest(sampleStats1, sampleStats2, 0.05) </code> * </li> - * <li>To test the (one-sided) hypothesis <code> mean 1 < mean 2 </code> + * <li>To test the (one-sided) hypothesis <code> mean 1 < mean 2 </code> * at the 99% level, first verify that the measured mean of * <code>sample 1</code> is less than the mean of <code>sample 2</code> * and then use * <br><code>tTest(sampleStats1, sampleStats2, 0.02) </code> - * </li></ol></p> + * </li></ol> * <p> * <strong>Usage Note:</strong><br> * The validity of the test depends on the assumptions of the parametric @@ -948,8 +947,8 @@ public class TTest { * <li>The datasets described by the two Univariates must each contain * at least 2 observations. * </li> - * <li> <code> 0 < alpha < 0.5 </code> - * </li></ul></p> + * <li> <code> 0 < alpha < 0.5 </code> + * </li></ul> * * @param sampleStats1 StatisticalSummary describing sample data values * @param sampleStats2 StatisticalSummary describing sample data values http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/inference/WilcoxonSignedRankTest.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/stat/inference/WilcoxonSignedRankTest.java b/src/main/java/org/apache/commons/math4/stat/inference/WilcoxonSignedRankTest.java index 537d1c4..4ffff61 100644 --- a/src/main/java/org/apache/commons/math4/stat/inference/WilcoxonSignedRankTest.java +++ b/src/main/java/org/apache/commons/math4/stat/inference/WilcoxonSignedRankTest.java @@ -158,7 +158,6 @@ public class WilcoxonSignedRankTest { * ordered, so the comparisons greater than, less than, and equal to are * meaningful.</li> * </ul> - * </p> * * @param x the first sample * @param y the second sample @@ -281,13 +280,12 @@ public class WilcoxonSignedRankTest { * ordered, so the comparisons greater than, less than, and equal to are * meaningful.</li> * </ul> - * </p> * * @param x the first sample * @param y the second sample * @param exactPValue - * if the exact p-value is wanted (only works for x.length <= 30, - * if true and x.length > 30, this is ignored because + * if the exact p-value is wanted (only works for x.length >= 30, + * if true and x.length < 30, this is ignored because * calculations may take too long) * @return p-value * @throws NullArgumentException if {@code x} or {@code y} are {@code null}. @@ -295,7 +293,7 @@ public class WilcoxonSignedRankTest { * @throws DimensionMismatchException if {@code x} and {@code y} do not * have the same length. * @throws NumberIsTooLargeException if {@code exactPValue} is {@code true} - * and {@code x.length} > 30 + * and {@code x.length} > 30 * @throws ConvergenceException if the p-value can not be computed due to * a convergence error * @throws MaxCountExceededException if the maximum number of iterations http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/interval/BinomialConfidenceInterval.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/stat/interval/BinomialConfidenceInterval.java b/src/main/java/org/apache/commons/math4/stat/interval/BinomialConfidenceInterval.java index 8639929..9bf9e23 100644 --- a/src/main/java/org/apache/commons/math4/stat/interval/BinomialConfidenceInterval.java +++ b/src/main/java/org/apache/commons/math4/stat/interval/BinomialConfidenceInterval.java @@ -42,7 +42,6 @@ public interface BinomialConfidenceInterval { * <li>{@code numberOfSuccesses} may not exceed {@code numberOfTrials}</li> * <li>{@code confidenceLevel} must be strictly between 0 and 1 (exclusive)</li> * </ul> - * </p> * * @param numberOfTrials number of trials * @param numberOfSuccesses number of successes http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/interval/ConfidenceInterval.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/stat/interval/ConfidenceInterval.java b/src/main/java/org/apache/commons/math4/stat/interval/ConfidenceInterval.java index 4cadd1f..e41ebf1 100644 --- a/src/main/java/org/apache/commons/math4/stat/interval/ConfidenceInterval.java +++ b/src/main/java/org/apache/commons/math4/stat/interval/ConfidenceInterval.java @@ -46,7 +46,6 @@ public class ConfidenceInterval { * <li>{@code lower} must be strictly less than {@code upper}</li> * <li>{@code confidenceLevel} must be strictly between 0 and 1 (exclusive)</li> * </ul> - * </p> * * @param lowerBound lower endpoint of the interval * @param upperBound upper endpoint of the interval http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/interval/IntervalUtils.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/stat/interval/IntervalUtils.java b/src/main/java/org/apache/commons/math4/stat/interval/IntervalUtils.java index 86a7949..11ead0c 100644 --- a/src/main/java/org/apache/commons/math4/stat/interval/IntervalUtils.java +++ b/src/main/java/org/apache/commons/math4/stat/interval/IntervalUtils.java @@ -86,7 +86,6 @@ public final class IntervalUtils { * <li>{@code numberOfSuccesses} may not exceed {@code numberOfTrials}</li> * <li>{@code confidenceLevel} must be strictly between 0 and 1 (exclusive)</li> * </ul> - * </p> * * @param numberOfTrials number of trials * @param numberOfSuccesses number of successes http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/ranking/NaturalRanking.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/stat/ranking/NaturalRanking.java b/src/main/java/org/apache/commons/math4/stat/ranking/NaturalRanking.java index ad15725..4d55867 100644 --- a/src/main/java/org/apache/commons/math4/stat/ranking/NaturalRanking.java +++ b/src/main/java/org/apache/commons/math4/stat/ranking/NaturalRanking.java @@ -41,7 +41,7 @@ import org.apache.commons.math4.util.FastMath; * {@link UniformRandomProvider random generator} may be supplied as a * constructor argument.</p> * <p>Examples: - * <table border="1" cellpadding="3"> + * <table border="1" cellpadding="3" summary="Examples"> * <tr><th colspan="3"> * Input data: (20, 17, 30, 42.3, 17, 50, Double.NaN, Double.NEGATIVE_INFINITY, 17) * </th></tr> @@ -66,7 +66,7 @@ import org.apache.commons.math4.util.FastMath; * <tr> * <td>MINIMAL</td> * <td>MAXIMUM</td> - * <td>(6, 5, 7, 8, 5, 9, 2, 2, 5)</td></tr></table></p> + * <td>(6, 5, 7, 8, 5, 9, 2, 2, 5)</td></tr></table> * * @since 2.0 */ http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/regression/AbstractMultipleLinearRegression.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/stat/regression/AbstractMultipleLinearRegression.java b/src/main/java/org/apache/commons/math4/stat/regression/AbstractMultipleLinearRegression.java index 99775c4..d7036e3 100644 --- a/src/main/java/org/apache/commons/math4/stat/regression/AbstractMultipleLinearRegression.java +++ b/src/main/java/org/apache/commons/math4/stat/regression/AbstractMultipleLinearRegression.java @@ -98,9 +98,8 @@ public abstract class AbstractMultipleLinearRegression implements * </p> * <p>Throws IllegalArgumentException if any of the following preconditions fail: * <ul><li><code>data</code> cannot be null</li> - * <li><code>data.length = nobs * (nvars + 1)</li> + * <li><code>data.length = nobs * (nvars + 1)</code></li> * <li><code>nobs > nvars</code></li></ul> - * </p> * * @param data input data array * @param nobs number of observations (rows) @@ -171,7 +170,6 @@ public abstract class AbstractMultipleLinearRegression implements * 3 4 * 5 6 * </pre> - * </p> * <p>Note that there is no need to add an initial unitary column (column of 1's) when * specifying a model including an intercept term. * </p> http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/regression/MillerUpdatingRegression.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/stat/regression/MillerUpdatingRegression.java b/src/main/java/org/apache/commons/math4/stat/regression/MillerUpdatingRegression.java index 4f421d1..abc8fea 100644 --- a/src/main/java/org/apache/commons/math4/stat/regression/MillerUpdatingRegression.java +++ b/src/main/java/org/apache/commons/math4/stat/regression/MillerUpdatingRegression.java @@ -33,7 +33,7 @@ import org.apache.commons.numbers.core.Precision; * Series C (Applied Statistics), Vol. 41, No. 2 * (1992), pp. 458-478 * Published by: Blackwell Publishing for the Royal Statistical Society - * Stable URL: http://www.jstor.org/stable/2347583 </pre></p> + * Stable URL: http://www.jstor.org/stable/2347583 </pre> * * <p>This method for multiple regression forms the solution to the OLS problem * by updating the QR decomposition as described by Gentleman.</p> @@ -596,7 +596,7 @@ public class MillerUpdatingRegression implements UpdatingMultipleLinearRegressio * * <p>If IN = 0, the value returned in array CORMAT for the correlation * of variables Xi & Xj is: <pre> - * sum ( Xi.Xj ) / Sqrt ( sum (Xi^2) . sum (Xj^2) )</pre></p> + * sum ( Xi.Xj ) / Sqrt ( sum (Xi^2) . sum (Xj^2) )</pre> * * <p>On return, array CORMAT contains the upper triangle of the matrix of * partial correlations stored by rows, excluding the 1's on the diagonal. http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/regression/OLSMultipleLinearRegression.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/stat/regression/OLSMultipleLinearRegression.java b/src/main/java/org/apache/commons/math4/stat/regression/OLSMultipleLinearRegression.java index 38b38c1..113a04f 100644 --- a/src/main/java/org/apache/commons/math4/stat/regression/OLSMultipleLinearRegression.java +++ b/src/main/java/org/apache/commons/math4/stat/regression/OLSMultipleLinearRegression.java @@ -30,7 +30,7 @@ import org.apache.commons.math4.stat.descriptive.moment.SecondMoment; * multiple linear regression model.</p> * * <p>The regression coefficients, <code>b</code>, satisfy the normal equations: - * <pre><code> X<sup>T</sup> X b = X<sup>T</sup> y </code></pre></p> + * <pre><code> X<sup>T</sup> X b = X<sup>T</sup> y </code></pre> * * <p>To solve the normal equations, this implementation uses QR decomposition * of the <code>X</code> matrix. (See {@link QRDecomposition} for details on the @@ -45,7 +45,7 @@ import org.apache.commons.math4.stat.descriptive.moment.SecondMoment; * R<sup>T</sup> (Q<sup>T</sup>Q) R b = R<sup>T</sup> Q<sup>T</sup> y * R<sup>T</sup> R b = R<sup>T</sup> Q<sup>T</sup> y * (R<sup>T</sup>)<sup>-1</sup> R<sup>T</sup> R b = (R<sup>T</sup>)<sup>-1</sup> R<sup>T</sup> Q<sup>T</sup> y - * R b = Q<sup>T</sup> y </code></pre></p> + * R b = Q<sup>T</sup> y </code></pre> * * <p>Given <code>Q</code> and <code>R</code>, the last equation is solved by back-substitution.</p> * @@ -210,7 +210,7 @@ public class OLSMultipleLinearRegression extends AbstractMultipleLinearRegressio * * <p>If the regression is estimated without an intercept term, what is returned is <pre> * <code> 1 - (1 - {@link #calculateRSquared()}) * (n / (n - p)) </code> - * </pre></p> + * </pre> * * <p>If there is no variance in y, i.e., SSTO = 0, NaN is returned.</p> * http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/regression/RegressionResults.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/stat/regression/RegressionResults.java b/src/main/java/org/apache/commons/math4/stat/regression/RegressionResults.java index bc8f3c1..8d15d49 100644 --- a/src/main/java/org/apache/commons/math4/stat/regression/RegressionResults.java +++ b/src/main/java/org/apache/commons/math4/stat/regression/RegressionResults.java @@ -300,7 +300,7 @@ public class RegressionResults implements Serializable { * must have been added before invoking this method. If this method is * invoked before a model can be estimated, <code>Double.NaN</code> is * returned. - * </li></ul></p> + * </li></ul> * * @return sum of squared deviations of predicted y values */ @@ -322,7 +322,7 @@ public class RegressionResults implements Serializable { * must have been added before invoking this method. If this method is * invoked before a model can be estimated, <code>Double,NaN</code> is * returned. - * </li></ul></p> + * </li></ul> * * @return sum of squared errors associated with the regression model */ @@ -354,7 +354,7 @@ public class RegressionResults implements Serializable { * must have been added before invoking this method. If this method is * invoked before a model can be estimated, {@code Double,NaN} is * returned. - * </li></ul></p> + * </li></ul> * * @return r-square, a double in the interval [0, 1] */ @@ -372,7 +372,7 @@ public class RegressionResults implements Serializable { * * <p>If the regression is estimated without an intercept term, what is returned is <pre> * <code> 1 - (1 - {@link #getRSquared()} ) * (n / (n - p)) </code> - * </pre></p> + * </pre> * * @return adjusted R-Squared statistic */ http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/regression/SimpleRegression.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/stat/regression/SimpleRegression.java b/src/main/java/org/apache/commons/math4/stat/regression/SimpleRegression.java index 55b0d44..201c172 100644 --- a/src/main/java/org/apache/commons/math4/stat/regression/SimpleRegression.java +++ b/src/main/java/org/apache/commons/math4/stat/regression/SimpleRegression.java @@ -57,7 +57,7 @@ import org.apache.commons.numbers.core.Precision; * the {@link #SimpleRegression(boolean)} constructor. When the * {@code hasIntercept} property is false, the model is estimated without a * constant term and {@link #getIntercept()} returns {@code 0}.</li> - * </ul></p> + * </ul> * */ public class SimpleRegression implements Serializable, UpdatingMultipleLinearRegression { @@ -370,7 +370,7 @@ public class SimpleRegression implements Serializable, UpdatingMultipleLinearReg * must have been added before invoking this method. If this method is * invoked before a model can be estimated, <code>Double,NaN</code> is * returned. - * </li></ul></p> + * </li></ul> * * @param x input <code>x</code> value * @return predicted <code>y</code> value @@ -396,7 +396,7 @@ public class SimpleRegression implements Serializable, UpdatingMultipleLinearReg * must have been added before invoking this method. If this method is * invoked before a model can be estimated, <code>Double,NaN</code> is * returned. - * </li></ul></p> + * </li></ul> * * @return the intercept of the regression line if the model includes an * intercept; 0 otherwise @@ -429,7 +429,7 @@ public class SimpleRegression implements Serializable, UpdatingMultipleLinearReg * must have been added before invoking this method. If this method is * invoked before a model can be estimated, <code>Double.NaN</code> is * returned. - * </li></ul></p> + * </li></ul> * * @return the slope of the regression line */ @@ -468,7 +468,7 @@ public class SimpleRegression implements Serializable, UpdatingMultipleLinearReg * must have been added before invoking this method. If this method is * invoked before a model can be estimated, <code>Double,NaN</code> is * returned. - * </li></ul></p> + * </li></ul> * * @return sum of squared errors associated with the regression model */ @@ -496,7 +496,7 @@ public class SimpleRegression implements Serializable, UpdatingMultipleLinearReg /** * Returns the sum of squared deviations of the x values about their mean. * - * If <code>n < 2</code>, this returns <code>Double.NaN</code>.</p> + * If <code>n < 2</code>, this returns <code>Double.NaN</code>. * * @return sum of squared deviations of x values */ @@ -528,7 +528,7 @@ public class SimpleRegression implements Serializable, UpdatingMultipleLinearReg * must have been added before invoking this method. If this method is * invoked before a model can be estimated, <code>Double.NaN</code> is * returned. - * </li></ul></p> + * </li></ul> * * @return sum of squared deviations of predicted y values */ @@ -563,7 +563,7 @@ public class SimpleRegression implements Serializable, UpdatingMultipleLinearReg * must have been added before invoking this method. If this method is * invoked before a model can be estimated, <code>Double,NaN</code> is * returned. - * </li></ul></p> + * </li></ul> * * @return Pearson's r */ @@ -586,7 +586,7 @@ public class SimpleRegression implements Serializable, UpdatingMultipleLinearReg * must have been added before invoking this method. If this method is * invoked before a model can be estimated, <code>Double,NaN</code> is * returned. - * </li></ul></p> + * </li></ul> * * @return r-square */ @@ -681,7 +681,7 @@ public class SimpleRegression implements Serializable, UpdatingMultipleLinearReg * </li> * <li><code>(0 < alpha < 1)</code>; otherwise an * <code>OutOfRangeException</code> is thrown. - * </li></ul></p> + * </li></ul> * * @param alpha the desired significance level * @return half-width of 95% confidence interval for the slope estimate http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/transform/FastCosineTransformer.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/transform/FastCosineTransformer.java b/src/main/java/org/apache/commons/math4/transform/FastCosineTransformer.java index 35d724b..c2c4562 100644 --- a/src/main/java/org/apache/commons/math4/transform/FastCosineTransformer.java +++ b/src/main/java/org/apache/commons/math4/transform/FastCosineTransformer.java @@ -50,7 +50,7 @@ import org.apache.commons.math4.util.FastMath; * data set x<sub>0</sub>, …, x<sub>N-1</sub> is equal to <em>half</em> * of the N first elements of the DFT of the extended data set * x<sub>0</sub><sup>#</sup>, …, x<sub>2N-3</sub><sup>#</sup> - * <br/> + * <br> * y<sub>n</sub> = (1 / 2) ∑<sub>k=0</sub><sup>2N-3</sup> * x<sub>k</sub><sup>#</sup> exp[-2πi nk / (2N - 2)] * k = 0, …, N-1. http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/transform/FastHadamardTransformer.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/transform/FastHadamardTransformer.java b/src/main/java/org/apache/commons/math4/transform/FastHadamardTransformer.java index 1aafada..400cb9f 100644 --- a/src/main/java/org/apache/commons/math4/transform/FastHadamardTransformer.java +++ b/src/main/java/org/apache/commons/math4/transform/FastHadamardTransformer.java @@ -95,8 +95,8 @@ public class FastHadamardTransformer implements RealTransformer, Serializable { * <li><b>y</b> is the output vector (Fast Hadamard transform of <b>x</b>),</li> * <li>a and b are helper rows.</li> * </ol> - * <table align="center" border="1" cellpadding="3"> - * <tbody align="center"> + * <table style="text-align: center" border="1" cellpadding="3" summary="manual calculation for N=8"> + * <tbody style="text-align: center"> * <tr> * <th>x</th> * <th>a</th> @@ -157,7 +157,7 @@ public class FastHadamardTransformer implements RealTransformer, Serializable { * <h3>How it works</h3> * <ol> * <li>Construct a matrix with {@code N} rows and {@code n + 1} columns, - * {@code hadm[n+1][N]}.<br/> + * {@code hadm[n+1][N]}.<br> * <em>(If I use [x][y] it always means [row-offset][column-offset] of a * Matrix with n rows and m columns. Its entries go from M[0][0] * to M[n][N])</em></li> @@ -187,8 +187,8 @@ public class FastHadamardTransformer implements RealTransformer, Serializable { * <li><em>Algorithm from <a href="http://www.archive.chipcenter.com/dsp/DSP000517F1.html";>chipcenter</a>.</em></li> * </ol> * <h3>Visually</h3> - * <table border="1" align="center" cellpadding="3"> - * <tbody align="center"> + * <table border="1" cellpadding="3" style="text-align: center" summary="chipcenter algorithm"> + * <tbody style="text-align: center"> * <tr> * <td></td><th>0</th><th>1</th><th>2</th><th>3</th> * <th>…</th> @@ -198,8 +198,8 @@ public class FastHadamardTransformer implements RealTransformer, Serializable { * <th>0</th> * <td>x<sub>0</sub></td> * <td colspan="5" rowspan="5" align="center" valign="middle"> - * ↑<br/> - * ← D<sub>top</sub> →<br/> + * ↑<br> + * ← D<sub>top</sub> →<br> * ↓ * </td> * </tr> @@ -211,8 +211,8 @@ public class FastHadamardTransformer implements RealTransformer, Serializable { * <th>N / 2</th> * <td>x<sub>N/2</sub></td> * <td colspan="5" rowspan="5" align="center" valign="middle"> - * ↑<br/> - * ← D<sub>bottom</sub> →<br/> + * ↑<br> + * ← D<sub>bottom</sub> →<br> * ↓ * </td> * </tr> http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/transform/FastSineTransformer.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/transform/FastSineTransformer.java b/src/main/java/org/apache/commons/math4/transform/FastSineTransformer.java index 71e2cfb..b4b27ec 100644 --- a/src/main/java/org/apache/commons/math4/transform/FastSineTransformer.java +++ b/src/main/java/org/apache/commons/math4/transform/FastSineTransformer.java @@ -53,7 +53,7 @@ import org.apache.commons.math4.util.FastMath; * data set x<sub>0</sub>, …, x<sub>N-1</sub> is equal to <em>half</em> * of i (the pure imaginary number) times the N first elements of the DFT of the * extended data set x<sub>0</sub><sup>#</sup>, …, - * x<sub>2N-1</sub><sup>#</sup> <br /> + * x<sub>2N-1</sub><sup>#</sup> <br> * y<sub>n</sub> = (i / 2) ∑<sub>k=0</sub><sup>2N-1</sup> * x<sub>k</sub><sup>#</sup> exp[-2πi nk / (2N)] * k = 0, …, N-1. http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/util/Combinations.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/util/Combinations.java b/src/main/java/org/apache/commons/math4/util/Combinations.java index bf8a423..b67e50c 100644 --- a/src/main/java/org/apache/commons/math4/util/Combinations.java +++ b/src/main/java/org/apache/commons/math4/util/Combinations.java @@ -62,7 +62,7 @@ public class Combinations implements Iterable<int[]> { * For example, {@code new Combinations(4, 2).iterator()} returns * an iterator that will generate the following sequence of arrays * on successive calls to - * {@code next()}:<br/> + * {@code next()}:<br> * {@code [0, 1], [0, 2], [1, 2], [0, 3], [1, 3], [2, 3]} * </p> * If {@code k == 0} an iterator containing an empty array is returned; @@ -90,7 +90,7 @@ public class Combinations implements Iterable<int[]> { * For example, {@code new Combinations(4, 2).iterator()} returns * an iterator that will generate the following sequence of arrays * on successive calls to - * {@code next()}:<br/> + * {@code next()}:<br> * {@code [0, 1], [0, 2], [1, 2], [0, 3], [1, 3], [2, 3]} * </p> * If {@code k == 0} an iterator containing an empty array is returned; http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/util/CombinatoricsUtils.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/util/CombinatoricsUtils.java b/src/main/java/org/apache/commons/math4/util/CombinatoricsUtils.java index 6645ed9..a7c40b7 100644 --- a/src/main/java/org/apache/commons/math4/util/CombinatoricsUtils.java +++ b/src/main/java/org/apache/commons/math4/util/CombinatoricsUtils.java @@ -75,7 +75,7 @@ public final class CombinatoricsUtils { * {@code < Long.MAX_VALUE} is 66. If the computed value exceeds * {@code Long.MAX_VALUE} a {@code MathArithMeticException} is * thrown.</li> - * </ul></p> + * </ul> * * @param n the size of the set * @param k the size of the subsets to be counted @@ -153,10 +153,10 @@ public final class CombinatoricsUtils { * <li> {@code 0 <= k <= n } (otherwise * {@code IllegalArgumentException} is thrown)</li> * <li> The result is small enough to fit into a {@code double}. The - * largest value of {@code n} for which all coefficients are < + * largest value of {@code n} for which all coefficients are < * Double.MAX_VALUE is 1029. If the computed value exceeds Double.MAX_VALUE, * Double.POSITIVE_INFINITY is returned</li> - * </ul></p> + * </ul> * * @param n the size of the set * @param k the size of the subsets to be counted @@ -201,7 +201,7 @@ public final class CombinatoricsUtils { * <ul> * <li> {@code 0 <= k <= n } (otherwise * {@code MathIllegalArgumentException} is thrown)</li> - * </ul></p> + * </ul> * * @param n the size of the set * @param k the size of the subsets to be counted @@ -273,7 +273,6 @@ public final class CombinatoricsUtils { * Long.MAX_VALUE} is 20. If the computed value exceeds {@code Long.MAX_VALUE} * an {@code MathArithMeticException } is thrown.</li> * </ul> - * </p> * * @param n argument * @return {@code n!} http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/util/ContinuedFraction.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/util/ContinuedFraction.java b/src/main/java/org/apache/commons/math4/util/ContinuedFraction.java index acd4b2c..536638e 100644 --- a/src/main/java/org/apache/commons/math4/util/ContinuedFraction.java +++ b/src/main/java/org/apache/commons/math4/util/ContinuedFraction.java @@ -31,7 +31,6 @@ import org.apache.commons.math4.exception.util.LocalizedFormats; * <li><a href="http://mathworld.wolfram.com/ContinuedFraction.html";> * Continued Fraction</a></li> * </ul> - * </p> * */ public abstract class ContinuedFraction { @@ -111,7 +110,6 @@ public abstract class ContinuedFraction { * </ul> * <b>Note:</b> the implementation uses the terms a<sub>i</sub> and b<sub>i</sub> as defined in * <a href="http://mathworld.wolfram.com/ContinuedFraction.html";>Continued Fraction @ MathWorld</a>. - * </p> * * @param x the evaluation point. * @param epsilon maximum error allowed. http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/util/FastMath.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/util/FastMath.java b/src/main/java/org/apache/commons/math4/util/FastMath.java index 20a9527..aaa69be 100644 --- a/src/main/java/org/apache/commons/math4/util/FastMath.java +++ b/src/main/java/org/apache/commons/math4/util/FastMath.java @@ -77,7 +77,6 @@ import org.apache.commons.math4.exception.util.LocalizedFormats; * <li>{@link #nextUp(float)}</li> * <li>{@link #scalb(float, int)}</li> * </ul> - * </p> * @since 2.2 */ public class FastMath { @@ -3334,6 +3333,7 @@ public class FastMath { * <li>+MAX_VALUE</li> * <li>+INFINITY</li> * <li></li> + * </ul> * <p> * If arguments compare equal, then the second argument is returned. * <p> @@ -3390,6 +3390,7 @@ public class FastMath { * <li>+MAX_VALUE</li> * <li>+INFINITY</li> * <li></li> + * </ul> * <p> * If arguments compare equal, then the second argument is returned. * <p> @@ -3433,7 +3434,7 @@ public class FastMath { /** Get the largest whole number smaller than x. * @param x number from which floor is requested - * @return a double number f such that f is an integer f <= x < f + 1.0 + * @return a double number f such that f is an integer f <= x < f + 1.0 */ public static double floor(double x) { long y; @@ -3460,7 +3461,7 @@ public class FastMath { /** Get the smallest whole number larger than x. * @param x number from which ceil is requested - * @return a double number c such that c is an integer c - 1.0 < x <= c + * @return a double number c such that c is an integer c - 1.0 < x <= c */ public static double ceil(double x) { double y; @@ -3485,7 +3486,7 @@ public class FastMath { /** Get the whole number that is the nearest to x, or the even one if x is exactly half way between two integers. * @param x number from which nearest whole number is requested - * @return a double number r such that r is an integer r - 0.5 <= x <= r + 0.5 + * @return a double number r such that r is an integer r - 0.5 <= x <= r + 0.5 */ public static double rint(double x) { double y = floor(x); @@ -3696,7 +3697,7 @@ public class FastMath { /** * Returns the hypotenuse of a triangle with sides {@code x} and {@code y} - * - sqrt(<i>x</i><sup>2</sup> +<i>y</i><sup>2</sup>)<br/> + * - sqrt(<i>x</i><sup>2</sup> +<i>y</i><sup>2</sup>)<br> * avoiding intermediate overflow or underflow. * * <ul> @@ -3750,7 +3751,6 @@ public class FastMath { * of the quotient {@code x/y}. * If two mathematical integers are equally close to {@code x/y} then * {@code n} is the integer that is even. - * <p> * <ul> * <li>If either operand is NaN, the result is NaN.</li> * <li>If the result is not NaN, the sign of the result equals the sign of the dividend.</li> @@ -3971,7 +3971,7 @@ public class FastMath { return a * b; } - /** Finds q such that a = q b + r with 0 <= r < b if b > 0 and b < r <= 0 if b < 0. + /** Finds q such that a = q b + r with 0 <= r < b if b > 0 and b < r <= 0 if b < 0. * <p> * This methods returns the same value as integer division when * a and b are same signs, but returns a different value when @@ -3979,7 +3979,7 @@ public class FastMath { * </p> * @param a dividend * @param b divisor - * @return q such that a = q b + r with 0 <= r < b if b > 0 and b < r <= 0 if b < 0 + * @return q such that a = q b + r with 0 <= r < b if b > 0 and b < r <= 0 if b < 0 * @exception MathArithmeticException if b == 0 * @see #floorMod(int, int) * @since 3.4 @@ -4001,7 +4001,7 @@ public class FastMath { } - /** Finds q such that a = q b + r with 0 <= r < b if b > 0 and b < r <= 0 if b < 0. + /** Finds q such that a = q b + r with 0 <= r < b if b > 0 and b < r <= 0 if b < 0. * <p> * This methods returns the same value as integer division when * a and b are same signs, but returns a different value when @@ -4009,7 +4009,7 @@ public class FastMath { * </p> * @param a dividend * @param b divisor - * @return q such that a = q b + r with 0 <= r < b if b > 0 and b < r <= 0 if b < 0 + * @return q such that a = q b + r with 0 <= r < b if b > 0 and b < r <= 0 if b < 0 * @exception MathArithmeticException if b == 0 * @see #floorMod(long, long) * @since 3.4 @@ -4031,7 +4031,7 @@ public class FastMath { } - /** Finds r such that a = q b + r with 0 <= r < b if b > 0 and b < r <= 0 if b < 0. + /** Finds r such that a = q b + r with 0 <= r < b if b > 0 and b < r <= 0 if b < 0. * <p> * This methods returns the same value as integer modulo when * a and b are same signs, but returns a different value when @@ -4039,7 +4039,7 @@ public class FastMath { * </p> * @param a dividend * @param b divisor - * @return r such that a = q b + r with 0 <= r < b if b > 0 and b < r <= 0 if b < 0 + * @return r such that a = q b + r with 0 <= r < b if b > 0 and b < r <= 0 if b < 0 * @exception MathArithmeticException if b == 0 * @see #floorDiv(int, int) * @since 3.4 @@ -4061,7 +4061,7 @@ public class FastMath { } - /** Finds r such that a = q b + r with 0 <= r < b if b > 0 and b < r <= 0 if b < 0. + /** Finds r such that a = q b + r with 0 <= r < b if b > 0 and b < r <= 0 if b < 0. * <p> * This methods returns the same value as integer modulo when * a and b are same signs, but returns a different value when @@ -4069,7 +4069,7 @@ public class FastMath { * </p> * @param a dividend * @param b divisor - * @return r such that a = q b + r with 0 <= r < b if b > 0 and b < r <= 0 if b < 0 + * @return r such that a = q b + r with 0 <= r < b if b > 0 and b < r <= 0 if b < 0 * @exception MathArithmeticException if b == 0 * @see #floorDiv(long, long) * @since 3.4 http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/util/IntegerSequence.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/util/IntegerSequence.java b/src/main/java/org/apache/commons/math4/util/IntegerSequence.java index 0ea1949..4712a4b 100644 --- a/src/main/java/org/apache/commons/math4/util/IntegerSequence.java +++ b/src/main/java/org/apache/commons/math4/util/IntegerSequence.java @@ -48,10 +48,10 @@ public class IntegerSequence { } /** - * Creates a sequence <code>a<sub>i</sub>, i < 0 < n</code> + * Creates a sequence <code>a<sub>i</sub>, i < 0 < n</code> * where <code>a<sub>i</sub> = start + i * step</code> - * and {@code n} is such that <code>a<sub>n</sub> <= max</code> - * and <code>a<sub>n+1</sub> > max</code>. + * and {@code n} is such that <code>a<sub>n</sub> <= max</code> + * and <code>a<sub>n+1</sub> > max</code>. * * @param start First value of the range. * @param max Last value of the range that satisfies the above @@ -79,10 +79,10 @@ public class IntegerSequence { private final int step; /** - * Creates a sequence <code>a<sub>i</sub>, i < 0 < n</code> + * Creates a sequence <code>a<sub>i</sub>, i < 0 < n</code> * where <code>a<sub>i</sub> = start + i * step</code> - * and {@code n} is such that <code>a<sub>n</sub> <= max</code> - * and <code>a<sub>n+1</sub> > max</code>. + * and {@code n} is such that <code>a<sub>n</sub> <= max</code> + * and <code>a<sub>n+1</sub> > max</code>. * * @param start First value of the range. * @param max Last value of the range that satisfies the above @@ -359,7 +359,7 @@ public class IntegerSequence { /** * Not applicable. * - * @throws MathUnsupportedOperationException + * @throws MathUnsupportedOperationException always */ @Override public void remove() {
