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Posted to commits@commons.apache.org by ra...@apache.org on 2017/05/07 16:36:53 UTC
[2/5] [math] Fix some javadoc issues.
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() {