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Posted to commits@commons.apache.org by ra...@apache.org on 2017/05/07 16:36:54 UTC
[3/5] [math] Fix some javadoc issues.
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/gradient/NonLinearConjugateGradientOptimizer.java
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diff --git a/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/gradient/NonLinearConjugateGradientOptimizer.java b/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/gradient/NonLinearConjugateGradientOptimizer.java
index 75dd024..2bd188e 100644
--- a/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/gradient/NonLinearConjugateGradientOptimizer.java
+++ b/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/gradient/NonLinearConjugateGradientOptimizer.java
@@ -31,11 +31,11 @@ import org.apache.commons.math4.optim.nonlinear.scalar.LineSearch;
/**
* Non-linear conjugate gradient optimizer.
- * <br/>
+ * <br>
* This class supports both the Fletcher-Reeves and the Polak-Ribière
* update formulas for the conjugate search directions.
* It also supports optional preconditioning.
- * <br/>
+ * <br>
* Constraints are not supported: the call to
* {@link #optimize(OptimizationData[]) optimize} will throw
* {@link MathUnsupportedOperationException} if bounds are passed to it.
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/AbstractSimplex.java
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diff --git a/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/AbstractSimplex.java b/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/AbstractSimplex.java
index 45da036..4ddfaed 100644
--- a/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/AbstractSimplex.java
+++ b/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/AbstractSimplex.java
@@ -34,12 +34,12 @@ import org.apache.commons.math4.optim.PointValuePair;
/**
* This class implements the simplex concept.
* It is intended to be used in conjunction with {@link SimplexOptimizer}.
- * <br/>
+ * <br>
* The initial configuration of the simplex is set by the constructors
* {@link #AbstractSimplex(double[])} or {@link #AbstractSimplex(double[][])}.
* The other {@link #AbstractSimplex(int) constructor} will set all steps
* to 1, thus building a default configuration from a unit hypercube.
- * <br/>
+ * <br>
* Users <em>must</em> call the {@link #build(double[]) build} method in order
* to create the data structure that will be acted on by the other methods of
* this class.
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/BOBYQAOptimizer.java
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diff --git a/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/BOBYQAOptimizer.java b/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/BOBYQAOptimizer.java
index 74bf89d..4366b15 100644
--- a/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/BOBYQAOptimizer.java
+++ b/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/BOBYQAOptimizer.java
@@ -35,7 +35,7 @@ import org.apache.commons.math4.util.FastMath;
* <a href="http://plato.asu.edu/ftp/other_software/bobyqa.zip">here</a>.
* See <a href="http://www.optimization-online.org/DB_HTML/2010/05/2616.html">
* this paper</a> for an introduction.
- * <br/>
+ * <br>
* BOBYQA is particularly well suited for high dimensional problems
* where derivatives are not available. In most cases it outperforms the
* {@link PowellOptimizer} significantly. Stochastic algorithms like
@@ -148,9 +148,9 @@ public class BOBYQAOptimizer
/**
* Differences {@link #getLowerBound()} - {@link #originShift}.
* All the components of every {@link #trustRegionCenterOffset} are going
- * to satisfy the bounds<br/>
+ * to satisfy the bounds<br>
* {@link #getLowerBound() lowerBound}<sub>i</sub> ≤
- * {@link #trustRegionCenterOffset}<sub>i</sub>,<br/>
+ * {@link #trustRegionCenterOffset}<sub>i</sub>,<br>
* with appropriate equalities when {@link #trustRegionCenterOffset} is
* on a constraint boundary.
* XXX "sl" in the original code.
@@ -159,9 +159,9 @@ public class BOBYQAOptimizer
/**
* Differences {@link #getUpperBound()} - {@link #originShift}
* All the components of every {@link #trustRegionCenterOffset} are going
- * to satisfy the bounds<br/>
+ * to satisfy the bounds<br>
* {@link #trustRegionCenterOffset}<sub>i</sub> ≤
- * {@link #getUpperBound() upperBound}<sub>i</sub>,<br/>
+ * {@link #getUpperBound() upperBound}<sub>i</sub>,<br>
* with appropriate equalities when {@link #trustRegionCenterOffset} is
* on a constraint boundary.
* XXX "su" in the original code.
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/CMAESOptimizer.java
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diff --git a/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/CMAESOptimizer.java b/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/CMAESOptimizer.java
index e47266a..18af3c0 100644
--- a/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/CMAESOptimizer.java
+++ b/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/CMAESOptimizer.java
@@ -56,16 +56,16 @@ import org.apache.commons.math4.util.MathArrays;
* <p>
* In general, on smooth objective functions the CMA-ES is roughly ten times
* slower than BFGS (counting objective function evaluations, no gradients provided).
- * For up to <math>N=10</math> variables also the derivative-free simplex
+ * For up to <code>N=10</code> variables also the derivative-free simplex
* direct search method (Nelder and Mead) can be faster, but it is
* far less reliable than CMA-ES.
* <p>
* The CMA-ES is particularly well suited for non-separable
* and/or badly conditioned problems. To observe the advantage of CMA compared
* to a conventional evolution strategy, it will usually take about
- * <math>30 N</math> function evaluations. On difficult problems the complete
+ * <code>30 N</code> function evaluations. On difficult problems the complete
* optimization (a single run) is expected to take <em>roughly</em> between
- * <math>30 N</math> and <math>300 N<sup>2</sup></math>
+ * <code>30 N</code> and <code>300 N<sup>2</sup></code>
* function evaluations.
* <p>
* This implementation is translated and adapted from the Matlab version
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/PowellOptimizer.java
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diff --git a/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/PowellOptimizer.java b/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/PowellOptimizer.java
index 90d81de..f4d5fed 100644
--- a/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/PowellOptimizer.java
+++ b/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/PowellOptimizer.java
@@ -34,14 +34,14 @@ import org.apache.commons.math4.util.MathArrays;
* This code is translated and adapted from the Python version of this
* algorithm (as implemented in module {@code optimize.py} v0.5 of
* <em>SciPy</em>).
- * <br/>
+ * <br>
* The default stopping criterion is based on the differences of the
* function value between two successive iterations. It is however possible
* to define a custom convergence checker that might terminate the algorithm
* earlier.
- * <br/>
+ * <br>
* Line search is performed by the {@link LineSearch} class.
- * <br/>
+ * <br>
* Constraints are not supported: the call to
* {@link #optimize(OptimizationData[]) optimize} will throw
* {@link MathUnsupportedOperationException} if bounds are passed to it.
@@ -77,7 +77,7 @@ public class PowellOptimizer
* This constructor allows to specify a user-defined convergence checker,
* in addition to the parameters that control the default convergence
* checking procedure.
- * <br/>
+ * <br>
* The internal line search tolerances are set to the square-root of their
* corresponding value in the multivariate optimizer.
*
@@ -131,7 +131,7 @@ public class PowellOptimizer
/**
* The parameters control the default convergence checking procedure.
- * <br/>
+ * <br>
* The internal line search tolerances are set to the square-root of their
* corresponding value in the multivariate optimizer.
*
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/SimplexOptimizer.java
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diff --git a/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/SimplexOptimizer.java b/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/SimplexOptimizer.java
index 9658944..54e2884 100644
--- a/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/SimplexOptimizer.java
+++ b/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/SimplexOptimizer.java
@@ -50,7 +50,7 @@ import org.apache.commons.math4.optim.nonlinear.scalar.MultivariateOptimizer;
* the objective function values at the vertices of a simplex (which is a
* set of n+1 points in dimension n) that is updated by the algorithms
* steps.
- * <p>
+ * </p>
* <p>
* The simplex update procedure ({@link NelderMeadSimplex} or
* {@link MultiDirectionalSimplex}) must be passed to the
@@ -79,7 +79,7 @@ import org.apache.commons.math4.optim.nonlinear.scalar.MultivariateOptimizer;
* MultivariateFunctionMappingAdapter} or
* {@link org.apache.commons.math4.optim.nonlinear.scalar.MultivariateFunctionPenaltyAdapter
* MultivariateFunctionPenaltyAdapter}.
- * <br/>
+ * <br>
* The call to {@link #optimize(OptimizationData[]) optimize} will throw
* {@link MathUnsupportedOperationException} if bounds are passed to it.
* </p>
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/optim/package-info.java
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diff --git a/src/main/java/org/apache/commons/math4/optim/package-info.java b/src/main/java/org/apache/commons/math4/optim/package-info.java
index 8f3d99c..d6b6348 100644
--- a/src/main/java/org/apache/commons/math4/optim/package-info.java
+++ b/src/main/java/org/apache/commons/math4/optim/package-info.java
@@ -23,7 +23,7 @@
* a scalar function, called the
* {@link org.apache.commons.math4.optim.nonlinear.scalar.ObjectiveFunction <em>objective
* function</em>}.
- * <br/>
+ * <br>
* For some scalar objective functions the gradient can be computed (analytically
* or numerically). Algorithms that use this knowledge are defined in the
* {@link org.apache.commons.math4.optim.nonlinear.scalar.gradient} package.
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/optim/univariate/BrentOptimizer.java
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diff --git a/src/main/java/org/apache/commons/math4/optim/univariate/BrentOptimizer.java b/src/main/java/org/apache/commons/math4/optim/univariate/BrentOptimizer.java
index e1b2d9d..6cc67b8 100644
--- a/src/main/java/org/apache/commons/math4/optim/univariate/BrentOptimizer.java
+++ b/src/main/java/org/apache/commons/math4/optim/univariate/BrentOptimizer.java
@@ -30,7 +30,7 @@ import org.apache.commons.numbers.core.Precision;
* It implements Richard Brent's algorithm (from his book "Algorithms for
* Minimization without Derivatives", p. 79) for finding minima of real
* univariate functions.
- * <br/>
+ * <br>
* This code is an adaptation, partly based on the Python code from SciPy
* (module "optimize.py" v0.5); the original algorithm is also modified
* <ul>
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/optim/univariate/MultiStartUnivariateOptimizer.java
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diff --git a/src/main/java/org/apache/commons/math4/optim/univariate/MultiStartUnivariateOptimizer.java b/src/main/java/org/apache/commons/math4/optim/univariate/MultiStartUnivariateOptimizer.java
index 373a307..6b3e9af 100644
--- a/src/main/java/org/apache/commons/math4/optim/univariate/MultiStartUnivariateOptimizer.java
+++ b/src/main/java/org/apache/commons/math4/optim/univariate/MultiStartUnivariateOptimizer.java
@@ -31,7 +31,7 @@ import org.apache.commons.rng.UniformRandomProvider;
/**
* Special implementation of the {@link UnivariateOptimizer} interface
* adding multi-start features to an existing optimizer.
- * <br/>
+ * <br>
* This class wraps an optimizer in order to use it several times in
* turn with different starting points (trying to avoid being trapped
* in a local extremum when looking for a global one).
@@ -99,7 +99,7 @@ public class MultiStartUnivariateOptimizer
* restarts. The {@code optimize} method returns the best point only.
* This method returns all the points found at the end of each starts,
* including the best one already returned by the {@code optimize} method.
- * <br/>
+ * <br>
* The returned array as one element for each start as specified
* in the constructor. It is ordered with the results from the
* runs that did converge first, sorted from best to worst
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/optim/univariate/SearchInterval.java
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diff --git a/src/main/java/org/apache/commons/math4/optim/univariate/SearchInterval.java b/src/main/java/org/apache/commons/math4/optim/univariate/SearchInterval.java
index 172cf80..8bed386 100644
--- a/src/main/java/org/apache/commons/math4/optim/univariate/SearchInterval.java
+++ b/src/main/java/org/apache/commons/math4/optim/univariate/SearchInterval.java
@@ -22,7 +22,7 @@ import org.apache.commons.math4.optim.OptimizationData;
/**
* Search interval and (optional) start value.
- * <br/>
+ * <br>
* Immutable class.
*
* @since 3.1
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/optim/univariate/SimpleUnivariateValueChecker.java
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diff --git a/src/main/java/org/apache/commons/math4/optim/univariate/SimpleUnivariateValueChecker.java b/src/main/java/org/apache/commons/math4/optim/univariate/SimpleUnivariateValueChecker.java
index b37a5b3..878dd61 100644
--- a/src/main/java/org/apache/commons/math4/optim/univariate/SimpleUnivariateValueChecker.java
+++ b/src/main/java/org/apache/commons/math4/optim/univariate/SimpleUnivariateValueChecker.java
@@ -29,7 +29,7 @@ import org.apache.commons.math4.util.FastMath;
* difference between the objective function values is smaller than a
* threshold or if either the absolute difference between the objective
* function values is smaller than another threshold.
- * <br/>
+ * <br>
* The {@link #converged(int,UnivariatePointValuePair,UnivariatePointValuePair)
* converged} method will also return {@code true} if the number of iterations
* has been set (see {@link #SimpleUnivariateValueChecker(double,double,int)
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/random/RngAdaptor.java
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diff --git a/src/main/java/org/apache/commons/math4/random/RngAdaptor.java b/src/main/java/org/apache/commons/math4/random/RngAdaptor.java
index 2852d58..7e91357 100644
--- a/src/main/java/org/apache/commons/math4/random/RngAdaptor.java
+++ b/src/main/java/org/apache/commons/math4/random/RngAdaptor.java
@@ -63,7 +63,7 @@ import org.apache.commons.rng.core.RandomProviderDefaultState;
* new API} as it defines a "post-processing" of the output of a
* <i>uniform</i> RNG in order to follow a different distribution.
* </li>
- * </p>
+ * </ul>
*
* @since 4.0
*
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/special/BesselJ.java
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diff --git a/src/main/java/org/apache/commons/math4/special/BesselJ.java b/src/main/java/org/apache/commons/math4/special/BesselJ.java
index ff82161..b677939 100644
--- a/src/main/java/org/apache/commons/math4/special/BesselJ.java
+++ b/src/main/java/org/apache/commons/math4/special/BesselJ.java
@@ -50,7 +50,7 @@ import org.apache.commons.math4.util.MathArrays;
* D. J., Math. Comp. 26, 1972, pp 941-947.</li>
* <li>"Bessel Functions of Real Argument and Integer Order," Sookne, D. J., NBS
* Jour. of Res. B. 77B, 1973, pp 125-132.</li>
- * </ul> </p>
+ * </ul>
* @since 3.4
*/
public class BesselJ
@@ -176,8 +176,7 @@ public class BesselJ
* {@link #getVals()} returns the computed function values.
* {@link #getnVals()} is the number of values among those returned by {@link #getnVals()}
* that can be considered accurate.
- * </p><p>
- * <ul>
+ * </p><ul>
* <li>nVals < 0: An argument is out of range. For example, nb <= 0, alpha
* < 0 or > 1, or x is too large. In this case, b(0) is set to zero, the
* remainder of the b-vector is not calculated, and nVals is set to
@@ -188,7 +187,7 @@ public class BesselJ
* precision is lost for nVals < n <= nb. If b(n) does not vanish for n >
* nVals (because it is too small to be represented), and b(n)/b(nVals) =
* \(10^{-k}\), then only the first NSIG-k significant figures of b(n) can be
- * trusted.</li></ul></p>
+ * trusted.</li></ul>
*/
public static class BesselJResult {
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/special/Erf.java
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diff --git a/src/main/java/org/apache/commons/math4/special/Erf.java b/src/main/java/org/apache/commons/math4/special/Erf.java
index 9872230..c0c4ce0 100644
--- a/src/main/java/org/apache/commons/math4/special/Erf.java
+++ b/src/main/java/org/apache/commons/math4/special/Erf.java
@@ -29,10 +29,10 @@ public class Erf {
* The number {@code X_CRIT} is used by {@link #erf(double, double)} internally.
* This number solves {@code erf(x)=0.5} within 1ulp.
* More precisely, the current implementations of
- * {@link #erf(double)} and {@link #erfc(double)} satisfy:<br/>
- * {@code erf(X_CRIT) < 0.5},<br/>
- * {@code erf(Math.nextUp(X_CRIT) > 0.5},<br/>
- * {@code erfc(X_CRIT) = 0.5}, and<br/>
+ * {@link #erf(double)} and {@link #erfc(double)} satisfy:<br>
+ * {@code erf(X_CRIT) < 0.5},<br>
+ * {@code erf(Math.nextUp(X_CRIT) > 0.5},<br>
+ * {@code erfc(X_CRIT) = 0.5}, and<br>
* {@code erfc(Math.nextUp(X_CRIT) < 0.5}
*/
private static final double X_CRIT = 0.4769362762044697;
@@ -74,7 +74,7 @@ public class Erf {
* Returns the complementary error function.
*
* <p>erfc(x) = 2/√π <sub>x</sub>∫<sup>∞</sup> e<sup>-t<sup>2</sup></sup>dt
- * <br/>
+ * <br>
* = 1 - {@link #erf(double) erf(x)} </p>
*
* <p>This implementation computes erfc(x) using the
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/Frequency.java
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diff --git a/src/main/java/org/apache/commons/math4/stat/Frequency.java b/src/main/java/org/apache/commons/math4/stat/Frequency.java
index f676720..dfcca5a 100644
--- a/src/main/java/org/apache/commons/math4/stat/Frequency.java
+++ b/src/main/java/org/apache/commons/math4/stat/Frequency.java
@@ -56,7 +56,7 @@ import org.apache.commons.math4.util.MathUtils;
* Since they are not Comparable with each other the user must do any necessary coercion.
* Float.NaN and Double.NaN are not treated specially; they may occur in input and will
* occur in output if appropriate.
- * </b>
+ * </p>
* <p>
* The values are ordered using the default (natural order), unless a
* <code>Comparator</code> is supplied in the constructor.</p>
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/StatUtils.java
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diff --git a/src/main/java/org/apache/commons/math4/stat/StatUtils.java b/src/main/java/org/apache/commons/math4/stat/StatUtils.java
index a6813ce..d267bce 100644
--- a/src/main/java/org/apache/commons/math4/stat/StatUtils.java
+++ b/src/main/java/org/apache/commons/math4/stat/StatUtils.java
@@ -520,7 +520,6 @@ public final class StatUtils {
* <code>Double.NaN</code> if the array is empty.
* <p>
* Throws <code>MathIllegalArgumentException</code> if the array is null.
- * <p>
* <ul>
* <li>The result is <code>NaN</code> iff all values are <code>NaN</code>
* (i.e. <code>NaN</code> values have no impact on the value of the statistic).</li>
@@ -542,7 +541,6 @@ public final class StatUtils {
* <p>
* Throws <code>MathIllegalArgumentException</code> if the array is null or
* the array index parameters are not valid.
- * <p>
* <ul>
* <li>The result is <code>NaN</code> iff all values are <code>NaN</code>
* (i.e. <code>NaN</code> values have no impact on the value of the statistic).</li>
@@ -567,7 +565,6 @@ public final class StatUtils {
* <code>Double.NaN</code> if the array is empty.
* <p>
* Throws <code>MathIllegalArgumentException</code> if the array is null.
- * <p>
* <ul>
* <li>The result is <code>NaN</code> iff all values are <code>NaN</code>
* (i.e. <code>NaN</code> values have no impact on the value of the statistic).</li>
@@ -589,7 +586,6 @@ public final class StatUtils {
* <p>
* Throws <code>MathIllegalArgumentException</code> if the array is null or
* the array index parameters are not valid.
- * <p>
* <ul>
* <li>The result is <code>NaN</code> iff all values are <code>NaN</code>
* (i.e. <code>NaN</code> values have no impact on the value of the statistic).</li>
@@ -612,7 +608,6 @@ public final class StatUtils {
/**
* Returns an estimate of the <code>p</code>th percentile of the values
* in the <code>values</code> array.
- * <p>
* <ul>
* <li>Returns <code>Double.NaN</code> if <code>values</code> has length
* <code>0</code></li>
@@ -640,7 +635,6 @@ public final class StatUtils {
* in the <code>values</code> array, starting with the element in (0-based)
* position <code>begin</code> in the array and including <code>length</code>
* values.
- * <p>
* <ul>
* <li>Returns <code>Double.NaN</code> if <code>length = 0</code></li>
* <li>Returns (for any value of <code>p</code>) <code>values[begin]</code>
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/correlation/KendallsCorrelation.java
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diff --git a/src/main/java/org/apache/commons/math4/stat/correlation/KendallsCorrelation.java b/src/main/java/org/apache/commons/math4/stat/correlation/KendallsCorrelation.java
index 32ed6d0..1fdb155 100644
--- a/src/main/java/org/apache/commons/math4/stat/correlation/KendallsCorrelation.java
+++ b/src/main/java/org/apache/commons/math4/stat/correlation/KendallsCorrelation.java
@@ -27,7 +27,7 @@ import org.apache.commons.math4.util.FastMath;
import org.apache.commons.math4.util.Pair;
/**
- * Implementation of Kendall's Tau-b rank correlation</a>.
+ * Implementation of Kendall's Tau-b rank correlation.
* <p>
* A pair of observations (x<sub>1</sub>, y<sub>1</sub>) and
* (x<sub>2</sub>, y<sub>2</sub>) are considered <i>concordant</i> if
@@ -40,9 +40,9 @@ import org.apache.commons.math4.util.Pair;
* discordant.
* <p>
* Kendall's Tau-b is defined as:
- * <pre>
+ * <div style="white-space: pre"><code>
* tau<sub>b</sub> = (n<sub>c</sub> - n<sub>d</sub>) / sqrt((n<sub>0</sub> - n<sub>1</sub>) * (n<sub>0</sub> - n<sub>2</sub>))
- * </pre>
+ * </code></div>
* <p>
* where:
* <ul>
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/correlation/PearsonsCorrelation.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/correlation/PearsonsCorrelation.java b/src/main/java/org/apache/commons/math4/stat/correlation/PearsonsCorrelation.java
index 6059752..b14f8a6 100644
--- a/src/main/java/org/apache/commons/math4/stat/correlation/PearsonsCorrelation.java
+++ b/src/main/java/org/apache/commons/math4/stat/correlation/PearsonsCorrelation.java
@@ -144,11 +144,11 @@ public class PearsonsCorrelation {
/**
* Returns a matrix of standard errors associated with the estimates
- * in the correlation matrix.<br/>
+ * in the correlation matrix.<br>
* <code>getCorrelationStandardErrors().getEntry(i,j)</code> is the standard
* error associated with <code>getCorrelationMatrix.getEntry(i,j)</code>
*
- * <p>The formula used to compute the standard error is <br/>
+ * <p>The formula used to compute the standard error is <br>
* <code>SE<sub>r</sub> = ((1 - r<sup>2</sup>) / (n - 2))<sup>1/2</sup></code>
* where <code>r</code> is the estimated correlation coefficient and
* <code>n</code> is the number of observations in the source dataset.</p>
@@ -288,9 +288,9 @@ public class PearsonsCorrelation {
/**
* Derives a correlation matrix from a covariance matrix.
*
- * <p>Uses the formula <br/>
+ * <p>Uses the formula <br>
* <code>r(X,Y) = cov(X,Y)/s(X)s(Y)</code> where
- * <code>r(·,·)</code> is the correlation coefficient and
+ * <code>r(·,·)</code> is the correlation coefficient and
* <code>s(·)</code> means standard deviation.</p>
*
* @param covarianceMatrix the covariance matrix
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/descriptive/DescriptiveStatistics.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/descriptive/DescriptiveStatistics.java b/src/main/java/org/apache/commons/math4/stat/descriptive/DescriptiveStatistics.java
index 99e02cc..8ae7d7a 100644
--- a/src/main/java/org/apache/commons/math4/stat/descriptive/DescriptiveStatistics.java
+++ b/src/main/java/org/apache/commons/math4/stat/descriptive/DescriptiveStatistics.java
@@ -435,7 +435,7 @@ public class DescriptiveStatistics implements StatisticalSummary, Serializable {
* <code>MathIllegalArgumentException</code> is thrown)</li>
* <li>at least one value must be stored (returns <code>Double.NaN
* </code> otherwise)</li>
- * </ul></p>
+ * </ul>
*
* @param p the requested percentile (scaled from 0 - 100)
* @return An estimate for the pth percentile of the stored data
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/descriptive/MultivariateSummaryStatistics.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/descriptive/MultivariateSummaryStatistics.java b/src/main/java/org/apache/commons/math4/stat/descriptive/MultivariateSummaryStatistics.java
index a161fc8..5248b5c 100644
--- a/src/main/java/org/apache/commons/math4/stat/descriptive/MultivariateSummaryStatistics.java
+++ b/src/main/java/org/apache/commons/math4/stat/descriptive/MultivariateSummaryStatistics.java
@@ -453,7 +453,7 @@ public class MultivariateSummaryStatistics
* @throws DimensionMismatchException if the array dimension
* does not match the one used at construction
* @throws MathIllegalStateException if data has already been added
- * (i.e if n > 0)
+ * (i.e if n > 0)
*/
public void setSumImpl(StorelessUnivariateStatistic[] sumImpl)
throws MathIllegalStateException, DimensionMismatchException {
@@ -480,7 +480,7 @@ public class MultivariateSummaryStatistics
* @throws DimensionMismatchException if the array dimension
* does not match the one used at construction
* @throws MathIllegalStateException if data has already been added
- * (i.e if n > 0)
+ * (i.e if n > 0)
*/
public void setSumsqImpl(StorelessUnivariateStatistic[] sumsqImpl)
throws MathIllegalStateException, DimensionMismatchException {
@@ -507,7 +507,7 @@ public class MultivariateSummaryStatistics
* @throws DimensionMismatchException if the array dimension
* does not match the one used at construction
* @throws MathIllegalStateException if data has already been added
- * (i.e if n > 0)
+ * (i.e if n > 0)
*/
public void setMinImpl(StorelessUnivariateStatistic[] minImpl)
throws MathIllegalStateException, DimensionMismatchException {
@@ -534,7 +534,7 @@ public class MultivariateSummaryStatistics
* @throws DimensionMismatchException if the array dimension
* does not match the one used at construction
* @throws MathIllegalStateException if data has already been added
- * (i.e if n > 0)
+ * (i.e if n > 0)
*/
public void setMaxImpl(StorelessUnivariateStatistic[] maxImpl)
throws MathIllegalStateException, DimensionMismatchException{
@@ -561,7 +561,7 @@ public class MultivariateSummaryStatistics
* @throws DimensionMismatchException if the array dimension
* does not match the one used at construction
* @throws MathIllegalStateException if data has already been added
- * (i.e if n > 0)
+ * (i.e if n > 0)
*/
public void setSumLogImpl(StorelessUnivariateStatistic[] sumLogImpl)
throws MathIllegalStateException, DimensionMismatchException{
@@ -588,7 +588,7 @@ public class MultivariateSummaryStatistics
* @throws DimensionMismatchException if the array dimension
* does not match the one used at construction
* @throws MathIllegalStateException if data has already been added
- * (i.e if n > 0)
+ * (i.e if n > 0)
*/
public void setGeoMeanImpl(StorelessUnivariateStatistic[] geoMeanImpl)
throws MathIllegalStateException, DimensionMismatchException {
@@ -615,7 +615,7 @@ public class MultivariateSummaryStatistics
* @throws DimensionMismatchException if the array dimension
* does not match the one used at construction
* @throws MathIllegalStateException if data has already been added
- * (i.e if n > 0)
+ * (i.e if n > 0)
*/
public void setMeanImpl(StorelessUnivariateStatistic[] meanImpl)
throws MathIllegalStateException, DimensionMismatchException{
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/descriptive/SummaryStatistics.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/descriptive/SummaryStatistics.java b/src/main/java/org/apache/commons/math4/stat/descriptive/SummaryStatistics.java
index 91f77fb..9ab88c1 100644
--- a/src/main/java/org/apache/commons/math4/stat/descriptive/SummaryStatistics.java
+++ b/src/main/java/org/apache/commons/math4/stat/descriptive/SummaryStatistics.java
@@ -446,7 +446,7 @@ public class SummaryStatistics implements StatisticalSummary, Serializable {
* </p>
* @param sumImpl the StorelessUnivariateStatistic instance to use for
* computing the Sum
- * @throws MathIllegalStateException if data has already been added (i.e if n >0)
+ * @throws MathIllegalStateException if data has already been added (i.e if n >0)
* @since 1.2
*/
public void setSumImpl(StorelessUnivariateStatistic sumImpl)
@@ -476,7 +476,7 @@ public class SummaryStatistics implements StatisticalSummary, Serializable {
* </p>
* @param sumsqImpl the StorelessUnivariateStatistic instance to use for
* computing the sum of squares
- * @throws MathIllegalStateException if data has already been added (i.e if n > 0)
+ * @throws MathIllegalStateException if data has already been added (i.e if n > 0)
* @since 1.2
*/
public void setSumsqImpl(StorelessUnivariateStatistic sumsqImpl)
@@ -506,7 +506,7 @@ public class SummaryStatistics implements StatisticalSummary, Serializable {
* </p>
* @param minImpl the StorelessUnivariateStatistic instance to use for
* computing the minimum
- * @throws MathIllegalStateException if data has already been added (i.e if n > 0)
+ * @throws MathIllegalStateException if data has already been added (i.e if n > 0)
* @since 1.2
*/
public void setMinImpl(StorelessUnivariateStatistic minImpl)
@@ -536,7 +536,7 @@ public class SummaryStatistics implements StatisticalSummary, Serializable {
* </p>
* @param maxImpl the StorelessUnivariateStatistic instance to use for
* computing the maximum
- * @throws MathIllegalStateException if data has already been added (i.e if n > 0)
+ * @throws MathIllegalStateException if data has already been added (i.e if n > 0)
* @since 1.2
*/
public void setMaxImpl(StorelessUnivariateStatistic maxImpl)
@@ -566,7 +566,7 @@ public class SummaryStatistics implements StatisticalSummary, Serializable {
* </p>
* @param sumLogImpl the StorelessUnivariateStatistic instance to use for
* computing the log sum
- * @throws MathIllegalStateException if data has already been added (i.e if n > 0)
+ * @throws MathIllegalStateException if data has already been added (i.e if n > 0)
* @since 1.2
*/
public void setSumLogImpl(StorelessUnivariateStatistic sumLogImpl)
@@ -597,7 +597,7 @@ public class SummaryStatistics implements StatisticalSummary, Serializable {
* </p>
* @param geoMeanImpl the StorelessUnivariateStatistic instance to use for
* computing the geometric mean
- * @throws MathIllegalStateException if data has already been added (i.e if n > 0)
+ * @throws MathIllegalStateException if data has already been added (i.e if n > 0)
* @since 1.2
*/
public void setGeoMeanImpl(StorelessUnivariateStatistic geoMeanImpl)
@@ -627,7 +627,7 @@ public class SummaryStatistics implements StatisticalSummary, Serializable {
* </p>
* @param meanImpl the StorelessUnivariateStatistic instance to use for
* computing the mean
- * @throws MathIllegalStateException if data has already been added (i.e if n > 0)
+ * @throws MathIllegalStateException if data has already been added (i.e if n > 0)
* @since 1.2
*/
public void setMeanImpl(StorelessUnivariateStatistic meanImpl)
@@ -657,7 +657,7 @@ public class SummaryStatistics implements StatisticalSummary, Serializable {
* </p>
* @param varianceImpl the StorelessUnivariateStatistic instance to use for
* computing the variance
- * @throws MathIllegalStateException if data has already been added (i.e if n > 0)
+ * @throws MathIllegalStateException if data has already been added (i.e if n > 0)
* @since 1.2
*/
public void setVarianceImpl(StorelessUnivariateStatistic varianceImpl)
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/descriptive/moment/FirstMoment.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/descriptive/moment/FirstMoment.java b/src/main/java/org/apache/commons/math4/stat/descriptive/moment/FirstMoment.java
index 50b107f..e02a7cb 100644
--- a/src/main/java/org/apache/commons/math4/stat/descriptive/moment/FirstMoment.java
+++ b/src/main/java/org/apache/commons/math4/stat/descriptive/moment/FirstMoment.java
@@ -31,12 +31,11 @@ import org.apache.commons.math4.util.MathUtils;
* <p>
* To limit numeric errors, the value of the statistic is computed using the
* following recursive updating algorithm: </p>
- * <p>
* <ol>
* <li>Initialize <code>m = </code> the first value</li>
* <li>For each additional value, update using <br>
* <code>m = m + (new value - m) / (number of observations)</code></li>
- * </ol></p>
+ * </ol>
* <p>
* Returns <code>Double.NaN</code> if the dataset is empty. Note that
* Double.NaN may also be returned if the input includes NaN and / or infinite
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/descriptive/moment/GeometricMean.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/descriptive/moment/GeometricMean.java b/src/main/java/org/apache/commons/math4/stat/descriptive/moment/GeometricMean.java
index 938930b..aaeffe6 100644
--- a/src/main/java/org/apache/commons/math4/stat/descriptive/moment/GeometricMean.java
+++ b/src/main/java/org/apache/commons/math4/stat/descriptive/moment/GeometricMean.java
@@ -42,7 +42,7 @@ import org.apache.commons.math4.util.MathUtils;
* <li>If both <code>Double.POSITIVE_INFINITY</code> and
* <code>Double.NEGATIVE_INFINITY</code> are among the values, the result is
* <code>NaN.</code></li>
- * </ul> </p>
+ * </ul>
* <p>
* <strong>Note that this implementation is not synchronized.</strong> If
* multiple threads access an instance of this class concurrently, and at least
@@ -162,7 +162,7 @@ public class GeometricMean extends AbstractStorelessUnivariateStatistic implemen
* @param sumLogImpl the StorelessUnivariateStatistic instance to use
* for computing the log sum
* @throws MathIllegalStateException if data has already been added
- * (i.e if n > 0)
+ * (i.e if n > 0)
*/
public void setSumLogImpl(StorelessUnivariateStatistic sumLogImpl)
throws MathIllegalStateException {
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/descriptive/moment/Mean.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/descriptive/moment/Mean.java b/src/main/java/org/apache/commons/math4/stat/descriptive/moment/Mean.java
index 4a23b0e..2503039 100644
--- a/src/main/java/org/apache/commons/math4/stat/descriptive/moment/Mean.java
+++ b/src/main/java/org/apache/commons/math4/stat/descriptive/moment/Mean.java
@@ -199,7 +199,7 @@ public class Mean extends AbstractStorelessUnivariateStatistic
* <li>the weights array contains one or more NaN values</li>
* <li>the weights array contains negative values</li>
* <li>the start and length arguments do not determine a valid array</li>
- * </ul></p>
+ * </ul>
*
* @param values the input array
* @param weights the weights array
@@ -245,7 +245,7 @@ public class Mean extends AbstractStorelessUnivariateStatistic
* <li>the weights array contains one or more infinite values</li>
* <li>the weights array contains one or more NaN values</li>
* <li>the weights array contains negative values</li>
- * </ul></p>
+ * </ul>
*
* @param values the input array
* @param weights the weights array
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/descriptive/moment/SecondMoment.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/descriptive/moment/SecondMoment.java b/src/main/java/org/apache/commons/math4/stat/descriptive/moment/SecondMoment.java
index 6cab875..878ebe4 100644
--- a/src/main/java/org/apache/commons/math4/stat/descriptive/moment/SecondMoment.java
+++ b/src/main/java/org/apache/commons/math4/stat/descriptive/moment/SecondMoment.java
@@ -31,7 +31,7 @@ import org.apache.commons.math4.util.MathUtils;
* <li> dev = (current obs - previous mean) </li>
* <li> n = number of observations (including current obs) </li>
* </ul>
- * Then</p>
+ * Then
* <p>
* new value = old value + dev^2 * (n -1) / n.</p>
* <p>
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/descriptive/moment/SemiVariance.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/descriptive/moment/SemiVariance.java b/src/main/java/org/apache/commons/math4/stat/descriptive/moment/SemiVariance.java
index 2c8f840..b1427b8 100644
--- a/src/main/java/org/apache/commons/math4/stat/descriptive/moment/SemiVariance.java
+++ b/src/main/java/org/apache/commons/math4/stat/descriptive/moment/SemiVariance.java
@@ -28,8 +28,8 @@ import org.apache.commons.math4.util.MathUtils;
/**
* <p>Computes the semivariance of a set of values with respect to a given cutoff value.
* We define the <i>downside semivariance</i> of a set of values <code>x</code>
- * against the <i>cutoff value</i> <code>cutoff</code> to be <br/>
- * <code>Σ (x[i] - target)<sup>2</sup> / df</code> <br/>
+ * against the <i>cutoff value</i> <code>cutoff</code> to be <br>
+ * <code>Σ (x[i] - target)<sup>2</sup> / df</code> <br>
* where the sum is taken over all <code>i</code> such that <code>x[i] < cutoff</code>
* and <code>df</code> is the length of <code>x</code> (non-bias-corrected) or
* one less than this number (bias corrected). The <i>upside semivariance</i>
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/descriptive/moment/Variance.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/descriptive/moment/Variance.java b/src/main/java/org/apache/commons/math4/stat/descriptive/moment/Variance.java
index d88f952..7ed06bc 100644
--- a/src/main/java/org/apache/commons/math4/stat/descriptive/moment/Variance.java
+++ b/src/main/java/org/apache/commons/math4/stat/descriptive/moment/Variance.java
@@ -52,7 +52,7 @@ import org.apache.commons.math4.util.MathUtils;
* <code>incrementAll</code> and then executing <code>getResult</code> will
* sometimes give a different, less accurate, result than executing
* <code>evaluate</code> with the full array of values. The former approach
- * should only be used when the full array of values is not available.</p>
+ * should only be used when the full array of values is not available.
* <p>
* The "population variance" ( sum((x_i - mean)^2) / n ) can also
* be computed using this statistic. The <code>isBiasCorrected</code>
@@ -299,7 +299,7 @@ public class Variance extends AbstractStorelessUnivariateStatistic implements Se
* <li>the weights array contains one or more NaN values</li>
* <li>the weights array contains negative values</li>
* <li>the start and length arguments do not determine a valid array</li>
- * </ul></p>
+ * </ul>
* <p>
* Does not change the internal state of the statistic.</p>
* <p>
@@ -335,10 +335,10 @@ public class Variance extends AbstractStorelessUnivariateStatistic implements Se
* <p>
* Returns the weighted variance of the entries in the the input array.</p>
* <p>
- * Uses the formula <pre>
+ * Uses the formula <div style="white-space:pre"><code>
* Σ(weights[i]*(values[i] - weightedMean)<sup>2</sup>)/(Σ(weights[i]) - 1)
- * </pre>
- * where weightedMean is the weighted mean</p>
+ * </code></div>
+ * where weightedMean is the weighted mean
* <p>
* This formula will not return the same result as the unweighted variance when all
* weights are equal, unless all weights are equal to 1. The formula assumes that
@@ -357,7 +357,7 @@ public class Variance extends AbstractStorelessUnivariateStatistic implements Se
* <li>the weights array contains one or more infinite values</li>
* <li>the weights array contains one or more NaN values</li>
* <li>the weights array contains negative values</li>
- * </ul></p>
+ * </ul>
* <p>
* Does not change the internal state of the statistic.</p>
* <p>
@@ -463,9 +463,9 @@ public class Variance extends AbstractStorelessUnivariateStatistic implements Se
* the input array, using the precomputed weighted mean value. Returns
* <code>Double.NaN</code> if the designated subarray is empty.
* <p>
- * Uses the formula <pre>
+ * Uses the formula <div style="white-space:pre"><code>
* Σ(weights[i]*(values[i] - mean)<sup>2</sup>)/(Σ(weights[i]) - 1)
- * </pre></p>
+ * </code></div>
* <p>
* The formula used assumes that the supplied mean value is the weighted arithmetic
* mean of the sample data, not a known population parameter. This method
@@ -490,7 +490,7 @@ public class Variance extends AbstractStorelessUnivariateStatistic implements Se
* <li>the weights array contains one or more NaN values</li>
* <li>the weights array contains negative values</li>
* <li>the start and length arguments do not determine a valid array</li>
- * </ul></p>
+ * </ul>
* <p>
* Does not change the internal state of the statistic.</p>
*
@@ -541,9 +541,9 @@ public class Variance extends AbstractStorelessUnivariateStatistic implements Se
* <p>Returns the weighted variance of the values in the input array, using
* the precomputed weighted mean value.</p>
* <p>
- * Uses the formula <pre>
+ * Uses the formula <div style="white-space:pre"><code>
* Σ(weights[i]*(values[i] - mean)<sup>2</sup>)/(Σ(weights[i]) - 1)
- * </pre></p>
+ * </code></div>
* <p>
* The formula used assumes that the supplied mean value is the weighted arithmetic
* mean of the sample data, not a known population parameter. This method
@@ -567,7 +567,7 @@ public class Variance extends AbstractStorelessUnivariateStatistic implements Se
* <li>the weights array contains one or more infinite values</li>
* <li>the weights array contains one or more NaN values</li>
* <li>the weights array contains negative values</li>
- * </ul></p>
+ * </ul>
* <p>
* Does not change the internal state of the statistic.</p>
*
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/descriptive/package-info.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/descriptive/package-info.java b/src/main/java/org/apache/commons/math4/stat/descriptive/package-info.java
index dd73f99..37397a4 100644
--- a/src/main/java/org/apache/commons/math4/stat/descriptive/package-info.java
+++ b/src/main/java/org/apache/commons/math4/stat/descriptive/package-info.java
@@ -21,22 +21,22 @@
* <h3>UnivariateStatistic API Usage Examples:</h3>
*
* <h4>UnivariateStatistic:</h4>
- * <code>/∗ evaluation approach ∗/<br/>
- * double[] values = new double[] { 1, 2, 3, 4, 5 };<br/>
- * <span style="font-weight: bold;">UnivariateStatistic stat = new Mean();</span><br/>
- * out.println("mean = " + <span style="font-weight: bold;">stat.evaluate(values)</span>);<br/>
+ * <code>/∗ evaluation approach ∗/<br>
+ * double[] values = new double[] { 1, 2, 3, 4, 5 };<br>
+ * <span style="font-weight: bold;">UnivariateStatistic stat = new Mean();</span><br>
+ * out.println("mean = " + <span style="font-weight: bold;">stat.evaluate(values)</span>);<br>
* </code>
*
* <h4>StorelessUnivariateStatistic:</h4>
- * <code>/∗ incremental approach ∗/<br/>
- * double[] values = new double[] { 1, 2, 3, 4, 5 };<br/>
- * <span style="font-weight: bold;">StorelessUnivariateStatistic stat = new Mean();</span><br/>
- * out.println("mean before adding a value is NaN = " + <span style="font-weight: bold;">stat.getResult()</span>);<br/>
- * for (int i = 0; i < values.length; i++) {<br/>
- * <span style="font-weight: bold;">stat.increment(values[i]);</span><br/>
- * out.println("current mean = " + <span style="font-weight: bold;">stat2.getResult()</span>);<br/>
- * }<br/>
- * <span style="font-weight: bold;"> stat.clear();</span><br/>
+ * <code>/∗ incremental approach ∗/<br>
+ * double[] values = new double[] { 1, 2, 3, 4, 5 };<br>
+ * <span style="font-weight: bold;">StorelessUnivariateStatistic stat = new Mean();</span><br>
+ * out.println("mean before adding a value is NaN = " + <span style="font-weight: bold;">stat.getResult()</span>);<br>
+ * for (int i = 0; i < values.length; i++) {<br>
+ * <span style="font-weight: bold;">stat.increment(values[i]);</span><br>
+ * out.println("current mean = " + <span style="font-weight: bold;">stat2.getResult()</span>);<br>
+ * }<br>
+ * <span style="font-weight: bold;"> stat.clear();</span><br>
* out.println("mean after clear is NaN = " + <span style="font-weight: bold;">stat.getResult()</span>);
* </code>
*
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/descriptive/rank/Max.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/descriptive/rank/Max.java b/src/main/java/org/apache/commons/math4/stat/descriptive/rank/Max.java
index 502d2d4..1615003 100644
--- a/src/main/java/org/apache/commons/math4/stat/descriptive/rank/Max.java
+++ b/src/main/java/org/apache/commons/math4/stat/descriptive/rank/Max.java
@@ -26,13 +26,12 @@ import org.apache.commons.math4.util.MathUtils;
/**
* Returns the maximum of the available values.
- * <p>
* <ul>
* <li>The result is <code>NaN</code> iff all values are <code>NaN</code>
* (i.e. <code>NaN</code> values have no impact on the value of the statistic).</li>
* <li>If any of the values equals <code>Double.POSITIVE_INFINITY</code>,
* the result is <code>Double.POSITIVE_INFINITY.</code></li>
- * </ul></p>
+ * </ul>
* <p>
* <strong>Note that this implementation is not synchronized.</strong> If
* multiple threads access an instance of this class concurrently, and at least
@@ -112,13 +111,12 @@ public class Max extends AbstractStorelessUnivariateStatistic implements Seriali
* <p>
* Throws <code>MathIllegalArgumentException</code> if the array is null or
* the array index parameters are not valid.</p>
- * <p>
* <ul>
* <li>The result is <code>NaN</code> iff all values are <code>NaN</code>
* (i.e. <code>NaN</code> values have no impact on the value of the statistic).</li>
* <li>If any of the values equals <code>Double.POSITIVE_INFINITY</code>,
* the result is <code>Double.POSITIVE_INFINITY.</code></li>
- * </ul></p>
+ * </ul>
*
* @param values the input array
* @param begin index of the first array element to include
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/descriptive/rank/Min.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/descriptive/rank/Min.java b/src/main/java/org/apache/commons/math4/stat/descriptive/rank/Min.java
index 72eaef4..479a1f7 100644
--- a/src/main/java/org/apache/commons/math4/stat/descriptive/rank/Min.java
+++ b/src/main/java/org/apache/commons/math4/stat/descriptive/rank/Min.java
@@ -26,13 +26,12 @@ import org.apache.commons.math4.util.MathUtils;
/**
* Returns the minimum of the available values.
- * <p>
* <ul>
* <li>The result is <code>NaN</code> iff all values are <code>NaN</code>
* (i.e. <code>NaN</code> values have no impact on the value of the statistic).</li>
* <li>If any of the values equals <code>Double.NEGATIVE_INFINITY</code>,
* the result is <code>Double.NEGATIVE_INFINITY.</code></li>
- * </ul></p>
+ * </ul>
* <p>
* <strong>Note that this implementation is not synchronized.</strong> If
* multiple threads access an instance of this class concurrently, and at least
@@ -112,13 +111,12 @@ public class Min extends AbstractStorelessUnivariateStatistic implements Seriali
* <p>
* Throws <code>MathIllegalArgumentException</code> if the array is null or
* the array index parameters are not valid.</p>
- * <p>
* <ul>
* <li>The result is <code>NaN</code> iff all values are <code>NaN</code>
* (i.e. <code>NaN</code> values have no impact on the value of the statistic).</li>
* <li>If any of the values equals <code>Double.NEGATIVE_INFINITY</code>,
* the result is <code>Double.NEGATIVE_INFINITY.</code></li>
- * </ul> </p>
+ * </ul>
*
* @param values the input array
* @param begin index of the first array element to include
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/descriptive/rank/PSquarePercentile.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/descriptive/rank/PSquarePercentile.java b/src/main/java/org/apache/commons/math4/stat/descriptive/rank/PSquarePercentile.java
index 7f3e54d..b6bdce5 100644
--- a/src/main/java/org/apache/commons/math4/stat/descriptive/rank/PSquarePercentile.java
+++ b/src/main/java/org/apache/commons/math4/stat/descriptive/rank/PSquarePercentile.java
@@ -41,7 +41,7 @@ import org.apache.commons.numbers.core.Precision;
/**
* A {@link StorelessUnivariateStatistic} estimating percentiles using the
- * <ahref=http://www.cs.wustl.edu/~jain/papers/ftp/psqr.pdf>P<SUP>2</SUP></a>
+ * <a href=http://www.cs.wustl.edu/~jain/papers/ftp/psqr.pdf>P<SUP>2</SUP></a>
* Algorithm as explained by <a href=http://www.cse.wustl.edu/~jain/>Raj
* Jain</a> and Imrich Chlamtac in
* <a href=http://www.cse.wustl.edu/~jain/papers/psqr.htm>P<SUP>2</SUP> Algorithm
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/descriptive/rank/Percentile.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/descriptive/rank/Percentile.java b/src/main/java/org/apache/commons/math4/stat/descriptive/rank/Percentile.java
index a85fb63..c05d782 100644
--- a/src/main/java/org/apache/commons/math4/stat/descriptive/rank/Percentile.java
+++ b/src/main/java/org/apache/commons/math4/stat/descriptive/rank/Percentile.java
@@ -43,20 +43,20 @@ import org.apache.commons.numbers.core.Precision;
* significantly different results. The algorithm implemented here works as follows:
* <ol>
* <li>Let <code>n</code> be the length of the (sorted) array and
- * <code>0 < p <= 100</code> be the desired percentile.</li>
+ * <code>0 < p <= 100</code> be the desired percentile.</li>
* <li>If <code> n = 1 </code> return the unique array element (regardless of
* the value of <code>p</code>); otherwise </li>
* <li>Compute the estimated percentile position
* <code> pos = p * (n + 1) / 100</code> and the difference, <code>d</code>
* between <code>pos</code> and <code>floor(pos)</code> (i.e. the fractional
* part of <code>pos</code>).</li>
- * <li> If <code>pos < 1</code> return the smallest element in the array.</li>
- * <li> Else if <code>pos >= n</code> return the largest element in the array.</li>
+ * <li> If <code>pos < 1</code> return the smallest element in the array.</li>
+ * <li> Else if <code>pos >= n</code> return the largest element in the array.</li>
* <li> Else let <code>lower</code> be the element in position
* <code>floor(pos)</code> in the array and let <code>upper</code> be the
* next element in the array. Return <code>lower + d * (upper - lower)</code>
* </li>
- * </ol></p>
+ * </ol>
* <p>
* To compute percentiles, the data must be at least partially ordered. Input
* arrays are copied and recursively partitioned using an ordering definition.
@@ -247,7 +247,6 @@ public class Percentile extends AbstractUnivariateStatistic implements Serializa
* <p>
* Calls to this method do not modify the internal <code>quantile</code>
* state of this statistic.</p>
- * <p>
* <ul>
* <li>Returns <code>Double.NaN</code> if <code>values</code> has length
* <code>0</code></li>
@@ -256,7 +255,7 @@ public class Percentile extends AbstractUnivariateStatistic implements Serializa
* <li>Throws <code>MathIllegalArgumentException</code> if <code>values</code>
* is null or p is not a valid quantile value (p must be greater than 0
* and less than or equal to 100) </li>
- * </ul></p>
+ * </ul>
* <p>
* See {@link Percentile} for a description of the percentile estimation
* algorithm used.</p>
@@ -277,14 +276,13 @@ public class Percentile extends AbstractUnivariateStatistic implements Serializa
* Returns an estimate of the <code>quantile</code>th percentile of the
* designated values in the <code>values</code> array. The quantile
* estimated is determined by the <code>quantile</code> property.
- * <p>
* <ul>
* <li>Returns <code>Double.NaN</code> if <code>length = 0</code></li>
* <li>Returns (for any value of <code>quantile</code>)
* <code>values[begin]</code> if <code>length = 1 </code></li>
* <li>Throws <code>MathIllegalArgumentException</code> if <code>values</code>
* is null, or <code>start</code> or <code>length</code> is invalid</li>
- * </ul></p>
+ * </ul>
* <p>
* See {@link Percentile} for a description of the percentile estimation
* algorithm used.</p>
@@ -310,7 +308,6 @@ public class Percentile extends AbstractUnivariateStatistic implements Serializa
* <p>
* Calls to this method do not modify the internal <code>quantile</code>
* state of this statistic.</p>
- * <p>
* <ul>
* <li>Returns <code>Double.NaN</code> if <code>length = 0</code></li>
* <li>Returns (for any value of <code>p</code>) <code>values[begin]</code>
@@ -319,7 +316,7 @@ public class Percentile extends AbstractUnivariateStatistic implements Serializa
* is null , <code>begin</code> or <code>length</code> is invalid, or
* <code>p</code> is not a valid quantile value (p must be greater than 0
* and less than or equal to 100)</li>
- * </ul></p>
+ * </ul>
* <p>
* See {@link Percentile} for a description of the percentile estimation
* algorithm used.</p>
@@ -367,7 +364,7 @@ public class Percentile extends AbstractUnivariateStatistic implements Serializa
* Sets the value of the quantile field (determines what percentile is
* computed when evaluate() is called with no quantile argument).
*
- * @param p a value between 0 < p <= 100
+ * @param p a value between 0 < p <= 100
* @throws MathIllegalArgumentException if p is not greater than 0 and less
* than or equal to 100
*/
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/descriptive/summary/Product.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/descriptive/summary/Product.java b/src/main/java/org/apache/commons/math4/stat/descriptive/summary/Product.java
index 5be6ce9..58609f8 100644
--- a/src/main/java/org/apache/commons/math4/stat/descriptive/summary/Product.java
+++ b/src/main/java/org/apache/commons/math4/stat/descriptive/summary/Product.java
@@ -144,7 +144,7 @@ public class Product extends AbstractStorelessUnivariateStatistic implements Ser
* <li>the weights array contains one or more NaN values</li>
* <li>the weights array contains negative values</li>
* <li>the start and length arguments do not determine a valid array</li>
- * </ul></p>
+ * </ul>
*
* <p>Uses the formula, <pre>
* weighted product = ∏values[i]<sup>weights[i]</sup>
@@ -182,7 +182,7 @@ public class Product extends AbstractStorelessUnivariateStatistic implements Ser
* <li>the weights array contains one or more infinite values</li>
* <li>the weights array contains one or more NaN values</li>
* <li>the weights array contains negative values</li>
- * </ul></p>
+ * </ul>
*
* <p>Uses the formula,
* <pre>
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/descriptive/summary/Sum.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/descriptive/summary/Sum.java b/src/main/java/org/apache/commons/math4/stat/descriptive/summary/Sum.java
index bbe29fa..e112b5e 100644
--- a/src/main/java/org/apache/commons/math4/stat/descriptive/summary/Sum.java
+++ b/src/main/java/org/apache/commons/math4/stat/descriptive/summary/Sum.java
@@ -143,11 +143,11 @@ public class Sum extends AbstractStorelessUnivariateStatistic implements Seriali
* <li>the weights array contains one or more NaN values</li>
* <li>the weights array contains negative values</li>
* <li>the start and length arguments do not determine a valid array</li>
- * </ul></p>
+ * </ul>
* <p>
* Uses the formula, <pre>
* weighted sum = Σ(values[i] * weights[i])
- * </pre></p>
+ * </pre>
*
* @param values the input array
* @param weights the weights array
@@ -179,11 +179,11 @@ public class Sum extends AbstractStorelessUnivariateStatistic implements Seriali
* <li>the weights array contains one or more infinite values</li>
* <li>the weights array contains one or more NaN values</li>
* <li>the weights array contains negative values</li>
- * </ul></p>
+ * </ul>
* <p>
* Uses the formula, <pre>
* weighted sum = Σ(values[i] * weights[i])
- * </pre></p>
+ * </pre>
*
* @param values the input array
* @param weights the weights array
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/descriptive/summary/SumOfLogs.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/descriptive/summary/SumOfLogs.java b/src/main/java/org/apache/commons/math4/stat/descriptive/summary/SumOfLogs.java
index fcb00b9..75e7547 100644
--- a/src/main/java/org/apache/commons/math4/stat/descriptive/summary/SumOfLogs.java
+++ b/src/main/java/org/apache/commons/math4/stat/descriptive/summary/SumOfLogs.java
@@ -38,7 +38,7 @@ import org.apache.commons.math4.util.MathUtils;
* <li>If both <code>Double.POSITIVE_INFINITY</code> and
* <code>Double.NEGATIVE_INFINITY</code> are among the values, the result is
* <code>NaN.</code></li>
- * </ul></p>
+ * </ul>
* <p>
* <strong>Note that this implementation is not synchronized.</strong> If
* multiple threads access an instance of this class concurrently, and at least
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/inference/BinomialTest.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/inference/BinomialTest.java b/src/main/java/org/apache/commons/math4/stat/inference/BinomialTest.java
index d500311..2846c68 100644
--- a/src/main/java/org/apache/commons/math4/stat/inference/BinomialTest.java
+++ b/src/main/java/org/apache/commons/math4/stat/inference/BinomialTest.java
@@ -86,7 +86,7 @@ public class BinomialTest {
* <li>Number of successes must be ≥ 0.</li>
* <li>Number of successes must be ≤ number of trials.</li>
* <li>Probability must be ≥ 0 and ≤ 1.</li>
- * </ul></p>
+ * </ul>
*
* @param numberOfTrials number of trials performed
* @param numberOfSuccesses number of successes observed
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/inference/ChiSquareTest.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/inference/ChiSquareTest.java b/src/main/java/org/apache/commons/math4/stat/inference/ChiSquareTest.java
index add6c01..3836064 100644
--- a/src/main/java/org/apache/commons/math4/stat/inference/ChiSquareTest.java
+++ b/src/main/java/org/apache/commons/math4/stat/inference/ChiSquareTest.java
@@ -62,7 +62,7 @@ public class ChiSquareTest {
* </li>
* <li>The observed and expected arrays must have the same length and
* their common length must be at least 2.
- * </li></ul></p><p>
+ * </li></ul><p>
* If any of the preconditions are not met, an
* <code>IllegalArgumentException</code> is thrown.</p>
* <p><strong>Note: </strong>This implementation rescales the
@@ -135,7 +135,7 @@ public class ChiSquareTest {
* </li>
* <li>The observed and expected arrays must have the same length and
* their common length must be at least 2.
- * </li></ul></p><p>
+ * </li></ul><p>
* If any of the preconditions are not met, an
* <code>IllegalArgumentException</code> is thrown.</p>
* <p><strong>Note: </strong>This implementation rescales the
@@ -181,7 +181,7 @@ public class ChiSquareTest {
* <li>The observed and expected arrays must have the same length and
* their common length must be at least 2.
* <li> <code> 0 < alpha < 0.5 </code>
- * </li></ul></p><p>
+ * </li></ul><p>
* If any of the preconditions are not met, an
* <code>IllegalArgumentException</code> is thrown.</p>
* <p><strong>Note: </strong>This implementation rescales the
@@ -231,7 +231,7 @@ public class ChiSquareTest {
* <li>The 2-way table represented by <code>counts</code> must have at
* least 2 columns and at least 2 rows.
* </li>
- * </li></ul></p><p>
+ * </ul><p>
* If any of the preconditions are not met, an
* <code>IllegalArgumentException</code> is thrown.</p>
*
@@ -295,7 +295,7 @@ public class ChiSquareTest {
* <li>The 2-way table represented by <code>counts</code> must have at least 2
* columns and at least 2 rows.
* </li>
- * </li></ul></p><p>
+ * </ul><p>
* If any of the preconditions are not met, an
* <code>IllegalArgumentException</code> is thrown.</p>
*
@@ -342,7 +342,7 @@ public class ChiSquareTest {
* same length).</li>
* <li>The 2-way table represented by <code>counts</code> must have at least 2 columns and
* at least 2 rows.</li>
- * </li></ul></p><p>
+ * </ul><p>
* If any of the preconditions are not met, an
* <code>IllegalArgumentException</code> is thrown.</p>
*
@@ -378,8 +378,8 @@ public class ChiSquareTest {
* <code>
* ∑[(K * observed1[i] - observed2[i]/K)<sup>2</sup> / (observed1[i] + observed2[i])]
* </code> where
- * <br/><code>K = &sqrt;[&sum(observed2 / ∑(observed1)]</code>
- * </p>
+ * <br><code>K = √[∑(observed2 / ∑(observed1)]</code>
+ *
* <p>This statistic can be used to perform a Chi-Square test evaluating the
* null hypothesis that both observed counts follow the same distribution.</p>
* <p>
@@ -392,7 +392,7 @@ public class ChiSquareTest {
* </li>
* <li>The arrays <code>observed1</code> and <code>observed2</code> must have
* the same length and their common length must be at least 2.
- * </li></ul></p><p>
+ * </li></ul><p>
* If any of the preconditions are not met, an
* <code>IllegalArgumentException</code> is thrown.</p>
*
@@ -538,7 +538,7 @@ public class ChiSquareTest {
* <li>The arrays <code>observed1</code> and <code>observed2</code> must
* have the same length and their common length must be at least 2.
* </li>
- * <li> <code> 0 < alpha < 0.5 </code>
+ * <li> <code> 0 < alpha < 0.5 </code>
* </li></ul><p>
* If any of the preconditions are not met, an
* <code>IllegalArgumentException</code> is thrown.</p>
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/inference/GTest.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/inference/GTest.java b/src/main/java/org/apache/commons/math4/stat/inference/GTest.java
index 0ee009d..0da2043 100644
--- a/src/main/java/org/apache/commons/math4/stat/inference/GTest.java
+++ b/src/main/java/org/apache/commons/math4/stat/inference/GTest.java
@@ -55,7 +55,7 @@ public class GTest {
* <li>Expected counts must all be positive. </li>
* <li>Observed counts must all be ≥ 0. </li>
* <li>The observed and expected arrays must have the same length and their
- * common length must be at least 2. </li></ul></p>
+ * common length must be at least 2. </li></ul>
*
* <p>If any of the preconditions are not met, a
* {@code MathIllegalArgumentException} is thrown.</p>
@@ -111,7 +111,7 @@ public class GTest {
/**
* Returns the <i>observed significance level</i>, or <a href=
* "http://www.cas.lancs.ac.uk/glossary_v1.1/hyptest.html#pvalue"> p-value</a>,
- * associated with a G-Test for goodness of fit</a> comparing the
+ * associated with a G-Test for goodness of fit comparing the
* {@code observed} frequency counts to those in the {@code expected} array.
*
* <p>The number returned is the smallest significance level at which one
@@ -128,7 +128,7 @@ public class GTest {
* <li>Observed counts must all be ≥ 0. </li>
* <li>The observed and expected arrays must have the
* same length and their common length must be at least 2.</li>
- * </ul></p>
+ * </ul>
*
* <p>If any of the preconditions are not met, a
* {@code MathIllegalArgumentException} is thrown.</p>
@@ -202,14 +202,14 @@ public class GTest {
* {@code gTest(expected, observed, 0.01)}</p>
*
* <p>Returns true iff {@link #gTest(double[], long[])
- * gTestGoodnessOfFitPValue(expected, observed)} < alpha</p>
+ * gTestGoodnessOfFitPValue(expected, observed)} < alpha</p>
*
* <p><strong>Preconditions</strong>: <ul>
* <li>Expected counts must all be positive. </li>
* <li>Observed counts must all be ≥ 0. </li>
* <li>The observed and expected arrays must have the same length and their
* common length must be at least 2.
- * <li> {@code 0 < alpha < 0.5} </li></ul></p>
+ * <li> {@code 0 < alpha < 0.5} </li></ul>
*
* <p>If any of the preconditions are not met, a
* {@code MathIllegalArgumentException} is thrown.</p>
@@ -279,8 +279,8 @@ public class GTest {
/**
* Calculates the <a href="http://en.wikipedia.org/wiki/Entropy_%28information_theory%29">
* Shannon entropy</a> for a vector. The values of {@code k} are taken to be
- * incidence counts of the values of a random variable. What is returned is <br/>
- * ∑p<sub>i</sub>log(p<sub>i</sub><br/>
+ * incidence counts of the values of a random variable. What is returned is <br>
+ * ∑p<sub>i</sub>log(p<sub>i</sub><br>
* where p<sub>i</sub> = k[i] / (sum of elements in k)
*
* @param k Vector (for ex. Row Sums of a trials)
@@ -314,8 +314,8 @@ public class GTest {
* <p> where {@code H} is the
* <a href="http://en.wikipedia.org/wiki/Entropy_%28information_theory%29">
* Shannon Entropy</a> of the random variable formed by viewing the elements
- * of the argument array as incidence counts; <br/>
- * {@code k} is a matrix with rows {@code [observed1, observed2]}; <br/>
+ * of the argument array as incidence counts; <br>
+ * {@code k} is a matrix with rows {@code [observed1, observed2]}; <br>
* {@code rowSums, colSums} are the row/col sums of {@code k}; <br>
* and {@code totalSum} is the overall sum of all entries in {@code k}.</p>
*
@@ -327,7 +327,7 @@ public class GTest {
* <li>Observed counts for a specific bin must not both be zero. </li>
* <li>Observed counts for a specific sample must not all be 0. </li>
* <li>The arrays {@code observed1} and {@code observed2} must have
- * the same length and their common length must be at least 2. </li></ul></p>
+ * the same length and their common length must be at least 2. </li></ul>
*
* <p>If any of the preconditions are not met, a
* {@code MathIllegalArgumentException} is thrown.</p>
@@ -398,7 +398,7 @@ public class GTest {
*
* <p>{@code (sgn) sqrt(gValueDataSetsComparison({k11, k12}, {k21, k22})}</p>
*
- * <p>where {@code sgn} is -1 if {@code k11 / (k11 + k12) < k21 / (k21 + k22))};<br/>
+ * <p>where {@code sgn} is -1 if {@code k11 / (k11 + k12) < k21 / (k21 + k22))};<br>
* 1 otherwise.</p>
*
* <p>Signed root LLR has two advantages over the basic LLR: a) it is positive
@@ -450,7 +450,7 @@ public class GTest {
* <li>Observed counts for a specific sample must not all be 0. </li>
* <li>The arrays {@code observed1} and {@code observed2} must
* have the same length and their common length must be at least 2. </li>
- * </ul><p>
+ * </ul>
* <p> If any of the preconditions are not met, a
* {@code MathIllegalArgumentException} is thrown.</p>
*
@@ -500,7 +500,7 @@ public class GTest {
* <li>Observed counts for a specific sample must not all be 0. </li>
* <li>The arrays {@code observed1} and {@code observed2} must
* have the same length and their common length must be at least 2. </li>
- * <li>{@code 0 < alpha < 0.5} </li></ul></p>
+ * <li>{@code 0 < alpha < 0.5} </li></ul>
*
* <p>If any of the preconditions are not met, a
* {@code MathIllegalArgumentException} is thrown.</p>