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From pste...@apache.org
Subject svn commit: r617953 [3/3] - in /commons/proper/math/trunk/src/java/org/apache/commons/math: distribution/ fraction/ linear/ stat/ stat/descriptive/ stat/descriptive/moment/ stat/descriptive/rank/ stat/descriptive/summary/ stat/inference/ stat/regressio...
Date Sun, 03 Feb 2008 05:54:06 GMT
Modified: commons/proper/math/trunk/src/java/org/apache/commons/math/stat/regression/SimpleRegression.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/java/org/apache/commons/math/stat/regression/SimpleRegression.java?rev=617953&r1=617952&r2=617953&view=diff
==============================================================================
--- commons/proper/math/trunk/src/java/org/apache/commons/math/stat/regression/SimpleRegression.java
(original)
+++ commons/proper/math/trunk/src/java/org/apache/commons/math/stat/regression/SimpleRegression.java
Sat Feb  2 21:54:00 2008
@@ -26,15 +26,15 @@
  * Estimates an ordinary least squares regression model
  * with one independent variable.
  * <p>
- * <code> y = intercept + slope * x  </code>
+ * <code> y = intercept + slope * x  </code></p>
  * <p>
  * Standard errors for <code>intercept</code> and <code>slope</code>
are 
- * available as well as ANOVA, r-square and Pearson's r statistics.
+ * available as well as ANOVA, r-square and Pearson's r statistics.</p>
  * <p>
  * Observations (x,y pairs) can be added to the model one at a time or they 
  * can be provided in a 2-dimensional array.  The observations are not stored
  * in memory, so there is no limit to the number of observations that can be
- * added to the model. 
+ * added to the model.</p> 
  * <p>
  * <strong>Usage Notes</strong>: <ul>
  * <li> When there are fewer than two observations in the model, or when
@@ -48,7 +48,7 @@
  * and get updated statistics without using a new instance.  There is no 
  * "compute" method that updates all statistics.  Each of the getters performs
  * the necessary computations to return the requested statistic.</li>
- * </ul>
+ * </ul></p>
  *
  * @version $Revision$ $Date$
  */
@@ -111,7 +111,7 @@
      * "Algorithms for Computing the Sample Variance: Analysis and
      * Recommendations", Chan, T.F., Golub, G.H., and LeVeque, R.J. 
      * 1983, American Statistician, vol. 37, pp. 242-247, referenced in
-     * Weisberg, S. "Applied Linear Regression". 2nd Ed. 1985
+     * Weisberg, S. "Applied Linear Regression". 2nd Ed. 1985.</p>
      *
      *
      * @param x independent variable value
@@ -144,14 +144,14 @@
      * <code>data</code>.
      * <p>
      * <code>(data[0][0],data[0][1])</code> will be the first observation, then
-     * <code>(data[1][0],data[1][1])</code>, etc. 
+     * <code>(data[1][0],data[1][1])</code>, etc.</p>
      * <p> 
      * This method does not replace data that has already been added.  The
      * observations represented by <code>data</code> are added to the existing
-     * dataset.
+     * dataset.</p>
      * <p> 
      * To replace all data, use <code>clear()</code> before adding the new 
-     * data.
+     * data.</p>
      * 
      * @param data array of observations to be added
      */
@@ -187,14 +187,14 @@
      * supplied <code>x</code> value,  based on the data that has been
      * added to the model when this method is activated.
      * <p>
-     * <code> predict(x) = intercept + slope * x </code>
+     * <code> predict(x) = intercept + slope * x </code></p>
      * <p>
      * <strong>Preconditions</strong>: <ul>
      * <li>At least two observations (with at least two different x values)
      * 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>
+     * </li></ul></p>
      *
      * @param x input <code>x</code> value
      * @return predicted <code>y</code> value
@@ -209,14 +209,14 @@
      * <p>
      * The least squares estimate of the intercept is computed using the 
      * <a href="http://www.xycoon.com/estimation4.htm">normal equations</a>.
-     * The intercept is sometimes denoted b0. 
+     * The intercept is sometimes denoted b0.</p>
      * <p>
      * <strong>Preconditions</strong>: <ul>
      * <li>At least two observations (with at least two different x values)
      * 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>
+     * </li></ul></p>
      *
      * @return the intercept of the regression line
      */
@@ -229,14 +229,14 @@
     * <p>
     * The least squares estimate of the slope is computed using the 
     * <a href="http://www.xycoon.com/estimation4.htm">normal equations</a>.
-    * The slope is sometimes denoted b1. 
+    * The slope is sometimes denoted b1.</p>
     * <p>
     * <strong>Preconditions</strong>: <ul>
     * <li>At least two observations (with at least two different x values)
     * 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>
+    * </li></ul></p>
     *
     * @return the slope of the regression line
     */
@@ -255,27 +255,27 @@
      * sum of squared errors</a> (SSE) associated with the regression 
      * model.
      * <p>
-     * The sum is computed using the computational formula
+     * The sum is computed using the computational formula</p>
      * <p>
-     * <code>SSE = SYY - (SXY * SXY / SXX)</code>
+     * <code>SSE = SYY - (SXY * SXY / SXX)</code></p>
      * <p>
      * where <code>SYY</code> is the sum of the squared deviations of the y
      * values about their mean, <code>SXX</code> is similarly defined and
      * <code>SXY</code> is the sum of the products of x and y mean deviations.
-     * <p>
+     * </p><p>
      * The sums are accumulated using the updating algorithm referenced in 
-     * {@link #addData}.  
+     * {@link #addData}.</p>
      * <p>
      * The return value is constrained to be non-negative - i.e., if due to 
      * rounding errors the computational formula returns a negative result, 
-     * 0 is returned.
+     * 0 is returned.</p>
      * <p>
      * <strong>Preconditions</strong>: <ul>
      * <li>At least two observations (with at least two different x values)
      * 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>
+     * </li></ul></p>
      *
      * @return sum of squared errors associated with the regression model
      */
@@ -287,9 +287,9 @@
      * Returns the sum of squared deviations of the y values about their mean.
      * <p>
      * This is defined as SSTO 
-     * <a href="http://www.xycoon.com/SumOfSquares.htm">here</a>.
+     * <a href="http://www.xycoon.com/SumOfSquares.htm">here</a>.</p>
      * <p>
-     * If <code>n < 2</code>, this returns <code>Double.NaN</code>.
+     * If <code>n < 2</code>, this returns <code>Double.NaN</code>.</p>
      *
      * @return sum of squared deviations of y values
      */
@@ -305,14 +305,14 @@
      * their mean (which equals the mean of y).
      * <p>
      * This is usually abbreviated SSR or SSM.  It is defined as SSM 
-     * <a href="http://www.xycoon.com/SumOfSquares.htm">here</a>
+     * <a href="http://www.xycoon.com/SumOfSquares.htm">here</a></p>
      * <p>
      * <strong>Preconditions</strong>: <ul>
      * <li>At least two observations (with at least two different x values)
      * 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>
+     * </li></ul></p>
      *
      * @return sum of squared deviations of predicted y values
      */
@@ -326,7 +326,7 @@
      * <p>
      * If there are fewer than <strong>three</strong> data pairs in the model,
      * or if there is no variation in <code>x</code>, this returns 
-     * <code>Double.NaN</code>.
+     * <code>Double.NaN</code>.</p>
      *
      * @return sum of squared deviations of y values
      */
@@ -347,7 +347,7 @@
      * 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>
+     * </li></ul></p>
      *
      * @return Pearson's r
      */
@@ -370,7 +370,7 @@
      * 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>
+     * </li></ul></p>
      *
      * @return r-square
      */
@@ -386,7 +386,7 @@
      * <p>
      * If there are fewer that <strong>three</strong> observations in the 
      * model, or if there is no variation in x, this returns 
-     * <code>Double.NaN</code>.
+     * <code>Double.NaN</code>.</p>
      *
      * @return standard error associated with intercept estimate
      */
@@ -402,7 +402,8 @@
      * <p>
      * If there are fewer that <strong>three</strong> data pairs in the model,
      * or if there is no variation in x, this returns <code>Double.NaN</code>.
-     *
+     * </p>
+     * 
      * @return standard error associated with slope estimate
      */
     public double getSlopeStdErr() {
@@ -413,23 +414,22 @@
      * Returns the half-width of a 95% confidence interval for the slope
      * estimate.
      * <p>
-     * The 95% confidence interval is 
+     * The 95% confidence interval is</p>
      * <p>
      * <code>(getSlope() - getSlopeConfidenceInterval(), 
-     * getSlope() + getSlopeConfidenceInterval())</code>
+     * getSlope() + getSlopeConfidenceInterval())</code></p>
      * <p>
      * If there are fewer that <strong>three</strong> observations in the 
      * model, or if there is no variation in x, this returns 
-     * <code>Double.NaN</code>.
+     * <code>Double.NaN</code>.</p>
      * <p>
      * <strong>Usage Note</strong>:<br>
      * The validity of this statistic depends on the assumption that the 
      * observations included in the model are drawn from a
      * <a href="http://mathworld.wolfram.com/BivariateNormalDistribution.html">
-     * Bivariate Normal Distribution</a>.
+     * Bivariate Normal Distribution</a>.</p>
      *
      * @return half-width of 95% confidence interval for the slope estimate
-     * 
      * @throws MathException if the confidence interval can not be computed.
      */
     public double getSlopeConfidenceInterval() throws MathException {
@@ -440,28 +440,28 @@
      * Returns the half-width of a (100-100*alpha)% confidence interval for 
      * the slope estimate.
      * <p>
-     * The (100-100*alpha)% confidence interval is 
+     * The (100-100*alpha)% confidence interval is </p>
      * <p>
      * <code>(getSlope() - getSlopeConfidenceInterval(), 
-     * getSlope() + getSlopeConfidenceInterval())</code>
+     * getSlope() + getSlopeConfidenceInterval())</code></p>
      * <p>
      * To request, for example, a 99% confidence interval, use 
-     * <code>alpha = .01</code>
+     * <code>alpha = .01</code></p>
      * <p>
      * <strong>Usage Note</strong>:<br>
      * The validity of this statistic depends on the assumption that the 
      * observations included in the model are drawn from a
      * <a href="http://mathworld.wolfram.com/BivariateNormalDistribution.html">
-     * Bivariate Normal Distribution</a>.
+     * Bivariate Normal Distribution</a>.</p>
      * <p>
      * <strong> Preconditions:</strong><ul>
      * <li>If there are fewer that <strong>three</strong> observations
in the 
      * model, or if there is no variation in x, this returns 
-     * <code>Double.NaN</code>. 
+     * <code>Double.NaN</code>.
      * </li>
      * <li><code>(0 < alpha < 1)</code>; otherwise an 
      * <code>IllegalArgumentException</code> is thrown.
-     * </li></ul>    
+     * </li></ul></p> 
      *
      * @param alpha the desired significance level 
      * @return half-width of 95% confidence interval for the slope estimate
@@ -483,16 +483,16 @@
      * such that the slope confidence interval with significance level
      * equal to <code>alpha</code> does not include <code>0</code>.
      * On regression output, this is often denoted <code>Prob(|t| > 0)</code>
-     * <p>
+     * </p><p>
      * <strong>Usage Note</strong>:<br>
      * The validity of this statistic depends on the assumption that the 
      * observations included in the model are drawn from a
      * <a href="http://mathworld.wolfram.com/BivariateNormalDistribution.html">
-     * Bivariate Normal Distribution</a>.
+     * Bivariate Normal Distribution</a>.</p>
      * <p>
      * If there are fewer that <strong>three</strong> observations in the 
      * model, or if there is no variation in x, this returns 
-     * <code>Double.NaN</code>.
+     * <code>Double.NaN</code>.</p>
      *
      * @return significance level for slope/correlation
      * @throws MathException if the significance level can not be computed.
@@ -507,7 +507,7 @@
     /**
     * Returns the intercept of the estimated regression line, given the slope.
     * <p>
-    * Will return <code>NaN</code> if slope is <code>NaN</code>.
+    * Will return <code>NaN</code> if slope is <code>NaN</code>.</p>
     *
     * @param slope current slope
     * @return the intercept of the regression line

Modified: commons/proper/math/trunk/src/java/org/apache/commons/math/transform/FastCosineTransformer.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/java/org/apache/commons/math/transform/FastCosineTransformer.java?rev=617953&r1=617952&r2=617953&view=diff
==============================================================================
--- commons/proper/math/trunk/src/java/org/apache/commons/math/transform/FastCosineTransformer.java
(original)
+++ commons/proper/math/trunk/src/java/org/apache/commons/math/transform/FastCosineTransformer.java
Sat Feb  2 21:54:00 2008
@@ -28,11 +28,11 @@
  * <b>Fast Fourier Transforms</b>, ISBN 0849371635, chapter 3.
  * <p>
  * FCT is its own inverse, up to a multiplier depending on conventions.
- * The equations are listed in the comments of the corresponding methods.
+ * The equations are listed in the comments of the corresponding methods.</p>
  * <p>
  * Different from FFT and FST, FCT requires the length of data set to be
  * power of 2 plus one. Users should especially pay attention to the
- * function transformation on how this affects the sampling.
+ * function transformation on how this affects the sampling.</p>
  *
  * @version $Revision$ $Date$
  */
@@ -53,7 +53,8 @@
      * <p>
      * The formula is $ F_n = (1/2) [f_0 + (-1)^n f_N] +
      *                        \Sigma_{k=0}^{N-1} f_k \cos(\pi nk/N) $
-     *
+     * </p>
+     * 
      * @param f the real data array to be transformed
      * @return the real transformed array
      * @throws MathException if any math-related errors occur
@@ -70,7 +71,8 @@
      * <p>
      * The formula is $ F_n = (1/2) [f_0 + (-1)^n f_N] +
      *                        \Sigma_{k=0}^{N-1} f_k \cos(\pi nk/N) $
-     *
+     * </p>
+     * 
      * @param f the function to be sampled and transformed
      * @param min the lower bound for the interval
      * @param max the upper bound for the interval
@@ -92,7 +94,8 @@
      * <p>
      * The formula is $ F_n = \sqrt{1/2N} [f_0 + (-1)^n f_N] +
      *                        \sqrt{2/N} \Sigma_{k=0}^{N-1} f_k \cos(\pi nk/N) $
-     *
+     * </p>
+     * 
      * @param f the real data array to be transformed
      * @return the real transformed array
      * @throws MathException if any math-related errors occur
@@ -111,6 +114,8 @@
      * The formula is $ F_n = \sqrt{1/2N} [f_0 + (-1)^n f_N] +
      *                        \sqrt{2/N} \Sigma_{k=0}^{N-1} f_k \cos(\pi nk/N) $
      *
+     * </p>
+     * 
      * @param f the function to be sampled and transformed
      * @param min the lower bound for the interval
      * @param max the upper bound for the interval
@@ -133,7 +138,8 @@
      * <p>
      * The formula is $ f_k = (1/N) [F_0 + (-1)^k F_N] +
      *                        (2/N) \Sigma_{n=0}^{N-1} F_n \cos(\pi nk/N) $
-     *
+     * </p>
+     * 
      * @param f the real data array to be inversely transformed
      * @return the real inversely transformed array
      * @throws MathException if any math-related errors occur
@@ -151,7 +157,8 @@
      * <p>
      * The formula is $ f_k = (1/N) [F_0 + (-1)^k F_N] +
      *                        (2/N) \Sigma_{n=0}^{N-1} F_n \cos(\pi nk/N) $
-     *
+     * </p>
+     * 
      * @param f the function to be sampled and inversely transformed
      * @param min the lower bound for the interval
      * @param max the upper bound for the interval
@@ -174,7 +181,8 @@
      * <p>
      * The formula is $ f_k = \sqrt{1/2N} [F_0 + (-1)^k F_N] +
      *                        \sqrt{2/N} \Sigma_{n=0}^{N-1} F_n \cos(\pi nk/N) $
-     *
+     * </p>
+     * 
      * @param f the real data array to be inversely transformed
      * @return the real inversely transformed array
      * @throws MathException if any math-related errors occur
@@ -191,7 +199,8 @@
      * <p>
      * The formula is $ f_k = \sqrt{1/2N} [F_0 + (-1)^k F_N] +
      *                        \sqrt{2/N} \Sigma_{n=0}^{N-1} F_n \cos(\pi nk/N) $
-     *
+     * </p>
+     * 
      * @param f the function to be sampled and inversely transformed
      * @param min the lower bound for the interval
      * @param max the upper bound for the interval

Modified: commons/proper/math/trunk/src/java/org/apache/commons/math/transform/FastFourierTransformer.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/java/org/apache/commons/math/transform/FastFourierTransformer.java?rev=617953&r1=617952&r2=617953&view=diff
==============================================================================
--- commons/proper/math/trunk/src/java/org/apache/commons/math/transform/FastFourierTransformer.java
(original)
+++ commons/proper/math/trunk/src/java/org/apache/commons/math/transform/FastFourierTransformer.java
Sat Feb  2 21:54:00 2008
@@ -29,13 +29,13 @@
  * <p>
  * There are several conventions for the definition of FFT and inverse FFT,
  * mainly on different coefficient and exponent. Here the equations are listed
- * in the comments of the corresponding methods.
+ * in the comments of the corresponding methods.</p>
  * <p>
  * We require the length of data set to be power of 2, this greatly simplifies
  * and speeds up the code. Users can pad the data with zeros to meet this
  * requirement. There are other flavors of FFT, for reference, see S. Winograd,
  * <i>On computing the discrete Fourier transform</i>, Mathematics of Computation,
- * 32 (1978), 175 - 199.
+ * 32 (1978), 175 - 199.</p>
  *
  * @version $Revision$ $Date$
  */
@@ -64,7 +64,8 @@
      * Transform the given real data set.
      * <p>
      * The formula is $ y_n = \Sigma_{k=0}^{N-1} e^{-2 \pi i nk/N} x_k $
-     *
+     * </p>
+     * 
      * @param f the real data array to be transformed
      * @return the complex transformed array
      * @throws MathException if any math-related errors occur
@@ -80,7 +81,8 @@
      * Transform the given real function, sampled on the given interval.
      * <p>
      * The formula is $ y_n = \Sigma_{k=0}^{N-1} e^{-2 \pi i nk/N} x_k $
-     *
+     * </p>
+     * 
      * @param f the function to be sampled and transformed
      * @param min the lower bound for the interval
      * @param max the upper bound for the interval
@@ -101,7 +103,8 @@
      * Transform the given complex data set.
      * <p>
      * The formula is $ y_n = \Sigma_{k=0}^{N-1} e^{-2 \pi i nk/N} x_k $
-     *
+     * </p>
+     * 
      * @param f the complex data array to be transformed
      * @return the complex transformed array
      * @throws MathException if any math-related errors occur
@@ -118,7 +121,8 @@
      * Transform the given real data set.
      * <p>
      * The formula is $y_n = (1/\sqrt{N}) \Sigma_{k=0}^{N-1} e^{-2 \pi i nk/N} x_k$
-     *
+     * </p>
+     * 
      * @param f the real data array to be transformed
      * @return the complex transformed array
      * @throws MathException if any math-related errors occur
@@ -135,7 +139,8 @@
      * Transform the given real function, sampled on the given interval.
      * <p>
      * The formula is $y_n = (1/\sqrt{N}) \Sigma_{k=0}^{N-1} e^{-2 \pi i nk/N} x_k$
-     *
+     * </p>
+     * 
      * @param f the function to be sampled and transformed
      * @param min the lower bound for the interval
      * @param max the upper bound for the interval
@@ -157,7 +162,8 @@
      * Transform the given complex data set.
      * <p>
      * The formula is $y_n = (1/\sqrt{N}) \Sigma_{k=0}^{N-1} e^{-2 \pi i nk/N} x_k$
-     *
+     * </p>
+     * 
      * @param f the complex data array to be transformed
      * @return the complex transformed array
      * @throws MathException if any math-related errors occur
@@ -175,7 +181,8 @@
      * Inversely transform the given real data set.
      * <p>
      * The formula is $ x_k = (1/N) \Sigma_{n=0}^{N-1} e^{2 \pi i nk/N} y_n $
-     *
+     * </p>
+     * 
      * @param f the real data array to be inversely transformed
      * @return the complex inversely transformed array
      * @throws MathException if any math-related errors occur
@@ -192,7 +199,8 @@
      * Inversely transform the given real function, sampled on the given interval.
      * <p>
      * The formula is $ x_k = (1/N) \Sigma_{n=0}^{N-1} e^{2 \pi i nk/N} y_n $
-     *
+     * </p>
+     * 
      * @param f the function to be sampled and inversely transformed
      * @param min the lower bound for the interval
      * @param max the upper bound for the interval
@@ -214,7 +222,8 @@
      * Inversely transform the given complex data set.
      * <p>
      * The formula is $ x_k = (1/N) \Sigma_{n=0}^{N-1} e^{2 \pi i nk/N} y_n $
-     *
+     * </p>
+     * 
      * @param f the complex data array to be inversely transformed
      * @return the complex inversely transformed array
      * @throws MathException if any math-related errors occur
@@ -232,7 +241,8 @@
      * Inversely transform the given real data set.
      * <p>
      * The formula is $x_k = (1/\sqrt{N}) \Sigma_{n=0}^{N-1} e^{2 \pi i nk/N} y_n$
-     *
+     * </p>
+     * 
      * @param f the real data array to be inversely transformed
      * @return the complex inversely transformed array
      * @throws MathException if any math-related errors occur
@@ -249,7 +259,8 @@
      * Inversely transform the given real function, sampled on the given interval.
      * <p>
      * The formula is $x_k = (1/\sqrt{N}) \Sigma_{n=0}^{N-1} e^{2 \pi i nk/N} y_n$
-     *
+     * </p>
+     * 
      * @param f the function to be sampled and inversely transformed
      * @param min the lower bound for the interval
      * @param max the upper bound for the interval
@@ -271,7 +282,8 @@
      * Inversely transform the given complex data set.
      * <p>
      * The formula is $x_k = (1/\sqrt{N}) \Sigma_{n=0}^{N-1} e^{2 \pi i nk/N} y_n$
-     *
+     * </p>
+     * 
      * @param f the complex data array to be inversely transformed
      * @return the complex inversely transformed array
      * @throws MathException if any math-related errors occur
@@ -401,7 +413,7 @@
      * <p>
      * The computed omega[] = { 1, w, w^2, ... w^(n-1) } where
      * w = exp(-2 \pi i / n), i = sqrt(-1). Note n is positive for
-     * forward transform and negative for inverse transform.
+     * forward transform and negative for inverse transform. </p>
      * 
      * @param n the integer passed in
      * @throws IllegalArgumentException if n = 0
@@ -440,7 +452,7 @@
      * The interval is divided equally into N sections and sample points
      * are taken from min to max-(max-min)/N. Usually f(x) is periodic
      * such that f(min) = f(max) (note max is not sampled), but we don't
-     * require that.
+     * require that.</p>
      *
      * @param f the function to be sampled
      * @param min the lower bound for the interval

Modified: commons/proper/math/trunk/src/java/org/apache/commons/math/transform/FastSineTransformer.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/java/org/apache/commons/math/transform/FastSineTransformer.java?rev=617953&r1=617952&r2=617953&view=diff
==============================================================================
--- commons/proper/math/trunk/src/java/org/apache/commons/math/transform/FastSineTransformer.java
(original)
+++ commons/proper/math/trunk/src/java/org/apache/commons/math/transform/FastSineTransformer.java
Sat Feb  2 21:54:00 2008
@@ -28,11 +28,11 @@
  * <b>Fast Fourier Transforms</b>, ISBN 0849371635, chapter 3.
  * <p>
  * FST is its own inverse, up to a multiplier depending on conventions.
- * The equations are listed in the comments of the corresponding methods.
+ * The equations are listed in the comments of the corresponding methods.</p>
  * <p>
  * Similar to FFT, we also require the length of data set to be power of 2.
  * In addition, the first element must be 0 and it's enforced in function
- * transformation after sampling.
+ * transformation after sampling.</p>
  *
  * @version $Revision$ $Date$
  */
@@ -52,7 +52,8 @@
      * Transform the given real data set.
      * <p>
      * The formula is $ F_n = \Sigma_{k=0}^{N-1} f_k \sin(\pi nk/N) $
-     *
+     * </p>
+     * 
      * @param f the real data array to be transformed
      * @return the real transformed array
      * @throws MathException if any math-related errors occur
@@ -68,7 +69,8 @@
      * Transform the given real function, sampled on the given interval.
      * <p>
      * The formula is $ F_n = \Sigma_{k=0}^{N-1} f_k \sin(\pi nk/N) $
-     *
+     * </p>
+     * 
      * @param f the function to be sampled and transformed
      * @param min the lower bound for the interval
      * @param max the upper bound for the interval
@@ -90,7 +92,8 @@
      * Transform the given real data set.
      * <p>
      * The formula is $ F_n = \sqrt{2/N} \Sigma_{k=0}^{N-1} f_k \sin(\pi nk/N) $
-     *
+     * </p>
+     * 
      * @param f the real data array to be transformed
      * @return the real transformed array
      * @throws MathException if any math-related errors occur
@@ -107,7 +110,8 @@
      * Transform the given real function, sampled on the given interval.
      * <p>
      * The formula is $ F_n = \sqrt{2/N} \Sigma_{k=0}^{N-1} f_k \sin(\pi nk/N) $
-     *
+     * </p>
+     * 
      * @param f the function to be sampled and transformed
      * @param min the lower bound for the interval
      * @param max the upper bound for the interval
@@ -130,7 +134,8 @@
      * Inversely transform the given real data set.
      * <p>
      * The formula is $ f_k = (2/N) \Sigma_{n=0}^{N-1} F_n \sin(\pi nk/N) $
-     *
+     * </p>
+     * 
      * @param f the real data array to be inversely transformed
      * @return the real inversely transformed array
      * @throws MathException if any math-related errors occur
@@ -147,7 +152,8 @@
      * Inversely transform the given real function, sampled on the given interval.
      * <p>
      * The formula is $ f_k = (2/N) \Sigma_{n=0}^{N-1} F_n \sin(\pi nk/N) $
-     *
+     * </p>
+     * 
      * @param f the function to be sampled and inversely transformed
      * @param min the lower bound for the interval
      * @param max the upper bound for the interval
@@ -170,7 +176,8 @@
      * Inversely transform the given real data set.
      * <p>
      * The formula is $ f_k = \sqrt{2/N} \Sigma_{n=0}^{N-1} F_n \sin(\pi nk/N) $
-     *
+     * </p>
+     * 
      * @param f the real data array to be inversely transformed
      * @return the real inversely transformed array
      * @throws MathException if any math-related errors occur
@@ -186,7 +193,8 @@
      * Inversely transform the given real function, sampled on the given interval.
      * <p>
      * The formula is $ f_k = \sqrt{2/N} \Sigma_{n=0}^{N-1} F_n \sin(\pi nk/N) $
-     *
+     * </p>
+     * 
      * @param f the function to be sampled and inversely transformed
      * @param min the lower bound for the interval
      * @param max the upper bound for the interval

Modified: commons/proper/math/trunk/src/java/org/apache/commons/math/util/ContinuedFraction.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/java/org/apache/commons/math/util/ContinuedFraction.java?rev=617953&r1=617952&r2=617953&view=diff
==============================================================================
--- commons/proper/math/trunk/src/java/org/apache/commons/math/util/ContinuedFraction.java
(original)
+++ commons/proper/math/trunk/src/java/org/apache/commons/math/util/ContinuedFraction.java
Sat Feb  2 21:54:00 2008
@@ -119,7 +119,7 @@
      * The recurrence relationship defined in those equations can result in
      * very large intermediate results which can result in numerical overflow.
      * As a means to combat these overflow conditions, the intermediate results
-     * are scaled whenever they threaten to become numerically unstable.
+     * are scaled whenever they threaten to become numerically unstable.</p>
      *   
      * @param x the evaluation point.
      * @param epsilon maximum error allowed.

Modified: commons/proper/math/trunk/src/java/org/apache/commons/math/util/MathUtils.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/java/org/apache/commons/math/util/MathUtils.java?rev=617953&r1=617952&r2=617953&view=diff
==============================================================================
--- commons/proper/math/trunk/src/java/org/apache/commons/math/util/MathUtils.java (original)
+++ commons/proper/math/trunk/src/java/org/apache/commons/math/util/MathUtils.java Sat Feb
 2 21:54:00 2008
@@ -145,7 +145,7 @@
      * <code>Long.MAX_VALUE</code> an <code>ArithMeticException
      *      </code> is
      * thrown.</li>
-     * </ul>
+     * </ul></p>
      * 
      * @param n the size of the set
      * @param k the size of the subsets to be counted
@@ -193,7 +193,7 @@
      * largest value of <code>n</code> 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>
+     * </ul></p>
      * 
      * @param n the size of the set
      * @param k the size of the subsets to be counted
@@ -215,7 +215,7 @@
      * <ul>
      * <li> <code>0 <= k <= n </code> (otherwise
      * <code>IllegalArgumentException</code> is thrown)</li>
-     * </ul>
+     * </ul></p>
      * 
      * @param n the size of the set
      * @param k the size of the subsets to be counted
@@ -340,7 +340,7 @@
      * <ul>
      * <li> <code>n >= 0</code> (otherwise
      * <code>IllegalArgumentException</code> is thrown)</li>
-     * </ul>
+     * </ul></p>
      * 
      * @param n argument
      * @return <code>n!</code>
@@ -615,9 +615,9 @@
      * If <code>direction</code> is greater than or equal to<code>d</code>,
      * the smallest machine representable number strictly greater than
      * <code>d</code> is returned; otherwise the largest representable number
-     * strictly less than <code>d</code> is returned.
+     * strictly less than <code>d</code> is returned.</p>
      * <p>
-     * If <code>d</code> is NaN or Infinite, it is returned unchanged.
+     * If <code>d</code> is NaN or Infinite, it is returned unchanged.</p>
      * 
      * @param d base number
      * @param direction (the only important thing is whether
@@ -823,7 +823,7 @@
      * for byte value <code>x</code>.
      * <p>
      * For a byte value x, this method returns (byte)(+1) if x > 0, (byte)(0) if
-     * x = 0, and (byte)(-1) if x < 0.
+     * x = 0, and (byte)(-1) if x < 0.</p>
      * 
      * @param x the value, a byte
      * @return (byte)(+1), (byte)(0), or (byte)(-1), depending on the sign of x
@@ -839,7 +839,7 @@
      * For a double value <code>x</code>, this method returns
      * <code>+1.0</code> if <code>x > 0</code>, <code>0.0</code>
if
      * <code>x = 0.0</code>, and <code>-1.0</code> if <code>x
< 0</code>.
-     * Returns <code>NaN</code> if <code>x</code> is <code>NaN</code>.
+     * Returns <code>NaN</code> if <code>x</code> is <code>NaN</code>.</p>
      * 
      * @param x the value, a double
      * @return +1.0, 0.0, or -1.0, depending on the sign of x
@@ -857,7 +857,7 @@
      * <p>
      * For a float value x, this method returns +1.0F if x > 0, 0.0F if x =
      * 0.0F, and -1.0F if x < 0. Returns <code>NaN</code> if <code>x</code>
-     * is <code>NaN</code>.
+     * is <code>NaN</code>.</p>
      * 
      * @param x the value, a float
      * @return +1.0F, 0.0F, or -1.0F, depending on the sign of x
@@ -874,7 +874,7 @@
      * for int value <code>x</code>.
      * <p>
      * For an int value x, this method returns +1 if x > 0, 0 if x = 0, and -1
-     * if x < 0.
+     * if x < 0.</p>
      * 
      * @param x the value, an int
      * @return +1, 0, or -1, depending on the sign of x
@@ -888,7 +888,7 @@
      * for long value <code>x</code>.
      * <p>
      * For a long value x, this method returns +1L if x > 0, 0L if x = 0, and
-     * -1L if x < 0.
+     * -1L if x < 0.</p>
      * 
      * @param x the value, a long
      * @return +1L, 0L, or -1L, depending on the sign of x
@@ -902,7 +902,7 @@
      * for short value <code>x</code>.
      * <p>
      * For a short value x, this method returns (short)(+1) if x > 0, (short)(0)
-     * if x = 0, and (short)(-1) if x < 0.
+     * if x = 0, and (short)(-1) if x < 0.</p>
      * 
      * @param x the value, a short
      * @return (short)(+1), (short)(0), or (short)(-1), depending on the sign of

Modified: commons/proper/math/trunk/src/java/org/apache/commons/math/util/ResizableDoubleArray.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/java/org/apache/commons/math/util/ResizableDoubleArray.java?rev=617953&r1=617952&r2=617953&view=diff
==============================================================================
--- commons/proper/math/trunk/src/java/org/apache/commons/math/util/ResizableDoubleArray.java
(original)
+++ commons/proper/math/trunk/src/java/org/apache/commons/math/util/ResizableDoubleArray.java
Sat Feb  2 21:54:00 2008
@@ -66,7 +66,6 @@
  * properties enforce this requirement, throwing IllegalArgumentException if it
  * is violated.
  * </p>
- * <p>
  * @version $Revision$ $Date$
  */
 public class ResizableDoubleArray implements DoubleArray, Serializable {
@@ -395,6 +394,7 @@
      * the new array size will be <code>internalArray.length * expansionFactor.</code>
      * If <code>expansionMode</code> is set to ADDITIVE_MODE,  the length
      * after expansion will be <code>internalArray.length + expansionFactor</code>
+     * </p>
      */
     protected synchronized void expand() {
 



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