You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@commons.apache.org by ps...@apache.org on 2008/02/03 06:54:06 UTC
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...
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() {