Tab police
git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@1499813 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Sebastian Bazley
parent
a6dfb27d91
commit
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@ -97,7 +97,7 @@
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<!-- Should Java compilations set the 'optimize' compiler option? -->
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<property name="compile.optimize" value="true"/>
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<!-- File encoding -->
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<property name="source.encoding" value="UTF-8"/>
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@ -241,9 +241,9 @@
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author="true"
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private="true"
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version="true"
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encoding="${source.encoding}"
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charset="${source.encoding}"
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docencoding="${source.encoding}"
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encoding="${source.encoding}"
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charset="${source.encoding}"
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docencoding="${source.encoding}"
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doctitle="<h1>${component.title} ${component.version}</h1>"
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windowtitle="${component.title} ${component.version}"
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bottom="Copyright (c) 2003-${current.year} Apache Software Foundation"
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@ -105,7 +105,7 @@
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<!-- The following equality test is intentional and needed for rounding purposes -->
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<Match>
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<Class name="org.apache.commons.math3.util.Precision" />
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<Method name="roundUnscaled" params="double,double,int" returns="double" />
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<Method name="roundUnscaled" params="double,double,int" returns="double" />
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<Bug pattern="FE_FLOATING_POINT_EQUALITY" />
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</Match>
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@ -707,17 +707,17 @@ counterpart in either Math or StrictMath (cf. MATH-740).
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Removed "MathRuntimeException" (from package "o.a.c.math").
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</action>
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<action dev="tn" type="fix" issue="MATH-739">
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Merged interface and implementation of statistical tests in
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o.a.c.m.stat.inference package.
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Merged interface and implementation of statistical tests in
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o.a.c.m.stat.inference package.
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</action>
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<action dev="tn" type="update" issue="MATH-670">
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Merged interface and implementation of EmpiricalDistribution.
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Merged interface and implementation of EmpiricalDistribution.
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</action>
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<action dev="tn" type="fix" issue="MATH-588">
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Relaxed test for equality in UnivariateStatisticAbstractTest.
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Relaxed test for equality in UnivariateStatisticAbstractTest.
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</action>
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<action dev="tn" type="update" issue="MATH-575">
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Modified the genetics package to use localized exception messages.
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Modified the genetics package to use localized exception messages.
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</action>
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<action dev="tn" type="fix" issue="MATH-652" due-to="Greg Sterijevski">
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Fixed a faulty test for zero in TridiagonalTransformer.
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@ -141,7 +141,7 @@ public class MidPointIntegrator extends BaseAbstractUnivariateIntegrator {
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/** {@inheritDoc} */
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@Override
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protected double doIntegrate()
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protected double doIntegrate()
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throws MathIllegalArgumentException, TooManyEvaluationsException, MaxCountExceededException {
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final double min = getMin();
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@ -41,22 +41,22 @@
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MeasurementModel</a>, which contain the corresponding transformation and noise covariance matrices.
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The parameter names used in the respective models correspond to the following names commonly used
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in the mathematical literature:
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<ul>
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<li>A - state transition matrix</li>
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<li>B - control input matrix</li>
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<li>H - measurement matrix</li>
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<li>Q - process noise covariance matrix</li>
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<li>R - measurement noise covariance matrix</li>
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<li>P - error covariance matrix</li>
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</ul>
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</p>
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<ul>
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<li>A - state transition matrix</li>
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<li>B - control input matrix</li>
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<li>H - measurement matrix</li>
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<li>Q - process noise covariance matrix</li>
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<li>R - measurement noise covariance matrix</li>
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<li>P - error covariance matrix</li>
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</ul>
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</p>
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<p>
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<dl>
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<dt>Initialization</dt>
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<dt>Initialization</dt>
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<dd> The following code will create a Kalman filter using the provided
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DefaultMeasurementModel and DefaultProcessModel classes. To support dynamically changing
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process and measurement noises, simply implement your own models.
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<source>
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<source>
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// A = [ 1 ]
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RealMatrix A = new Array2DRowRealMatrix(new double[] { 1d });
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// no control input
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@ -72,11 +72,11 @@ ProcessModel pm
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= new DefaultProcessModel(A, B, Q, new ArrayRealVector(new double[] { 0 }), null);
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MeasurementModel mm = new DefaultMeasurementModel(H, R);
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KalmanFilter filter = new KalmanFilter(pm, mm);
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</source>
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</dd>
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<dt>Iteration</dt>
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</source>
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</dd>
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<dt>Iteration</dt>
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<dd>The following code illustrates how to perform the predict/correct cycle:
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<source>
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<source>
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for (;;) {
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// predict the state estimate one time-step ahead
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// optionally provide some control input
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@ -91,9 +91,9 @@ for (;;) {
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double[] stateEstimate = filter.getStateEstimation();
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// do something with it
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}
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</source>
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</dd>
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<dt>Constant Voltage Example</dt>
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</source>
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</dd>
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<dt>Constant Voltage Example</dt>
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<dd>The following example creates a Kalman filter for a static process: a system with a
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constant voltage as internal state. We observe this process with an artificially
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imposed measurement noise of 0.1V and assume an internal process noise of 1e-5V.
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@ -148,7 +148,7 @@ for (int i = 0; i < 60; i++) {
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}
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</source>
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</dd>
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<dt>Increasing Speed Vehicle Example</dt>
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<dt>Increasing Speed Vehicle Example</dt>
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<dd>The following example creates a Kalman filter for a simple linear process: a
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vehicle driving along a street with a velocity increasing at a constant rate. The process
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state is modeled as (position, velocity) and we only observe the position. A measurement
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@ -164,7 +164,7 @@
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</p>
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</subsection>
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<subsection name="11.3 Binary Space Partitioning" href="partitioning">
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<p>
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<p>
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<a href="../apidocs/org/apache/commons/math3/geometry/partitioning/BSPTree.html">
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BSP trees</a> are an efficient way to represent space partitions and
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to associate attributes with each cell. Each node in a BSP tree
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@ -253,11 +253,11 @@
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<dl>
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<dt>Quadratic Problem Example</dt>
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<dd>
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We are looking to find the best parameters [a, b, c] for the quadratic function
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We are looking to find the best parameters [a, b, c] for the quadratic function
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<b><code>f(x) = a x<sup>2</sup> + b x + c</code></b>.
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The data set below was generated using [a = 8, b = 10, c = 16].
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The data set below was generated using [a = 8, b = 10, c = 16].
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A random number between zero and one was added to each y value calculated.
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<table cellspacing="0" cellpadding="3">
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@ -323,9 +323,9 @@ We'll tackle the implementation of the <code>MultivariateMatrixFunction jacobian
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In this case the Jacobian is the partial derivative of the function with respect
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to the parameters a, b and c. These derivatives are computed as follows:
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<ul>
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<li>d(ax<sup>2</sup> + bx + c)/da = x<sup>2</sup></li>
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<li>d(ax<sup>2</sup> + bx + c)/db = x</li>
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<li>d(ax<sup>2</sup> + bx + c)/dc = 1</li>
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<li>d(ax<sup>2</sup> + bx + c)/da = x<sup>2</sup></li>
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<li>d(ax<sup>2</sup> + bx + c)/db = x</li>
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<li>d(ax<sup>2</sup> + bx + c)/dc = 1</li>
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</ul>
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</p>
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@ -478,11 +478,11 @@ private static class QuadraticProblem
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}
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public double[] calculateTarget() {
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double[] target = new double[y.size()];
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for (int i = 0; i < y.size(); i++) {
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target[i] = y.get(i).doubleValue();
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}
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return target;
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double[] target = new double[y.size()];
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for (int i = 0; i < y.size(); i++) {
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target[i] = y.get(i).doubleValue();
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}
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return target;
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}
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private double[][] jacobian(double[] variables) {
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@ -81,8 +81,8 @@ public abstract class GaussianQuadratureAbstractTest {
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*/
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public abstract double getExpectedValue(final int n);
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/**
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* Checks that the value of the integral of each monomial
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/**
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* Checks that the value of the integral of each monomial
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* <code>x<sup>0</sup>, ... , x<sup>p</sup></code>
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* returned by the quadrature rule under test conforms with the expected
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* value.
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@ -104,7 +104,7 @@ public abstract class GaussianQuadratureAbstractTest {
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" with a " +
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integrator.getNumberOfPoints() + "-point quadrature rule",
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expected, actual, eps);
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} else {
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} else {
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double err = Math.abs(actual - expected) / Math.ulp(expected);
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Assert.assertEquals("while integrating monomial x**" + n + " with a " +
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+ integrator.getNumberOfPoints() + "-point quadrature rule, " +
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@ -955,7 +955,7 @@ public class RandomDataGeneratorTest {
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}
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String[] labels = {"{0, 1, 2}", "{ 0, 2, 1 }", "{ 1, 0, 2 }",
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"{ 1, 2, 0 }", "{ 2, 0, 1 }", "{ 2, 1, 0 }"};
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"{ 1, 2, 0 }", "{ 2, 0, 1 }", "{ 2, 1, 0 }"};
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TestUtils.assertChiSquareAccept(labels, expected, observed, 0.001);
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// Check size = 1 boundary case
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