Tab police

git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@1499813 13f79535-47bb-0310-9956-ffa450edef68

build.xml

@@ -97,7 +97,7 @@
 
   <!-- Should Java compilations set the 'optimize' compiler option? -->
   <property name="compile.optimize"        value="true"/>
-	
+
   <!-- File encoding -->
   <property name="source.encoding"         value="UTF-8"/>
     
@@ -241,9 +241,9 @@
                  author="true"
                 private="true"
                 version="true"
-    	       encoding="${source.encoding}"
+               encoding="${source.encoding}"
-    	        charset="${source.encoding}"
+                charset="${source.encoding}"
-    	    docencoding="${source.encoding}"
+            docencoding="${source.encoding}"
                doctitle="&lt;h1&gt;${component.title} ${component.version}&lt;/h1&gt;"
             windowtitle="${component.title} ${component.version}"
                  bottom="Copyright (c) 2003-${current.year}  Apache Software Foundation"

findbugs-exclude-filter.xml

@@ -105,7 +105,7 @@
   <!-- The following equality test is intentional and needed for rounding purposes -->
   <Match>
     <Class name="org.apache.commons.math3.util.Precision" />
-	<Method name="roundUnscaled" params="double,double,int" returns="double" />
+    <Method name="roundUnscaled" params="double,double,int" returns="double" />
     <Bug pattern="FE_FLOATING_POINT_EQUALITY" />
   </Match>
 

src/changes/changes.xml

@@ -707,17 +707,17 @@ counterpart in either Math or StrictMath (cf. MATH-740).
         Removed "MathRuntimeException" (from package "o.a.c.math").
       </action>
       <action dev="tn" type="fix" issue="MATH-739">
-      	Merged interface and implementation of statistical tests in
+        Merged interface and implementation of statistical tests in
-      	o.a.c.m.stat.inference package.
+        o.a.c.m.stat.inference package.
       </action>
       <action dev="tn" type="update" issue="MATH-670">
-      	Merged interface and implementation of EmpiricalDistribution.
+        Merged interface and implementation of EmpiricalDistribution.
       </action>
       <action dev="tn" type="fix" issue="MATH-588">
-      	Relaxed test for equality in UnivariateStatisticAbstractTest.
+        Relaxed test for equality in UnivariateStatisticAbstractTest.
       </action>
       <action dev="tn" type="update" issue="MATH-575">
-      	Modified the genetics package to use localized exception messages.
+        Modified the genetics package to use localized exception messages.
       </action>
       <action dev="tn" type="fix" issue="MATH-652" due-to="Greg Sterijevski">
         Fixed a faulty test for zero in TridiagonalTransformer.

src/main/java/org/apache/commons/math3/analysis/integration/MidPointIntegrator.java

@@ -141,7 +141,7 @@ public class MidPointIntegrator extends BaseAbstractUnivariateIntegrator {
 
     /** {@inheritDoc} */
     @Override
-	protected double doIntegrate()
+    protected double doIntegrate()
         throws MathIllegalArgumentException, TooManyEvaluationsException, MaxCountExceededException {
 
         final double min = getMin();

src/site/xdoc/userguide/filter.xml

@@ -41,22 +41,22 @@
           MeasurementModel</a>, which contain the corresponding transformation and noise covariance matrices. 
           The parameter names used in the respective models correspond to the following names commonly used 
           in the mathematical literature:
-		<ul>
+        <ul>
- 			<li>A - state transition matrix</li>
+             <li>A - state transition matrix</li>
- 			<li>B - control input matrix</li>
+             <li>B - control input matrix</li>
- 			<li>H - measurement matrix</li>
+             <li>H - measurement matrix</li>
- 			<li>Q - process noise covariance matrix</li>
+             <li>Q - process noise covariance matrix</li>
- 			<li>R - measurement noise covariance matrix</li>
+             <li>R - measurement noise covariance matrix</li>
- 			<li>P - error covariance matrix</li>
+             <li>P - error covariance matrix</li>
- 		</ul>
+         </ul>
- 		</p>
+         </p>
         <p>
         <dl>
-      	  <dt>Initialization</dt>
+          <dt>Initialization</dt>
           <dd> The following code will create a Kalman filter using the provided 
           DefaultMeasurementModel and DefaultProcessModel classes. To support dynamically changing
           process and measurement noises, simply implement your own models.
-          	<source>
+            <source>
 // A = [ 1 ]
 RealMatrix A = new Array2DRowRealMatrix(new double[] { 1d });
 // no control input
@@ -72,11 +72,11 @@ ProcessModel pm
    = new DefaultProcessModel(A, B, Q, new ArrayRealVector(new double[] { 0 }), null);
 MeasurementModel mm = new DefaultMeasurementModel(H, R);
 KalmanFilter filter = new KalmanFilter(pm, mm);
- 			</source>
+            </source>
-		  </dd>
+          </dd>
-		  <dt>Iteration</dt>
+          <dt>Iteration</dt>
           <dd>The following code illustrates how to perform the predict/correct cycle:  
-		  <source>
+          <source>
 for (;;) {
    // predict the state estimate one time-step ahead
    // optionally provide some control input
@@ -91,9 +91,9 @@ for (;;) {
    double[] stateEstimate = filter.getStateEstimation();
    // do something with it
 }
-		  </source>
+          </source>
-		  </dd>
+          </dd>
-		  <dt>Constant Voltage Example</dt>
+          <dt>Constant Voltage Example</dt>
           <dd>The following example creates a Kalman filter for a static process: a system with a 
           constant voltage as internal state. We observe this process with an artificially 
           imposed measurement noise of 0.1V and assume an internal process noise of 1e-5V.
@@ -148,7 +148,7 @@ for (int i = 0; i &lt; 60; i++) {
 }
           </source>
           </dd>
-      	  <dt>Increasing Speed Vehicle Example</dt>
+          <dt>Increasing Speed Vehicle Example</dt>
           <dd>The following example creates a Kalman filter for a simple linear process: a 
           vehicle driving along a street with a velocity increasing at a constant rate. The process
           state is modeled as (position, velocity) and we only observe the position. A measurement

src/site/xdoc/userguide/geometry.xml

@@ -164,7 +164,7 @@
         </p>
       </subsection>
       <subsection name="11.3 Binary Space Partitioning" href="partitioning">
-	    <p>
+        <p>
           <a href="../apidocs/org/apache/commons/math3/geometry/partitioning/BSPTree.html">
           BSP trees</a> are an efficient way to represent space partitions and
           to associate attributes with each cell. Each node in a BSP tree

src/site/xdoc/userguide/optimization.xml

@@ -253,11 +253,11 @@
     <dl>
     <dt>Quadratic Problem Example</dt>
     <dd>
-	
+
-	
+
-	We are looking to find the best parameters [a, b, c] for the quadratic function
+    We are looking to find the best parameters [a, b, c] for the quadratic function
     <b><code>f(x) = a x<sup>2</sup> + b x + c</code></b>.
-	The data set below was generated using [a = 8, b = 10, c = 16]. 
+    The data set below was generated using [a = 8, b = 10, c = 16]. 
     A random number between zero and one was added to each y value calculated.
 
     <table cellspacing="0" cellpadding="3">
@@ -323,9 +323,9 @@ We'll tackle the implementation of the <code>MultivariateMatrixFunction jacobian
 In this case the Jacobian is the partial derivative of the function with respect
 to the parameters a, b and c.  These derivatives are computed as follows:
 <ul>
-	<li>d(ax<sup>2</sup> + bx + c)/da = x<sup>2</sup></li>
+    <li>d(ax<sup>2</sup> + bx + c)/da = x<sup>2</sup></li>
-	<li>d(ax<sup>2</sup> + bx + c)/db = x</li>
+    <li>d(ax<sup>2</sup> + bx + c)/db = x</li>
-	<li>d(ax<sup>2</sup> + bx + c)/dc = 1</li>
+    <li>d(ax<sup>2</sup> + bx + c)/dc = 1</li>
 </ul>
 </p>
 
@@ -478,11 +478,11 @@ private static class QuadraticProblem
     }
 
     public double[] calculateTarget() {
-    	double[] target = new double[y.size()];
+        double[] target = new double[y.size()];
-    	for (int i = 0; i &lt; y.size(); i++) {
+        for (int i = 0; i &lt; y.size(); i++) {
-    		target[i] = y.get(i).doubleValue();
+            target[i] = y.get(i).doubleValue();
-    	}
+        }
-    	return target;
+        return target;
     }
 
     private double[][] jacobian(double[] variables) {

src/test/java/org/apache/commons/math3/analysis/integration/gauss/GaussianQuadratureAbstractTest.java

@@ -81,8 +81,8 @@ public abstract class GaussianQuadratureAbstractTest {
      */
     public abstract double getExpectedValue(final int n);
 
-	/**
+    /**
-	 * Checks that the value of the integral of each monomial
+     * Checks that the value of the integral of each monomial
      *   <code>x<sup>0</sup>, ... , x<sup>p</sup></code>
      * returned by the quadrature rule under test conforms with the expected
      * value.
@@ -104,7 +104,7 @@ public abstract class GaussianQuadratureAbstractTest {
                                     " with a " +
                                     integrator.getNumberOfPoints() + "-point quadrature rule",
                                     expected, actual, eps);
-			} else {
+            } else {
                 double err = Math.abs(actual - expected) / Math.ulp(expected);
                 Assert.assertEquals("while integrating monomial x**" + n + " with a " +
                                     + integrator.getNumberOfPoints() + "-point quadrature rule, " +

src/test/java/org/apache/commons/math3/random/RandomDataGeneratorTest.java

@@ -955,7 +955,7 @@ public class RandomDataGeneratorTest {
         }
         
         String[] labels = {"{0, 1, 2}", "{ 0, 2, 1 }", "{ 1, 0, 2 }",
-        		"{ 1, 2, 0 }", "{ 2, 0, 1 }", "{ 2, 1, 0 }"};
+                "{ 1, 2, 0 }", "{ 2, 0, 1 }", "{ 2, 1, 0 }"};
         TestUtils.assertChiSquareAccept(labels, expected, observed, 0.001);
 
         // Check size = 1 boundary case