tighten checkstyle rules for hidden fields

git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@810238 13f79535-47bb-0310-9956-ffa450edef68

checkstyle.xml

@@ -85,11 +85,13 @@
     <!-- Constant names should obey the traditional all uppercase naming convention -->
     <module name="ConstantName" />
 
-    <!--
+    <!-- Method parameters and local variables should not hide fields, except in constructors and setters -->
     <module name="HiddenField">
         <property name="ignoreConstructorParameter" value="true" />
         <property name="ignoreSetter" value="true" />
     </module>
+
+    <!--
     <module name="DeclarationOrder" />
     <module name="IllegalCatch" />
     <module name="RedundantModifier" />

src/main/java/org/apache/commons/math/analysis/integration/UnivariateRealIntegratorImpl.java

@@ -115,11 +115,11 @@ public abstract class UnivariateRealIntegratorImpl
     /**
      * Convenience function for implementations.
      * 
-     * @param result the result to set
+     * @param newResult the result to set
      * @param iterationCount the iteration count to set
      */
-    protected final void setResult(double result, int iterationCount) {
+    protected final void setResult(double newResult, int iterationCount) {
-        this.result = result;
+        this.result         = newResult;
         this.iterationCount = iterationCount;
         this.resultComputed = true;
     }

src/main/java/org/apache/commons/math/analysis/solvers/UnivariateRealSolverImpl.java

@@ -134,11 +134,11 @@ public abstract class UnivariateRealSolverImpl
     /**
      * Convenience function for implementations.
      * 
-     * @param result the result to set
+     * @param newResult the result to set
      * @param iterationCount the iteration count to set
      */
-    protected final void setResult(final double result, final int iterationCount) {
+    protected final void setResult(final double newResult, final int iterationCount) {
-        this.result         = result;
+        this.result         = newResult;
         this.iterationCount = iterationCount;
         this.resultComputed = true;
     }
@@ -171,16 +171,16 @@ public abstract class UnivariateRealSolverImpl
      * 
      * @param lower  the lower endpoint 
      * @param upper  the upper endpoint
-     * @param f the function
+     * @param function the function
      * @return true if f(lower) * f(upper) < 0
      * @throws FunctionEvaluationException if an error occurs evaluating the 
      * function at the endpoints
      */
     protected boolean isBracketing(final double lower, final double upper, 
-                                   final UnivariateRealFunction f)
+                                   final UnivariateRealFunction function)
         throws FunctionEvaluationException {
-        final double f1 = f.value(lower);
+        final double f1 = function.value(lower);
-        final double f2 = f.value(upper);
+        final double f2 = function.value(upper);
         return ((f1 > 0 && f2 < 0) || (f1 < 0 && f2 > 0));
     }
     
@@ -235,21 +235,21 @@ public abstract class UnivariateRealSolverImpl
      * 
      * @param lower  lower endpoint
      * @param upper upper endpoint
-     * @param f function
+     * @param function function
      * @throws IllegalArgumentException
      * @throws FunctionEvaluationException if an error occurs evaluating the 
      * function at the endpoints
      */
     protected void verifyBracketing(final double lower, final double upper, 
-                                    final UnivariateRealFunction f)
+                                    final UnivariateRealFunction function)
         throws FunctionEvaluationException {
         
         verifyInterval(lower, upper);
-        if (!isBracketing(lower, upper, f)) {
+        if (!isBracketing(lower, upper, function)) {
             throw MathRuntimeException.createIllegalArgumentException(
                     "function values at endpoints do not have different signs.  " +
                     "Endpoints: [{0}, {1}], Values: [{2}, {3}]",
-                    lower, upper, f.value(lower), f.value(upper));       
+                    lower, upper, function.value(lower), function.value(upper));       
         }
     }
 }

src/main/java/org/apache/commons/math/complex/Complex.java

@@ -1001,13 +1001,13 @@ public class Complex implements FieldElement<Complex>, Serializable  {
     /**
      * Create a complex number given the real and imaginary parts.
      *
-     * @param real the real part
+     * @param realPart the real part
-     * @param imaginary the imaginary part
+     * @param imaginaryPart the imaginary part
      * @return a new complex number instance
      * @since 1.2
      */
-    protected Complex createComplex(double real, double imaginary) {
+    protected Complex createComplex(double realPart, double imaginaryPart) {
-        return new Complex(real, imaginary);
+        return new Complex(realPart, imaginaryPart);
     }
 
     /**

src/main/java/org/apache/commons/math/distribution/WeibullDistributionImpl.java

@@ -35,10 +35,10 @@ public class WeibullDistributionImpl extends AbstractContinuousDistribution
     private static final long serialVersionUID = 8589540077390120676L;
     
     /** The shape parameter. */
-    private double alpha;
+    private double shape;
     
     /** The scale parameter. */
-    private double beta;
+    private double scale;
     
     /**
      * Creates weibull distribution with the given shape and scale and a
@@ -72,7 +72,7 @@ public class WeibullDistributionImpl extends AbstractContinuousDistribution
      * @return the shape parameter.
      */
     public double getShape() {
-        return alpha;
+        return shape;
     }
     
     /**
@@ -80,7 +80,7 @@ public class WeibullDistributionImpl extends AbstractContinuousDistribution
      * @return the scale parameter.
      */
     public double getScale() {
-        return beta;
+        return scale;
     }
     
     /**
@@ -121,7 +121,7 @@ public class WeibullDistributionImpl extends AbstractContinuousDistribution
                   "shape must be positive ({0})",
                   alpha);
         }       
-        this.alpha = alpha;
+        this.shape = alpha;
     }
     
     /**
@@ -134,7 +134,7 @@ public class WeibullDistributionImpl extends AbstractContinuousDistribution
                   "scale must be positive ({0})",
                   beta);
         }       
-        this.beta = beta;
+        this.scale = beta;
     }
 
     /**

src/main/java/org/apache/commons/math/estimation/AbstractEstimator.java

@@ -173,14 +173,14 @@ public abstract class AbstractEstimator implements Estimator {
         updateJacobian();
 
         // compute transpose(J).J, avoiding building big intermediate matrices
-        final int rows = problem.getMeasurements().length;
+        final int n = problem.getMeasurements().length;
-        final int cols = problem.getUnboundParameters().length;
+        final int m = problem.getUnboundParameters().length;
-        final int max  = cols * rows;
+        final int max  = m * n;
-        double[][] jTj = new double[cols][cols];
+        double[][] jTj = new double[m][m];
-        for (int i = 0; i < cols; ++i) {
+        for (int i = 0; i < m; ++i) {
-            for (int j = i; j < cols; ++j) {
+            for (int j = i; j < m; ++j) {
                 double sum = 0;
-                for (int k = 0; k < max; k += cols) {
+                for (int k = 0; k < max; k += m) {
                     sum += jacobian[k + i] * jacobian[k + j];
                 }
                 jTj[i][j] = sum;

src/main/java/org/apache/commons/math/genetics/AbstractListChromosome.java

@@ -57,11 +57,11 @@ public abstract class AbstractListChromosome<T> extends Chromosome {
     /**
      * 
      * Asserts that <code>representation</code> can represent a valid chromosome.
-     * @param representation representation of the chromosome
+     * @param chromosomeRepresentation representation of the chromosome
      * @throws InvalidRepresentationException iff the <code>representation</code> can not represent
      *         a valid chromosome
      */
-    protected abstract void checkValidity(List<T> representation) throws InvalidRepresentationException;
+    protected abstract void checkValidity(List<T> chromosomeRepresentation) throws InvalidRepresentationException;
 
     /**
      * Returns the (immutable) inner representation of the chromosome.
@@ -87,12 +87,12 @@ public abstract class AbstractListChromosome<T> extends Chromosome {
      * 
      * Usually, this method just calls a constructor of the class.
      * 
-     * @param representation
+     * @param chromosomeRepresentation
      *            the inner array representation of the new chromosome.
      * @return new instance extended from FixedLengthChromosome with the given
      *         arrayRepresentation
      */
-    public abstract AbstractListChromosome<T> newFixedLengthChromosome(final List<T> representation);
+    public abstract AbstractListChromosome<T> newFixedLengthChromosome(final List<T> chromosomeRepresentation);
     
     /**
      * {@inheritDoc}

src/main/java/org/apache/commons/math/genetics/BinaryChromosome.java

@@ -48,8 +48,8 @@ public abstract class BinaryChromosome extends AbstractListChromosome<Integer> {
      * {@inheritDoc}
      */
     @Override
-    protected void checkValidity(List<Integer> representation) throws InvalidRepresentationException {
+    protected void checkValidity(List<Integer> chromosomeRepresentation) throws InvalidRepresentationException {
-        for (int i : representation) {
+        for (int i : chromosomeRepresentation) {
             if (i < 0 || i >1)
                 throw new InvalidRepresentationException("Elements can be only 0 or 1.");
         }

src/main/java/org/apache/commons/math/genetics/RandomKey.java

@@ -172,8 +172,8 @@ public abstract class RandomKey<T> extends AbstractListChromosome<Double> implem
      * {@inheritDoc}
      */
     @Override    
-    protected void checkValidity(java.util.List<Double> representation) throws InvalidRepresentationException {
+    protected void checkValidity(java.util.List<Double> chromosomeRepresentation) throws InvalidRepresentationException {
-        for (double val : representation) {
+        for (double val : chromosomeRepresentation) {
             if (val < 0 || val > 1) {
                 throw new InvalidRepresentationException("Values of representation must be in [0,1] interval");
             }

src/main/java/org/apache/commons/math/linear/EigenDecompositionImpl.java

@@ -851,27 +851,27 @@ public class EigenDecompositionImpl implements EigenDecomposition {
             sigmaLow = 0;
 
             // find start of a new split segment to process
-            double eMin = (i0 == n0) ? 0 : work[4 * n0 - 6];
+            double offDiagMin = (i0 == n0) ? 0 : work[4 * n0 - 6];
-            double eMax = 0;
+            double offDiagMax = 0;
-            double qMax = work[4 * n0 - 4];
+            double diagMax    = work[4 * n0 - 4];
-            double qMin = qMax;
+            double diagMin    = diagMax;
             i0 = 0;
             for (int i = 4 * (n0 - 2); i >= 0; i -= 4) {
                 if (work[i + 2] <= 0) {
                     i0 = 1 + i / 4;
                     break;
                 }
-                if (qMin >= 4 * eMax) {
+                if (diagMin >= 4 * offDiagMax) {
-                    qMin = Math.min(qMin, work[i + 4]);
+                    diagMin    = Math.min(diagMin, work[i + 4]);
-                    eMax = Math.max(eMax, work[i + 2]);
+                    offDiagMax = Math.max(offDiagMax, work[i + 2]);
                 }
-                qMax = Math.max(qMax, work[i] + work[i + 2]);
+                diagMax    = Math.max(diagMax, work[i] + work[i + 2]);
-                eMin = Math.min(eMin, work[i + 2]);
+                offDiagMin = Math.min(offDiagMin, work[i + 2]);
             }
-            work[4 * n0 - 2] = eMin;
+            work[4 * n0 - 2] = offDiagMin;
 
             // lower bound of Gershgorin disk
-            dMin = -Math.max(0, qMin - 2 * Math.sqrt(qMin * eMax));
+            dMin = -Math.max(0, diagMin - 2 * Math.sqrt(diagMin * offDiagMax));
 
             pingPong = 0;
             int maxIter = 30 * (n0 - i0);
@@ -887,11 +887,11 @@ public class EigenDecompositionImpl implements EigenDecomposition {
                 // check for new splits after "ping" steps
                 // when the last elements of qd array are very small
                 if ((pingPong == 0) && (n0 - i0 > 3) &&
-                    (work[4 * n0 - 1] <= TOLERANCE_2 * qMax) &&
+                    (work[4 * n0 - 1] <= TOLERANCE_2 * diagMax) &&
                     (work[4 * n0 - 2] <= TOLERANCE_2 * sigma)) {
-                    int split = i0 - 1;
+                    int split  = i0 - 1;
-                    qMax = work[4 * i0];
+                    diagMax    = work[4 * i0];
-                    eMin = work[4 * i0 + 2];
+                    offDiagMin = work[4 * i0 + 2];
                     double previousEMin = work[4 * i0 + 3];
                     for (int i = 4 * i0; i < 4 * n0 - 11; i += 4) {
                         if ((work[i + 3] <= TOLERANCE_2 * work[i]) &&
@@ -899,16 +899,16 @@ public class EigenDecompositionImpl implements EigenDecomposition {
                             // insert a split
                             work[i + 2]  = -sigma;
                             split        = i / 4;
-                            qMax         = 0;
+                            diagMax      = 0;
-                            eMin         = work[i + 6];
+                            offDiagMin   = work[i + 6];
                             previousEMin = work[i + 7];
                         } else {
-                            qMax         = Math.max(qMax, work[i + 4]);
+                            diagMax      = Math.max(diagMax, work[i + 4]);
-                            eMin         = Math.min(eMin, work[i + 2]);
+                            offDiagMin   = Math.min(offDiagMin, work[i + 2]);
                             previousEMin = Math.min(previousEMin, work[i + 3]);
                         }
                     }
-                    work[4 * n0 - 2] = eMin;
+                    work[4 * n0 - 2] = offDiagMin;
                     work[4 * n0 - 1] = previousEMin;
                     i0 = split + 1;
                 }
@@ -1662,20 +1662,20 @@ public class EigenDecompositionImpl implements EigenDecomposition {
 
     /**
      * Update sigma.
-     * @param tau shift to apply to sigma
+     * @param shift shift to apply to sigma
      */
-    private void updateSigma(final double tau) {
+    private void updateSigma(final double shift) {
         // BEWARE: do NOT attempt to simplify the following statements
         // the expressions below take care to accumulate the part of sigma
         // that does not fit within a double variable into sigmaLow
-        if (tau < sigma) {
+        if (shift < sigma) {
-            sigmaLow += tau;
+            sigmaLow += shift;
             final double t = sigma + sigmaLow;
             sigmaLow -= t - sigma;
             sigma = t;
         } else {
-            final double t = sigma + tau;
+            final double t = sigma + shift;
-            sigmaLow += sigma - (t - tau);
+            sigmaLow += sigma - (t - shift);
             sigma = t;
         }
     }
@@ -1731,8 +1731,7 @@ public class EigenDecompositionImpl implements EigenDecomposition {
         int r = m - 1;
         double minG = Math.abs(work[6 * r] + work[6 * r + 3] + eigenvalue);
         for (int i = 0, sixI = 0; i < m - 1; ++i, sixI += 6) {
-            final double g = work[sixI] + d[i] * work[sixI + 9] / work[sixI + 10];
+            final double absG = Math.abs(work[sixI] + d[i] * work[sixI + 9] / work[sixI + 10]);
-            final double absG = Math.abs(g);
             if (absG < minG) {
                 r = i;
                 minG = absG;

src/main/java/org/apache/commons/math/linear/OpenMapRealMatrix.java

@@ -33,10 +33,10 @@ public class OpenMapRealMatrix extends AbstractRealMatrix implements SparseRealM
     private static final long serialVersionUID = -5962461716457143437L;
 
     /** Number of rows of the matrix. */
-    private final int rowDimension;
+    private final int rows;
 
     /** Number of columns of the matrix. */
-    private final int columnDimension;
+    private final int columns;
 
     /** Storage for (sparse) matrix elements. */
     private final OpenIntToDoubleHashMap entries;
@@ -48,8 +48,8 @@ public class OpenMapRealMatrix extends AbstractRealMatrix implements SparseRealM
      */
     public OpenMapRealMatrix(int rowDimension, int columnDimension) {
         super(rowDimension, columnDimension);
-        this.rowDimension = rowDimension;
+        this.rows    = rowDimension;
-        this.columnDimension = columnDimension;
+        this.columns = columnDimension;
         this.entries = new OpenIntToDoubleHashMap(0.0);
     }
   
@@ -58,8 +58,8 @@ public class OpenMapRealMatrix extends AbstractRealMatrix implements SparseRealM
      * @param matrix matrix to copy
      */
     public OpenMapRealMatrix(OpenMapRealMatrix matrix) {
-        this.rowDimension = matrix.rowDimension;
+        this.rows    = matrix.rows;
-        this.columnDimension = matrix.columnDimension;
+        this.columns = matrix.columns;
         this.entries = new OpenIntToDoubleHashMap(matrix.entries);
     }
   
@@ -79,7 +79,7 @@ public class OpenMapRealMatrix extends AbstractRealMatrix implements SparseRealM
     /** {@inheritDoc} */
     @Override
     public int getColumnDimension() {
-        return columnDimension;
+        return columns;
     }
 
     /** {@inheritDoc} */
@@ -108,8 +108,8 @@ public class OpenMapRealMatrix extends AbstractRealMatrix implements SparseRealM
         final OpenMapRealMatrix out = new OpenMapRealMatrix(this);
         for (OpenIntToDoubleHashMap.Iterator iterator = m.entries.iterator(); iterator.hasNext();) {
             iterator.advance();
-            final int row = iterator.key() / columnDimension;
+            final int row = iterator.key() / columns;
-            final int col = iterator.key() - row * columnDimension;
+            final int col = iterator.key() - row * columns;
             out.setEntry(row, col, getEntry(row, col) + iterator.value());
         }
 
@@ -143,8 +143,8 @@ public class OpenMapRealMatrix extends AbstractRealMatrix implements SparseRealM
         final OpenMapRealMatrix out = new OpenMapRealMatrix(this);
         for (OpenIntToDoubleHashMap.Iterator iterator = m.entries.iterator(); iterator.hasNext();) {
             iterator.advance();
-            final int row = iterator.key() / columnDimension;
+            final int row = iterator.key() / columns;
-            final int col = iterator.key() - row * columnDimension;
+            final int col = iterator.key() - row * columns;
             out.setEntry(row, col, getEntry(row, col) - iterator.value());
         }
 
@@ -164,13 +164,13 @@ public class OpenMapRealMatrix extends AbstractRealMatrix implements SparseRealM
             MatrixUtils.checkMultiplicationCompatible(this, m);
 
             final int outCols = m.getColumnDimension();
-            final BlockRealMatrix out = new BlockRealMatrix(rowDimension, outCols);
+            final BlockRealMatrix out = new BlockRealMatrix(rows, outCols);
             for (OpenIntToDoubleHashMap.Iterator iterator = entries.iterator(); iterator.hasNext();) {
                 iterator.advance();
                 final double value = iterator.value();
                 final int key      = iterator.key();
-                final int i        = key / columnDimension;
+                final int i        = key / columns;
-                final int k        = key % columnDimension;
+                final int k        = key % columns;
                 for (int j = 0; j < outCols; ++j) {
                     out.addToEntry(i, j, value * m.getEntry(k, j));
                 }
@@ -195,13 +195,13 @@ public class OpenMapRealMatrix extends AbstractRealMatrix implements SparseRealM
         MatrixUtils.checkMultiplicationCompatible(this, m);
 
         final int outCols = m.getColumnDimension();
-        OpenMapRealMatrix out = new OpenMapRealMatrix(rowDimension, outCols);
+        OpenMapRealMatrix out = new OpenMapRealMatrix(rows, outCols);
         for (OpenIntToDoubleHashMap.Iterator iterator = entries.iterator(); iterator.hasNext();) {
             iterator.advance();
             final double value = iterator.value();
             final int key      = iterator.key();
-            final int i        = key / columnDimension;
+            final int i        = key / columns;
-            final int k        = key % columnDimension;
+            final int k        = key % columns;
             for (int j = 0; j < outCols; ++j) {
                 final int rightKey = m.computeKey(k, j);
                 if (m.entries.containsKey(rightKey)) {
@@ -232,7 +232,7 @@ public class OpenMapRealMatrix extends AbstractRealMatrix implements SparseRealM
     /** {@inheritDoc} */
     @Override
     public int getRowDimension() {
-        return rowDimension;
+        return rows;
     }
 
     /** {@inheritDoc} */
@@ -285,7 +285,7 @@ public class OpenMapRealMatrix extends AbstractRealMatrix implements SparseRealM
      * @return key within the map to access the matrix element
      */
     private int computeKey(int row, int column) {
-        return row * columnDimension + column;
+        return row * columns + column;
     }
 
 

src/main/java/org/apache/commons/math/linear/SparseFieldMatrix.java

@@ -37,11 +37,11 @@ public class SparseFieldMatrix<T extends FieldElement<T>> extends AbstractFieldM
     /**
      * row dimension
      */
-    private final int rowDimension;
+    private final int rows;
     /**
      * column dimension
      */
-    private final int columnDimension;
+    private final int columns;
     
 
     /**
@@ -50,8 +50,8 @@ public class SparseFieldMatrix<T extends FieldElement<T>> extends AbstractFieldM
      */
     public SparseFieldMatrix(final Field<T> field) {
         super(field);
-        rowDimension = 0;
+        rows = 0;
-        columnDimension= 0;
+        columns= 0;
         entries = new OpenIntToFieldHashMap<T>(field);
     }
 
@@ -67,8 +67,8 @@ public class SparseFieldMatrix<T extends FieldElement<T>> extends AbstractFieldM
                              final int rowDimension, final int columnDimension)
         throws IllegalArgumentException {
         super(field, rowDimension, columnDimension);
-        this.rowDimension = rowDimension;
+        this.rows = rowDimension;
-        this.columnDimension = columnDimension;
+        this.columns = columnDimension;
         entries = new OpenIntToFieldHashMap<T>(field);
     }
     
@@ -78,8 +78,8 @@ public class SparseFieldMatrix<T extends FieldElement<T>> extends AbstractFieldM
      */
     public SparseFieldMatrix(SparseFieldMatrix<T> other) {
         super(other.getField(), other.getRowDimension(), other.getColumnDimension());
-        rowDimension = other.getRowDimension();
+        rows = other.getRowDimension();
-        columnDimension = other.getColumnDimension();
+        columns = other.getColumnDimension();
         entries = new OpenIntToFieldHashMap<T>(other.entries);
     }
 
@@ -89,11 +89,11 @@ public class SparseFieldMatrix<T extends FieldElement<T>> extends AbstractFieldM
      */
     public SparseFieldMatrix(FieldMatrix<T> other){
         super(other.getField(), other.getRowDimension(), other.getColumnDimension());
-        rowDimension = other.getRowDimension();
+        rows = other.getRowDimension();
-        columnDimension = other.getColumnDimension();
+        columns = other.getColumnDimension();
         entries = new OpenIntToFieldHashMap<T>(getField());
-        for (int i = 0; i < rowDimension; i++) {
+        for (int i = 0; i < rows; i++) {
-            for (int j = 0; j < columnDimension; j++) {
+            for (int j = 0; j < columns; j++) {
                 setEntry(i, j, other.getEntry(i, j));
             }
         }
@@ -131,7 +131,7 @@ public class SparseFieldMatrix<T extends FieldElement<T>> extends AbstractFieldM
     /** {@inheritDoc} */
     @Override
     public int getColumnDimension() {
-        return columnDimension;
+        return columns;
     }
 
     /** {@inheritDoc} */
@@ -145,7 +145,7 @@ public class SparseFieldMatrix<T extends FieldElement<T>> extends AbstractFieldM
     /** {@inheritDoc} */
     @Override
     public int getRowDimension() {
-        return rowDimension;
+        return rows;
     }
 
     /** {@inheritDoc} */
@@ -184,7 +184,7 @@ public class SparseFieldMatrix<T extends FieldElement<T>> extends AbstractFieldM
      * @return key within the map to access the matrix element
      */
     private int computeKey(int row, int column) {
-        return row * columnDimension + column;
+        return row * columns + column;
     }
 
 }

src/main/java/org/apache/commons/math/ode/AbstractIntegrator.java

@@ -184,30 +184,30 @@ public abstract class AbstractIntegrator implements FirstOrderIntegrator {
     }
 
     /** Perform some sanity checks on the integration parameters.
-     * @param equations differential equations set
+     * @param ode differential equations set
      * @param t0 start time
      * @param y0 state vector at t0
      * @param t target time for the integration
      * @param y placeholder where to put the state vector
      * @exception IntegratorException if some inconsistency is detected
      */
-    protected void sanityChecks(final FirstOrderDifferentialEquations equations,
+    protected void sanityChecks(final FirstOrderDifferentialEquations ode,
                                 final double t0, final double[] y0,
                                 final double t, final double[] y)
         throws IntegratorException {
 
-        if (equations.getDimension() != y0.length) {
+        if (ode.getDimension() != y0.length) {
             throw new IntegratorException(
                     "dimensions mismatch: ODE problem has dimension {0}," +
                     " initial state vector has dimension {1}",
-                    equations.getDimension(), y0.length);
+                    ode.getDimension(), y0.length);
         }
 
-        if (equations.getDimension() != y.length) {
+        if (ode.getDimension() != y.length) {
             throw new IntegratorException(
                     "dimensions mismatch: ODE problem has dimension {0}," +
                     " final state vector has dimension {1}",
-                    equations.getDimension(), y.length);
+                    ode.getDimension(), y.length);
         }
 
         if (Math.abs(t - t0) <= 1.0e-12 * Math.max(Math.abs(t0), Math.abs(t))) {

src/main/java/org/apache/commons/math/ode/MultistepIntegrator.java

@@ -183,10 +183,10 @@ public abstract class MultistepIntegrator extends AdaptiveStepsizeIntegrator {
      * <p>The various step and event handlers for this starter integrator
      * will be managed automatically by the multi-step integrator. Any
      * user configuration for these elements will be cleared before use.</p>
-     * @param starter starter integrator
+     * @param starterIntegrator starter integrator
      */
-    public void setStarterIntegrator(FirstOrderIntegrator starter) {
+    public void setStarterIntegrator(FirstOrderIntegrator starterIntegrator) {
-        this.starter = starter;
+        this.starter = starterIntegrator;
     }
 
     /** Start the integration.

src/main/java/org/apache/commons/math/ode/events/EventState.java

@@ -138,17 +138,17 @@ public class EventState {
     }
 
     /** Reinitialize the beginning of the step.
-     * @param t0 value of the independent <i>time</i> variable at the
+     * @param tStart value of the independent <i>time</i> variable at the
      * beginning of the step
-     * @param y0 array containing the current value of the state vector
+     * @param yStart array containing the current value of the state vector
      * at the beginning of the step
      * @exception EventException if the event handler
      * value cannot be evaluated at the beginning of the step
      */
-    public void reinitializeBegin(final double t0, final double[] y0)
+    public void reinitializeBegin(final double tStart, final double[] yStart)
         throws EventException {
-        this.t0 = t0;
+        t0 = tStart;
-        g0 = handler.g(t0, y0);
+        g0 = handler.g(tStart, yStart);
         g0Positive = (g0 >= 0);
     }
 

src/main/java/org/apache/commons/math/ode/nonstiff/GraggBulirschStoerIntegrator.java

@@ -150,29 +150,29 @@ public class GraggBulirschStoerIntegrator extends AdaptiveStepsizeIntegrator {
    * <p>By default, the test is performed, at most during two
    * iterations at each step, and at most once for each of these
    * iterations. The default stepsize reduction factor is 0.5.</p>
-   * @param performTest if true, stability check will be performed,
+   * @param performStabilityCheck if true, stability check will be performed,
      if false, the check will be skipped
-   * @param maxIter maximal number of iterations for which checks are
+   * @param maxNumIter maximal number of iterations for which checks are
    * performed (the number of iterations is reset to default if negative
    * or null)
-   * @param maxChecks maximal number of checks for each iteration
+   * @param maxNumChecks maximal number of checks for each iteration
    * (the number of checks is reset to default if negative or null)
-   * @param stabilityReduction stepsize reduction factor in case of
+   * @param stepsizeReductionFactor stepsize reduction factor in case of
    * failure (the factor is reset to default if lower than 0.0001 or
    * greater than 0.9999)
    */
-  public void setStabilityCheck(final boolean performTest,
+  public void setStabilityCheck(final boolean performStabilityCheck,
-                                final int maxIter, final int maxChecks,
+                                final int maxNumIter, final int maxNumChecks,
-                                final double stabilityReduction) {
+                                final double stepsizeReductionFactor) {
 
-    this.performTest = performTest;
+    this.performTest = performStabilityCheck;
-    this.maxIter     = (maxIter   <= 0) ? 2 : maxIter;
+    this.maxIter     = (maxNumIter   <= 0) ? 2 : maxNumIter;
-    this.maxChecks   = (maxChecks <= 0) ? 1 : maxChecks;
+    this.maxChecks   = (maxNumChecks <= 0) ? 1 : maxNumChecks;
 
-    if ((stabilityReduction < 0.0001) || (stabilityReduction > 0.9999)) {
+    if ((stepsizeReductionFactor < 0.0001) || (stepsizeReductionFactor > 0.9999)) {
       this.stabilityReduction = 0.5;
     } else {
-      this.stabilityReduction = stabilityReduction;
+      this.stabilityReduction = stepsizeReductionFactor;
     }
 
   }
@@ -192,40 +192,40 @@ public class GraggBulirschStoerIntegrator extends AdaptiveStepsizeIntegrator {
    * </pre>
    * The default values are 0.02 for stepControl3 and 4.0 for
    * stepControl4.</p>
-   * @param stepControl1 first stepsize control factor (the factor is
+   * @param control1 first stepsize control factor (the factor is
    * reset to default if lower than 0.0001 or greater than 0.9999)
-   * @param stepControl2 second stepsize control factor (the factor
+   * @param control2 second stepsize control factor (the factor
    * is reset to default if lower than 0.0001 or greater than 0.9999)
-   * @param stepControl3 third stepsize control factor (the factor is
+   * @param control3 third stepsize control factor (the factor is
    * reset to default if lower than 0.0001 or greater than 0.9999)
-   * @param stepControl4 fourth stepsize control factor (the factor
+   * @param control4 fourth stepsize control factor (the factor
    * is reset to default if lower than 1.0001 or greater than 999.9)
    */
-  public void setStepsizeControl(final double stepControl1, final double stepControl2,
+  public void setStepsizeControl(final double control1, final double control2,
-                                 final double stepControl3, final double stepControl4) {
+                                 final double control3, final double control4) {
 
-    if ((stepControl1 < 0.0001) || (stepControl1 > 0.9999)) {
+    if ((control1 < 0.0001) || (control1 > 0.9999)) {
       this.stepControl1 = 0.65;
     } else {
-      this.stepControl1 = stepControl1;
+      this.stepControl1 = control1;
     }
 
-    if ((stepControl2 < 0.0001) || (stepControl2 > 0.9999)) {
+    if ((control2 < 0.0001) || (control2 > 0.9999)) {
       this.stepControl2 = 0.94;
     } else {
-      this.stepControl2 = stepControl2;
+      this.stepControl2 = control2;
     }
 
-    if ((stepControl3 < 0.0001) || (stepControl3 > 0.9999)) {
+    if ((control3 < 0.0001) || (control3 > 0.9999)) {
       this.stepControl3 = 0.02;
     } else {
-      this.stepControl3 = stepControl3;
+      this.stepControl3 = control3;
     }
 
-    if ((stepControl4 < 1.0001) || (stepControl4 > 999.9)) {
+    if ((control4 < 1.0001) || (control4 > 999.9)) {
       this.stepControl4 = 4.0;
     } else {
-      this.stepControl4 = stepControl4;
+      this.stepControl4 = control4;
     }
 
   }
@@ -246,30 +246,30 @@ public class GraggBulirschStoerIntegrator extends AdaptiveStepsizeIntegrator {
    * <p>The default maximal order after construction is 18 (i.e. the
    * maximal number of columns is 9). The default values are 0.8 for
    * orderControl1 and 0.9 for orderControl2.</p>
-   * @param maxOrder maximal order in the extrapolation table (the
+   * @param maximalOrder maximal order in the extrapolation table (the
    * maximal order is reset to default if order <= 6 or odd)
-   * @param orderControl1 first order control factor (the factor is
+   * @param control1 first order control factor (the factor is
    * reset to default if lower than 0.0001 or greater than 0.9999)
-   * @param orderControl2 second order control factor (the factor
+   * @param control2 second order control factor (the factor
    * is reset to default if lower than 0.0001 or greater than 0.9999)
    */
-  public void setOrderControl(final int maxOrder,
+  public void setOrderControl(final int maximalOrder,
-                              final double orderControl1, final double orderControl2) {
+                              final double control1, final double control2) {
 
-    if ((maxOrder <= 6) || (maxOrder % 2 != 0)) {
+    if ((maximalOrder <= 6) || (maximalOrder % 2 != 0)) {
       this.maxOrder = 18;
     }
 
-    if ((orderControl1 < 0.0001) || (orderControl1 > 0.9999)) {
+    if ((control1 < 0.0001) || (control1 > 0.9999)) {
       this.orderControl1 = 0.8;
     } else {
-      this.orderControl1 = orderControl1;
+      this.orderControl1 = control1;
     }
 
-    if ((orderControl2 < 0.0001) || (orderControl2 > 0.9999)) {
+    if ((control2 < 0.0001) || (control2 > 0.9999)) {
       this.orderControl2 = 0.9;
     } else {
-      this.orderControl2 = orderControl2;
+      this.orderControl2 = control2;
     }
 
     // reinitialize the arrays
@@ -352,20 +352,20 @@ public class GraggBulirschStoerIntegrator extends AdaptiveStepsizeIntegrator {
    * default value for mudif is 4 and the interpolation error is used
    * in stepsize control by default.
 
-   * @param useInterpolationError if true, interpolation error is used
+   * @param useInterpolationErrorForControl if true, interpolation error is used
    * for stepsize control
-   * @param mudif interpolation order control parameter (the parameter
+   * @param mudifControlParameter interpolation order control parameter (the parameter
    * is reset to default if <= 0 or >= 7)
    */
-  public void setInterpolationControl(final boolean useInterpolationError,
+  public void setInterpolationControl(final boolean useInterpolationErrorForControl,
-                                      final int mudif) {
+                                      final int mudifControlParameter) {
 
-    this.useInterpolationError = useInterpolationError;
+    this.useInterpolationError = useInterpolationErrorForControl;
 
-    if ((mudif <= 0) || (mudif >= 7)) {
+    if ((mudifControlParameter <= 0) || (mudifControlParameter >= 7)) {
       this.mudif = 4;
     } else {
-      this.mudif = mudif;
+      this.mudif = mudifControlParameter;
     }
 
   }

src/main/java/org/apache/commons/math/ode/nonstiff/RungeKuttaStepInterpolator.java

@@ -108,18 +108,18 @@ abstract class RungeKuttaStepInterpolator
    * {@link AbstractStepInterpolator#getInterpolatedState
    * getInterpolatedState} method (for an interpolator which needs a
    * finalization) or if it clones the step interpolator.</p>
-   * @param integrator integrator being used
+   * @param rkIntegrator integrator being used
    * @param y reference to the integrator array holding the state at
    * the end of the step
-   * @param yDotK reference to the integrator array holding all the
+   * @param yDotArray reference to the integrator array holding all the
    * intermediate slopes
    * @param forward integration direction indicator
    */
-  public void reinitialize(final AbstractIntegrator integrator,
+  public void reinitialize(final AbstractIntegrator rkIntegrator,
-                           final double[] y, final double[][] yDotK, final boolean forward) {
+                           final double[] y, final double[][] yDotArray, final boolean forward) {
     reinitialize(y, forward);
-    this.yDotK = yDotK;
+    this.yDotK = yDotArray;
-    this.integrator = integrator;
+    this.integrator = rkIntegrator;
   }
 
   /** {@inheritDoc} */

src/main/java/org/apache/commons/math/ode/sampling/AbstractStepInterpolator.java

@@ -164,9 +164,9 @@ public abstract class AbstractStepInterpolator
   /** Reinitialize the instance
    * @param y reference to the integrator array holding the state at
    * the end of the step
-   * @param forward integration direction indicator
+   * @param isForward integration direction indicator
    */
-  protected void reinitialize(final double[] y, final boolean forward) {
+  protected void reinitialize(final double[] y, final boolean isForward) {
 
     previousTime      = Double.NaN;
     currentTime       = Double.NaN;
@@ -178,7 +178,7 @@ public abstract class AbstractStepInterpolator
     interpolatedDerivatives = new double[y.length];
 
     finalized         = false;
-    this.forward      = forward;
+    this.forward      = isForward;
     this.dirtyState   = true;
 
   }

src/main/java/org/apache/commons/math/ode/sampling/NordsieckStepInterpolator.java

@@ -114,19 +114,20 @@ public class NordsieckStepInterpolator extends AbstractStepInterpolator {
     /** Reinitialize the instance.
      * <p>Beware that all arrays <em>must</em> be references to integrator
      * arrays, in order to ensure proper update without copy.</p>
-     * @param referenceTime time at which all arrays are defined
+     * @param time time at which all arrays are defined
-     * @param scalingH step size used in the scaled and nordsieck arrays
+     * @param stepSize step size used in the scaled and nordsieck arrays
-     * @param scaled reference to the integrator array holding the first
+     * @param scaledDerivative reference to the integrator array holding the first
      * scaled derivative
-     * @param nordsieck reference to the integrator matrix holding the
+     * @param nordsieckVector reference to the integrator matrix holding the
      * nordsieck vector
      */
-    public void reinitialize(final double referenceTime, final double scalingH,
+    public void reinitialize(final double time, final double stepSize,
-                             final double[] scaled, final Array2DRowRealMatrix nordsieck) {
+                             final double[] scaledDerivative,
-        this.referenceTime = referenceTime;
+                             final Array2DRowRealMatrix nordsieckVector) {
-        this.scalingH      = scalingH;
+        this.referenceTime = time;
-        this.scaled        = scaled;
+        this.scalingH      = stepSize;
-        this.nordsieck     = nordsieck;
+        this.scaled        = scaledDerivative;
+        this.nordsieck     = nordsieckVector;
 
         // make sure the state and derivatives will depend on the new arrays
         setInterpolatedTime(getInterpolatedTime());
@@ -136,11 +137,11 @@ public class NordsieckStepInterpolator extends AbstractStepInterpolator {
     /** Rescale the instance.
      * <p>Since the scaled and Nordiseck arrays are shared with the caller,
      * this method has the side effect of rescaling this arrays in the caller too.</p>
-     * @param scalingH new step size to use in the scaled and nordsieck arrays
+     * @param stepSize new step size to use in the scaled and nordsieck arrays
      */
-    public void rescale(final double scalingH) {
+    public void rescale(final double stepSize) {
 
-        final double ratio = scalingH / this.scalingH;
+        final double ratio = stepSize / scalingH;
         for (int i = 0; i < scaled.length; ++i) {
             scaled[i] *= ratio;
         }
@@ -155,7 +156,7 @@ public class NordsieckStepInterpolator extends AbstractStepInterpolator {
             }
         }
 
-        this.scalingH = scalingH;
+        scalingH = stepSize;
 
     }
 

src/main/java/org/apache/commons/math/optimization/direct/DirectSearchOptimizer.java

@@ -246,8 +246,8 @@ public abstract class DirectSearchOptimizer implements MultivariateRealOptimizer
     }
 
     /** {@inheritDoc} */
-    public void setConvergenceChecker(RealConvergenceChecker checker) {
+    public void setConvergenceChecker(RealConvergenceChecker convergenceChecker) {
-        this.checker = checker;
+        this.checker = convergenceChecker;
     }
 
     /** {@inheritDoc} */
@@ -256,9 +256,9 @@ public abstract class DirectSearchOptimizer implements MultivariateRealOptimizer
     }
 
     /** {@inheritDoc} */
-    public RealPointValuePair optimize(final MultivariateRealFunction f,
+    public RealPointValuePair optimize(final MultivariateRealFunction function,
-                                         final GoalType goalType,
+                                       final GoalType goalType,
-                                         final double[] startPoint)
+                                       final double[] startPoint)
         throws FunctionEvaluationException, OptimizationException,
         IllegalArgumentException {
 
@@ -270,7 +270,7 @@ public abstract class DirectSearchOptimizer implements MultivariateRealOptimizer
             setStartConfiguration(unit);
         }
 
-        this.f = f;
+        this.f = function;
         final Comparator<RealPointValuePair> comparator =
             new Comparator<RealPointValuePair>() {
                 public int compare(final RealPointValuePair o1,

src/main/java/org/apache/commons/math/optimization/general/AbstractLeastSquaresOptimizer.java

@@ -79,16 +79,16 @@ public abstract class AbstractLeastSquaresOptimizer implements DifferentiableMul
     protected int rows;
 
     /** Objective function. */
-    private DifferentiableMultivariateVectorialFunction f;
+    private DifferentiableMultivariateVectorialFunction function;
 
     /** Objective function derivatives. */
     private MultivariateMatrixFunction jF;
 
     /** Target value for the objective functions at optimum. */
-    protected double[] target;
+    protected double[] targetValues;
 
     /** Weight for the least squares cost computation. */
-    protected double[] weights;
+    protected double[] residualsWeights;
 
     /** Current point. */
     protected double[] point;
@@ -148,8 +148,8 @@ public abstract class AbstractLeastSquaresOptimizer implements DifferentiableMul
     }
 
     /** {@inheritDoc} */
-    public void setConvergenceChecker(VectorialConvergenceChecker checker) {
+    public void setConvergenceChecker(VectorialConvergenceChecker convergenceChecker) {
-        this.checker = checker;
+        this.checker = convergenceChecker;
     }
 
     /** {@inheritDoc} */
@@ -182,7 +182,7 @@ public abstract class AbstractLeastSquaresOptimizer implements DifferentiableMul
         }
         for (int i = 0; i < rows; i++) {
             final double[] ji = jacobian[i];
-            final double factor = -Math.sqrt(weights[i]);
+            final double factor = -Math.sqrt(residualsWeights[i]);
             for (int j = 0; j < cols; ++j) {
                 ji[j] *= factor;
             }
@@ -202,16 +202,16 @@ public abstract class AbstractLeastSquaresOptimizer implements DifferentiableMul
             throw new FunctionEvaluationException(new MaxEvaluationsExceededException(maxEvaluations),
                                                   point);
         }
-        objective = f.value(point);
+        objective = function.value(point);
         if (objective.length != rows) {
             throw new FunctionEvaluationException(point, "dimension mismatch {0} != {1}",
                                                   objective.length, rows);
         }
         cost = 0;
         for (int i = 0, index = 0; i < rows; i++, index += cols) {
-            final double residual = target[i] - objective[i];
+            final double residual = targetValues[i] - objective[i];
             residuals[i] = residual;
-            cost += weights[i] * residual * residual;
+            cost += residualsWeights[i] * residual * residual;
         }
         cost = Math.sqrt(cost);
 
@@ -231,7 +231,7 @@ public abstract class AbstractLeastSquaresOptimizer implements DifferentiableMul
         double criterion = 0;
         for (int i = 0; i < rows; ++i) {
             final double residual = residuals[i];
-            criterion += weights[i] * residual * residual;
+            criterion += residualsWeights[i] * residual * residual;
         }
         return Math.sqrt(criterion / rows);
     }
@@ -244,7 +244,7 @@ public abstract class AbstractLeastSquaresOptimizer implements DifferentiableMul
         double chiSquare = 0;
         for (int i = 0; i < rows; ++i) {
             final double residual = residuals[i];
-            chiSquare += residual * residual / weights[i];
+            chiSquare += residual * residual / residualsWeights[i];
         }
         return chiSquare;
     }
@@ -329,12 +329,12 @@ public abstract class AbstractLeastSquaresOptimizer implements DifferentiableMul
         jacobianEvaluations  = 0;
 
         // store least squares problem characteristics
-        this.f         = f;
+        function         = f;
-        jF             = f.jacobian();
+        jF               = f.jacobian();
-        this.target    = target.clone();
+        targetValues     = target.clone();
-        this.weights   = weights.clone();
+        residualsWeights = weights.clone();
-        this.point     = startPoint.clone();
+        this.point       = startPoint.clone();
-        this.residuals = new double[target.length];
+        this.residuals   = new double[target.length];
 
         // arrays shared with the other private methods
         rows      = target.length;

src/main/java/org/apache/commons/math/optimization/general/AbstractScalarDifferentiableOptimizer.java

@@ -61,13 +61,13 @@ public abstract class AbstractScalarDifferentiableOptimizer
     protected RealConvergenceChecker checker;
 
     /** Objective function. */
-    private DifferentiableMultivariateRealFunction f;
+    private DifferentiableMultivariateRealFunction function;
 
     /** Objective function gradient. */
     private MultivariateVectorialFunction gradient;
 
     /** Type of optimization. */
-    protected GoalType goalType;
+    protected GoalType goal;
 
     /** Current point set. */
     protected double[] point;
@@ -118,8 +118,8 @@ public abstract class AbstractScalarDifferentiableOptimizer
     }
 
     /** {@inheritDoc} */
-    public void setConvergenceChecker(RealConvergenceChecker checker) {
+    public void setConvergenceChecker(RealConvergenceChecker convergenceChecker) {
-        this.checker = checker;
+        this.checker = convergenceChecker;
     }
 
     /** {@inheritDoc} */
@@ -140,31 +140,31 @@ public abstract class AbstractScalarDifferentiableOptimizer
 
     /** 
      * Compute the gradient vector.
-     * @param point point at which the gradient must be evaluated
+     * @param evaluationPoint point at which the gradient must be evaluated
      * @return gradient at the specified point
      * @exception FunctionEvaluationException if the function gradient
      */
-    protected double[] computeObjectiveGradient(final double[] point)
+    protected double[] computeObjectiveGradient(final double[] evaluationPoint)
         throws FunctionEvaluationException {
         ++gradientEvaluations;
-        return gradient.value(point);
+        return gradient.value(evaluationPoint);
     }
 
     /** 
      * Compute the objective function value.
-     * @param point point at which the objective function must be evaluated
+     * @param evaluationPoint point at which the objective function must be evaluated
      * @return objective function value at specified point
      * @exception FunctionEvaluationException if the function cannot be evaluated
      * or its dimension doesn't match problem dimension or the maximal number
      * of iterations is exceeded
      */
-    protected double computeObjectiveValue(final double[] point)
+    protected double computeObjectiveValue(final double[] evaluationPoint)
         throws FunctionEvaluationException {
         if (++evaluations > maxEvaluations) {
             throw new FunctionEvaluationException(new MaxEvaluationsExceededException(maxEvaluations),
-                                                  point);
+                                                  evaluationPoint);
         }
-        return f.value(point);
+        return function.value(evaluationPoint);
     }
 
     /** {@inheritDoc} */
@@ -179,10 +179,10 @@ public abstract class AbstractScalarDifferentiableOptimizer
         gradientEvaluations = 0;
 
         // store optimization problem characteristics
-        this.f        = f;
+        function = f;
-        gradient      = f.gradient();
+        gradient = f.gradient();
-        this.goalType = goalType;
+        goal     = goalType;
-        point         = startPoint.clone();
+        point    = startPoint.clone();
 
         return doOptimize();
 

src/main/java/org/apache/commons/math/optimization/general/GaussNewtonOptimizer.java

@@ -81,8 +81,8 @@ public class GaussNewtonOptimizer extends AbstractLeastSquaresOptimizer {
             for (int i = 0; i < rows; ++i) {
 
                 final double[] grad   = jacobian[i];
-                final double weight   = weights[i];
+                final double weight   = residualsWeights[i];
-                final double residual = objective[i] - target[i];
+                final double residual = objective[i] - targetValues[i];
 
                 // compute the normal equation
                 final double wr = weight * residual;

src/main/java/org/apache/commons/math/optimization/general/NonLinearConjugateGradientOptimizer.java

@@ -83,12 +83,12 @@ public class NonLinearConjugateGradientOptimizer
 
     /**
      * Set the solver to use during line search.
-     * @param solver solver to use during line search, may be null
+     * @param lineSearchSolver solver to use during line search, may be null
      * to remove an already registered solver and fall back to the
      * default {@link BrentSolver Brent solver}.
      */
-    public void setLineSearchSolver(final UnivariateRealSolver solver) {
+    public void setLineSearchSolver(final UnivariateRealSolver lineSearchSolver) {
-        this.solver = solver;
+        this.solver = lineSearchSolver;
     }
 
     /**
@@ -124,7 +124,7 @@ public class NonLinearConjugateGradientOptimizer
             }
             final int n = point.length;
             double[] r = computeObjectiveGradient(point);
-            if (goalType == GoalType.MINIMIZE) {
+            if (goal == GoalType.MINIMIZE) {
                 for (int i = 0; i < n; ++i) {
                     r[i] = -r[i];
                 }
@@ -168,7 +168,7 @@ public class NonLinearConjugateGradientOptimizer
                     point[i] += step * searchDirection[i];
                 }
                 r = computeObjectiveGradient(point);
-                if (goalType == GoalType.MINIMIZE) {
+                if (goal == GoalType.MINIMIZE) {
                     for (int i = 0; i < n; ++i) {
                         r[i] = -r[i];
                     }

src/main/java/org/apache/commons/math/optimization/linear/AbstractLinearOptimizer.java

@@ -44,16 +44,16 @@ public abstract class AbstractLinearOptimizer implements LinearOptimizer {
     private int iterations;
 
     /** Linear objective function. */
-    protected LinearObjectiveFunction f;
+    protected LinearObjectiveFunction function;
 
     /** Linear constraints. */
-    protected Collection<LinearConstraint> constraints;
+    protected Collection<LinearConstraint> linearConstraints;
 
     /** Type of optimization goal: either {@link GoalType#MAXIMIZE} or {@link GoalType#MINIMIZE}. */
-    protected GoalType goalType;
+    protected GoalType goal;
 
     /** Whether to restrict the variables to non-negative values. */
-    protected boolean restrictToNonNegative;
+    protected boolean nonNegative;
 
     /** Simple constructor with default settings.
      * <p>The maximal number of evaluation is set to its default value.</p>
@@ -95,10 +95,10 @@ public abstract class AbstractLinearOptimizer implements LinearOptimizer {
          throws OptimizationException {
 
         // store linear problem characteristics
-        this.f                     = f;
+        this.function          = f;
-        this.constraints           = constraints;
+        this.linearConstraints = constraints;
-        this.goalType              = goalType;
+        this.goal              = goalType;
-        this.restrictToNonNegative = restrictToNonNegative;
+        this.nonNegative       = restrictToNonNegative;
 
         iterations  = 0;
 

src/main/java/org/apache/commons/math/optimization/linear/SimplexSolver.java

@@ -184,7 +184,7 @@ public class SimplexSolver extends AbstractLinearOptimizer {
     public RealPointValuePair doOptimize()
         throws OptimizationException {
         final SimplexTableau tableau =
-            new SimplexTableau(f, constraints, goalType, restrictToNonNegative, epsilon);
+            new SimplexTableau(function, linearConstraints, goal, nonNegative, epsilon);
         solvePhase1(tableau);
         tableau.discardArtificialVariables();
         while (!isOptimal(tableau)) {

src/main/java/org/apache/commons/math/optimization/linear/SimplexTableau.java

@@ -192,12 +192,12 @@ class SimplexTableau implements Serializable {
 
     /**
      * Get new versions of the constraints which have positive right hand sides.
-     * @param constraints original (not normalized) constraints
+     * @param originalConstraints original (not normalized) constraints
      * @return new versions of the constraints
      */
-    public List<LinearConstraint> normalizeConstraints(Collection<LinearConstraint> constraints) {
+    public List<LinearConstraint> normalizeConstraints(Collection<LinearConstraint> originalConstraints) {
         List<LinearConstraint> normalized = new ArrayList<LinearConstraint>();
-        for (LinearConstraint constraint : constraints) {
+        for (LinearConstraint constraint : originalConstraints) {
             normalized.add(normalize(constraint));
         }
         return normalized;

src/main/java/org/apache/commons/math/random/RandomDataImpl.java

@@ -183,8 +183,7 @@ public class RandomDataImpl implements RandomData, Serializable {
                     "upper bound ({0}) must be greater than lower bound ({1})",
                     upper, lower);
         }
-        RandomGenerator rand = getRan();
+        double r = getRan().nextDouble();
-        double r = rand.nextDouble();
         return (int) ((r * upper) + ((1.0 - r) * lower) + r);
     }
 
@@ -204,8 +203,7 @@ public class RandomDataImpl implements RandomData, Serializable {
                   "upper bound ({0}) must be greater than lower bound ({1})",
                   upper, lower);
         }
-        RandomGenerator rand = getRan();
+        double r = getRan().nextDouble();
-        double r = rand.nextDouble();
         return (long) ((r * upper) + ((1.0 - r) * lower) + r);
     }
 
@@ -356,7 +354,7 @@ public class RandomDataImpl implements RandomData, Serializable {
                   "the Poisson mean must be positive ({0})", mean);
         }
 
-        RandomGenerator rand = getRan();
+        final RandomGenerator generator = getRan();
 
         double pivot = 6.0;
         if (mean < pivot) {
@@ -366,7 +364,7 @@ public class RandomDataImpl implements RandomData, Serializable {
             double rnd = 1.0d;
 
             while (n < 1000 * mean) {
-                rnd = rand.nextDouble();
+                rnd = generator.nextDouble();
                 r = r * rnd;
                 if (r >= p) {
                     n++;
@@ -458,8 +456,7 @@ public class RandomDataImpl implements RandomData, Serializable {
             throw MathRuntimeException.createIllegalArgumentException(
                   "standard deviation must be positive ({0})", sigma);
         }
-        RandomGenerator rand = getRan();
+        return sigma * getRan().nextGaussian() + mu;
-        return sigma * rand.nextGaussian() + mu;
     }
 
     /**
@@ -481,10 +478,10 @@ public class RandomDataImpl implements RandomData, Serializable {
             throw MathRuntimeException.createIllegalArgumentException(
                   "mean must be positive ({0})", mean);
         }
-        RandomGenerator rand = getRan();
+        final RandomGenerator generator = getRan();
-        double unif = rand.nextDouble();
+        double unif = generator.nextDouble();
         while (unif == 0.0d) {
-            unif = rand.nextDouble();
+            unif = generator.nextDouble();
         }
         return -mean * Math.log(unif);
     }
@@ -511,12 +508,12 @@ public class RandomDataImpl implements RandomData, Serializable {
                   "upper bound ({0}) must be greater than lower bound ({1})",
                   upper, lower);
         }
-        RandomGenerator rand = getRan();
+        final RandomGenerator generator = getRan();
 
         // ensure nextDouble() isn't 0.0
-        double u = rand.nextDouble();
+        double u = generator.nextDouble();
         while (u <= 0.0) {
-            u = rand.nextDouble();
+            u = generator.nextDouble();
         }
 
         return lower + u * (upper - lower);

src/main/java/org/apache/commons/math/stat/correlation/SpearmansCorrelation.java

@@ -124,11 +124,11 @@ public class SpearmansCorrelation {
      * input rectangular array.  The columns of the array represent values
      * of variables to be correlated.
      * 
-     * @param data matrix with columns representing variables to correlate
+     * @param matrix matrix with columns representing variables to correlate
      * @return correlation matrix
      */
-    public RealMatrix computeCorrelationMatrix(double[][] data) {
+    public RealMatrix computeCorrelationMatrix(double[][] matrix) {
-       return computeCorrelationMatrix(new BlockRealMatrix(data));
+       return computeCorrelationMatrix(new BlockRealMatrix(matrix));
     }
     
     /**

src/main/java/org/apache/commons/math/stat/descriptive/moment/Variance.java

@@ -481,10 +481,10 @@ public class Variance extends AbstractStorelessUnivariateStatistic implements Se
     }
 
     /**
-     * @param isBiasCorrected The isBiasCorrected to set.
+     * @param biasCorrected The isBiasCorrected to set.
      */
-    public void setBiasCorrected(boolean isBiasCorrected) {
+    public void setBiasCorrected(boolean biasCorrected) {
-        this.isBiasCorrected = isBiasCorrected;
+        this.isBiasCorrected = biasCorrected;
     }
 
     /**

src/main/java/org/apache/commons/math/stat/inference/TestUtils.java

@@ -53,11 +53,11 @@ public class TestUtils  {
     /**
      * Set the (singleton) TTest instance.
      * 
-     * @param tTest the new instance to use
+     * @param chiSquareTest the new instance to use
      * @since 1.2
      */
-    public static void setChiSquareTest(TTest tTest) {
+    public static void setChiSquareTest(TTest chiSquareTest) {
-        TestUtils.tTest = tTest;
+        TestUtils.tTest = chiSquareTest;
     }
     
     /**

src/main/java/org/apache/commons/math/util/ResizableDoubleArray.java

@@ -337,35 +337,33 @@ public class ResizableDoubleArray implements DoubleArray, Serializable {
      * IllegalArgumentException if the contractionCriteria is less than the 
      * expansionCriteria
      * 
-     * @param expansionFactor factor to be checked
+     * @param expansion factor to be checked
-     * @param contractionCriteria criteria to be checked
+     * @param contraction criteria to be checked
      * @throws IllegalArgumentException if the contractionCriteria is less than
      *         the expansionCriteria.
      */
-    protected void checkContractExpand(
+    protected void checkContractExpand(float contraction, float expansion) {
-        float contractionCriteria,
-        float expansionFactor) {
 
-        if (contractionCriteria < expansionFactor) {
+        if (contraction < expansion) {
             throw MathRuntimeException.createIllegalArgumentException(
                     "contraction criteria ({0}) smaller than the expansion factor ({1}).  This would " +
                     "lead to a never ending loop of expansion and contraction as a newly expanded " +
                     "internal storage array would immediately satisfy the criteria for contraction",
-                    contractionCriteria, expansionFactor);
+                    contraction, expansion);
         }
 
-        if (contractionCriteria <= 1.0) {
+        if (contraction <= 1.0) {
             throw MathRuntimeException.createIllegalArgumentException(
                     "contraction criteria smaller than one ({0}).  This would lead to a never ending " +
                     "loop of expansion and contraction as an internal storage array length equal " +
                     "to the number of elements would satisfy the contraction criteria.",
-                    contractionCriteria);
+                    contraction);
         }
 
-        if (expansionFactor <= 1.0) {
+        if (expansion <= 1.0) {
             throw MathRuntimeException.createIllegalArgumentException(
                     "expansion factor smaller than one ({0})",
-                    expansionFactor);
+                    expansion);
         }
     }
     

src/test/java/org/apache/commons/math/genetics/DummyBinaryChromosome.java

@@ -32,8 +32,8 @@ public class DummyBinaryChromosome extends BinaryChromosome {
     }
 
     @Override
-    public AbstractListChromosome<Integer> newFixedLengthChromosome(List<Integer> representation) {
+    public AbstractListChromosome<Integer> newFixedLengthChromosome(List<Integer> chromosomeRepresentation) {
-        return new DummyBinaryChromosome(representation);
+        return new DummyBinaryChromosome(chromosomeRepresentation);
     }
 
     public double fitness() {

src/test/java/org/apache/commons/math/genetics/DummyRandomKey.java

@@ -32,8 +32,8 @@ public class DummyRandomKey extends RandomKey<String> {
     }
 
     @Override
-    public AbstractListChromosome<Double> newFixedLengthChromosome(List<Double> representation) {
+    public AbstractListChromosome<Double> newFixedLengthChromosome(List<Double> chromosomeRepresentation) {
-        return new DummyRandomKey(representation);
+        return new DummyRandomKey(chromosomeRepresentation);
     }
 
     public double fitness() {

src/test/java/org/apache/commons/math/genetics/GeneticAlgorithmTestBinary.java

@@ -113,8 +113,8 @@ public class GeneticAlgorithmTestBinary {
         }
 
         @Override
-        public AbstractListChromosome<Integer> newFixedLengthChromosome(List<Integer> representation) {
+        public AbstractListChromosome<Integer> newFixedLengthChromosome(List<Integer> chromosomeRepresentation) {
-            return new FindOnes(representation);
+            return new FindOnes(chromosomeRepresentation);
         }
 
     }

src/test/java/org/apache/commons/math/genetics/GeneticAlgorithmTestPermutations.java

@@ -124,8 +124,8 @@ public class GeneticAlgorithmTestPermutations {
         }
 
         @Override
-        public AbstractListChromosome<Double> newFixedLengthChromosome(List<Double> representation) {
+        public AbstractListChromosome<Double> newFixedLengthChromosome(List<Double> chromosomeRepresentation) {
-            return new MinPermutations(representation);
+            return new MinPermutations(chromosomeRepresentation);
         }
     }
 }