Author: erans
Date: Fri Oct 28 23:03:16 2011
New Revision: 1190657
URL: http://svn.apache.org/viewvc?rev=1190657&view=rev
Log:
Javadoc and code formatting.
Modified:
commons/proper/math/trunk/src/main/java/org/apache/commons/math/optimization/direct/CMAESOptimizer.java
Modified:
commons/proper/math/trunk/src/main/java/org/apache/commons/math/optimization/direct/CMAESOptimizer.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/main/java/org/apache/commons/math/optimization/direct/CMAESOptimizer.java?rev=1190657&r1=1190656&r2=1190657&view=diff
==============================================================================
---
commons/proper/math/trunk/src/main/java/org/apache/commons/math/optimization/direct/CMAESOptimizer.java
(original)
+++
commons/proper/math/trunk/src/main/java/org/apache/commons/math/optimization/direct/CMAESOptimizer.java
Fri Oct 28 23:03:16 2011
@@ -78,10 +78,9 @@ import org.apache.commons.math.util.Math
* @since 3.0
*/
-public class CMAESOptimizer extends
- BaseAbstractScalarOptimizer<MultivariateRealFunction> implements
- MultivariateRealOptimizer {
-
+public class CMAESOptimizer
+ extends BaseAbstractScalarOptimizer<MultivariateRealFunction>
+ implements MultivariateRealOptimizer {
/** Default value for {@link #checkFeasableCount}: {@value}. */
public static final int DEFAULT_CHECKFEASABLECOUNT = 0;
/** Default value for {@link #stopfitness}: {@value}. */
@@ -238,60 +237,45 @@ public class CMAESOptimizer extends
}
/**
- * @param lambda
- * Population size.
+ * @param lambda Population size.
*/
public CMAESOptimizer(int lambda) {
this(lambda, null, null, DEFAULT_MAXITERATIONS, DEFAULT_STOPFITNESS,
- DEFAULT_ISACTIVECMA, DEFAULT_DIAGONALONLY,
- DEFAULT_CHECKFEASABLECOUNT, DEFAULT_RANDOMGENERATOR, false);
+ DEFAULT_ISACTIVECMA, DEFAULT_DIAGONALONLY,
+ DEFAULT_CHECKFEASABLECOUNT, DEFAULT_RANDOMGENERATOR, false);
}
/**
- * @param lambda
- * Population size.
- * @param inputSigma
- * Initial search volume - sigma of offspring objective
- * variables.
- * @param boundaries
- * Boundaries for objective variables.
+ * @param lambda Population size.
+ * @param inputSigma Initial search volume; sigma of offspring objective
variables.
+ * @param boundaries Boundaries for objective variables.
*/
public CMAESOptimizer(int lambda, double[] inputSigma,
- double[][] boundaries) {
+ double[][] boundaries) {
this(lambda, inputSigma, boundaries, DEFAULT_MAXITERATIONS,
DEFAULT_STOPFITNESS,
- DEFAULT_ISACTIVECMA, DEFAULT_DIAGONALONLY,
- DEFAULT_CHECKFEASABLECOUNT, DEFAULT_RANDOMGENERATOR, false);
+ DEFAULT_ISACTIVECMA, DEFAULT_DIAGONALONLY,
+ DEFAULT_CHECKFEASABLECOUNT, DEFAULT_RANDOMGENERATOR, false);
}
/**
- * @param lambda
- * Population size.
- * @param inputSigma
- * Initial search volume - sigma of offspring objective
- * variables.
- * @param boundaries
- * Boundaries for objective variables.
- * @param maxIterations
- * Maximal number of iterations.
- * @param stopfitness
- * stop if objective function value < stopfitness.
- * @param isActiveCMA
- * Chooses the covariance matrix update method.
- * @param diagonalOnly
- * Number of initial iterations, where the covariance matrix
- * remains diagonal.
- * @param checkFeasableCount
- * Determines how often new. random objective variables are
- * generated in case they are out of bounds.
- * @param random
- * Used random generator.
- * @param generateStatistics
- * Indicates whether statistic data is collected.
+ * @param lambda Population size.
+ * @param inputSigma Initial search volume; sigma of offspring objective
variables.
+ * @param boundaries Boundaries for objective variables.
+ * @param maxIterations Maximal number of iterations.
+ * @param stopFitness Whether to stop if objective function value is
smaller than
+ * {@code stopFitness}.
+ * @param isActiveCMA Chooses the covariance matrix update method.
+ * @param diagonalOnly Number of initial iterations, where the covariance
matrix
+ * remains diagonal.
+ * @param checkFeasableCount Determines how often new random objective
variables are
+ * generated in case they are out of bounds.
+ * @param random Random generator.
+ * @param generateStatistics Whether statistic data is collected.
*/
public CMAESOptimizer(int lambda, double[] inputSigma,
- double[][] boundaries, int maxIterations, double stopfitness,
- boolean isActiveCMA, int diagonalOnly, int checkFeasableCount,
- RandomGenerator random, boolean generateStatistics) {
+ double[][] boundaries, int maxIterations, double
stopFitness,
+ boolean isActiveCMA, int diagonalOnly, int
checkFeasableCount,
+ RandomGenerator random, boolean generateStatistics) {
this.lambda = lambda;
this.inputSigma = inputSigma == null ? null : (double[])
inputSigma.clone();
if (boundaries == null) {
@@ -542,10 +526,8 @@ public class CMAESOptimizer extends
/**
* Initialization of the dynamic search parameters
*
- * @param guess
- * initial guess for the arguments of the fitness function
+ * @param guess Initial guess for the arguments of the fitness function.
*/
-
private void initializeCMA(double[] guess) {
if (lambda <= 0) {
lambda = 4 + (int) (3. * Math.log(dimension));
@@ -615,14 +597,12 @@ public class CMAESOptimizer extends
}
/**
- * Update of the evolution paths ps and pc
+ * Update of the evolution paths ps and pc.
*
- * @param zmean
- * weighted row matrix of the gaussian random numbers generating
- * the current offspring
- * @param xold
- * xmean matrix of the previous generation
- * @return hsig flag indicating a small correction
+ * @param zmean Weighted row matrix of the gaussian random numbers
generating
+ * the current offspring.
+ * @param xold xmean matrix of the previous generation.
+ * @return hsig flag indicating a small correction.
*/
private boolean updateEvolutionPaths(RealMatrix zmean, RealMatrix xold) {
ps = ps.scalarMultiply(1. - cs).add(
@@ -643,16 +623,14 @@ public class CMAESOptimizer extends
/**
* Update of the covariance matrix C for diagonalOnly > 0
*
- * @param hsig
- * flag indicating a small correction
- * @param bestArz
- * fitness-sorted matrix of the gaussian random values of the
- * current offspring
- * @param xold
- * xmean matrix of the previous generation
- */
- private void updateCovarianceDiagonalOnly(boolean hsig, final RealMatrix
bestArz,
- final RealMatrix xold) {
+ * @param hsig Flag indicating a small correction.
+ * @param bestArz Fitness-sorted matrix of the gaussian random values of
the
+ * current offspring.
+ * @param xold xmean matrix of the previous generation.
+ */
+ private void updateCovarianceDiagonalOnly(boolean hsig,
+ final RealMatrix bestArz,
+ final RealMatrix xold) {
// minor correction if hsig==false
double oldFac = hsig ? 0 : ccov1Sep * cc * (2. - cc);
oldFac += 1. - ccov1Sep - ccovmuSep;
@@ -673,20 +651,15 @@ public class CMAESOptimizer extends
}
/**
- * Update of the covariance matrix C
+ * Update of the covariance matrix C.
*
- * @param hsig
- * flag indicating a small correction
- * @param bestArx
- * fitness-sorted matrix of the argument vectors producing the
- * current offspring
- * @param arz
- * unsorted matrix containing the gaussian random values of the
- * current offspring
- * @param arindex
- * indices indicating the fitness-order of the current offspring
- * @param xold
- * xmean matrix of the previous generation
+ * @param hsig Flag indicating a small correction.
+ * @param bestArx Fitness-sorted matrix of the argument vectors producing
the
+ * current offspring.
+ * @param arz Unsorted matrix containing the gaussian random values of the
+ * current offspring.
+ * @param arindex Indices indicating the fitness-order of the current
offspring.
+ * @param xold xmean matrix of the previous generation.
*/
private void updateCovariance(boolean hsig, final RealMatrix bestArx,
final RealMatrix arz, final int[] arindex, final RealMatrix xold) {
@@ -757,10 +730,9 @@ public class CMAESOptimizer extends
}
/**
- * Update B and D from C
+ * Update B and D from C.
*
- * @param negccov
- * Negative covariance factor.
+ * @param negccov Negative covariance factor.
*/
private void updateBD(double negccov) {
if (ccov1 + ccovmu + negccov > 0 &&
@@ -796,10 +768,8 @@ public class CMAESOptimizer extends
/**
* Pushes the current best fitness value in a history queue.
*
- * @param vals
- * the history queue
- * @param val
- * current best fitness value
+ * @param vals History queue.
+ * @param val Current best fitness value.
*/
private static void push(double[] vals, double val) {
for (int i = vals.length-1; i > 0; i--) {
@@ -811,9 +781,8 @@ public class CMAESOptimizer extends
/**
* Sorts fitness values.
*
- * @param doubles
- * array of values to be sorted
- * @return sorted array of indices pointing into doubles
+ * @param doubles Array of values to be sorted.
+ * @return a sorted array of indices pointing into doubles.
*/
private int[] sortedIndices(final double[] doubles) {
DoubleIndex[] dis = new DoubleIndex[doubles.length];
@@ -833,17 +802,14 @@ public class CMAESOptimizer extends
* order.
*/
private static class DoubleIndex implements Comparable<DoubleIndex> {
-
/** Value to compare. */
private double value;
/** Index into sorted array. */
private int index;
/**
- * @param value
- * Value to compare.
- * @param index
- * Index into sorted array.
+ * @param value Value to compare.
+ * @param index Index into sorted array.
*/
DoubleIndex(double value, int index) {
this.value = value;
@@ -886,7 +852,6 @@ public class CMAESOptimizer extends
* setValueRange().
*/
private class FitnessFunction {
-
/** Determines the penalty for boundary violations */
private double valueRange;
/**
@@ -903,9 +868,8 @@ public class CMAESOptimizer extends
}
/**
- * @param x
- * Original objective variables.
- * @return Normalized objective variables.
+ * @param x Original objective variables.
+ * @return the normalized objective variables.
*/
public double[] encode(final double[] x) {
if (boundaries == null) {
@@ -920,9 +884,8 @@ public class CMAESOptimizer extends
}
/**
- * @param x
- * Normalized objective variables.
- * @return Original objective variables.
+ * @param x Normalized objective variables.
+ * @return the original objective variables.
*/
public double[] decode(final double[] x) {
if (boundaries == null) {
@@ -937,9 +900,8 @@ public class CMAESOptimizer extends
}
/**
- * @param point
- * Normalized objective variables.
- * @return Objective value + penalty for violated bounds.
+ * @param point Normalized objective variables.
+ * @return the objective value + penalty for violated bounds.
*/
public double value(final double[] point) {
double value;
@@ -956,9 +918,8 @@ public class CMAESOptimizer extends
}
/**
- * @param x
- * Normalized objective variables.
- * @return True if in bounds
+ * @param x Normalized objective variables.
+ * @return {@code true} if in bounds.
*/
public boolean isFeasible(final double[] x) {
if (boundaries == null) {
@@ -976,17 +937,15 @@ public class CMAESOptimizer extends
}
/**
- * @param valueRange
- * Adjusts the penalty computation.
+ * @param valueRange Adjusts the penalty computation.
*/
public void setValueRange(double valueRange) {
this.valueRange = valueRange;
}
/**
- * @param x
- * Normalized objective variables.
- * @return Repaired objective variables - all in bounds.
+ * @param x Normalized objective variables.
+ * @return the repaired objective variables - all in bounds.
*/
private double[] repair(final double[] x) {
double[] repaired = new double[x.length];
@@ -1003,10 +962,8 @@ public class CMAESOptimizer extends
}
/**
- * @param x
- * Normalized objective variables.
- * @param repaired
- * Repaired objective variables.
+ * @param x Normalized objective variables.
+ * @param repaired Repaired objective variables.
* @return Penalty value according to the violation of the bounds.
*/
private double penalty(final double[] x, final double[] repaired) {
@@ -1022,9 +979,8 @@ public class CMAESOptimizer extends
// -----Matrix utility functions similar to the Matlab build in
functions------
/**
- * @param m
- * Input matrix
- * @return Matrix representing the element wise logarithm of m.
+ * @param m Input matrix
+ * @return Matrix representing the element-wise logarithm of m.
*/
private static RealMatrix log(final RealMatrix m) {
double[][] d = new double[m.getRowDimension()][m.getColumnDimension()];
@@ -1039,7 +995,7 @@ public class CMAESOptimizer extends
/**
* @param m
* Input matrix
- * @return Matrix representing the element wise square root of m.
+ * @return Matrix representing the element-wise square root of m.
*/
private static RealMatrix sqrt(final RealMatrix m) {
double[][] d = new double[m.getRowDimension()][m.getColumnDimension()];
@@ -1053,7 +1009,7 @@ public class CMAESOptimizer extends
/**
* @param m Input matrix
- * @return Matrix representing the element wise square (^2) of m.
+ * @return Matrix representing the element-wise square (^2) of m.
*/
private static RealMatrix square(final RealMatrix m) {
double[][] d = new double[m.getRowDimension()][m.getColumnDimension()];
@@ -1067,34 +1023,30 @@ public class CMAESOptimizer extends
}
/**
- * @param m
- * Input matrix 1.
- * @param n
- * Input matrix 2.
- * @return Matrix where the elements of m and m are element wise
multiplied.
+ * @param m Input matrix 1.
+ * @param n Input matrix 2.
+ * @return the matrix where the elements of m and n are element-wise
multiplied.
*/
private static RealMatrix times(final RealMatrix m, final RealMatrix n) {
double[][] d = new double[m.getRowDimension()][m.getColumnDimension()];
for (int r = 0; r < m.getRowDimension(); r++) {
for (int c = 0; c < m.getColumnDimension(); c++) {
- d[r][c] = m.getEntry(r, c)*n.getEntry(r, c);
+ d[r][c] = m.getEntry(r, c) * n.getEntry(r, c);
}
}
return new Array2DRowRealMatrix(d, false);
}
/**
- * @param m
- * Input matrix 1.
- * @param n
- * Input matrix 2.
- * @return Matrix where the elements of m and m are element wise divided.
+ * @param m Input matrix 1.
+ * @param n Input matrix 2.
+ * @return Matrix where the elements of m and n are element-wise divided.
*/
private static RealMatrix divide(final RealMatrix m, final RealMatrix n) {
double[][] d = new double[m.getRowDimension()][m.getColumnDimension()];
for (int r = 0; r < m.getRowDimension(); r++) {
for (int c = 0; c < m.getColumnDimension(); c++) {
- d[r][c] = m.getEntry(r, c)/n.getEntry(r, c);
+ d[r][c] = m.getEntry(r, c) / n.getEntry(r, c);
}
}
return new Array2DRowRealMatrix(d, false);
@@ -1117,7 +1069,7 @@ public class CMAESOptimizer extends
/**
* @param m Input matrix.
- * @param k diagonal position.
+ * @param k Diagonal position.
* @return Upper triangular part of matrix.
*/
private static RealMatrix triu(final RealMatrix m, int k) {
@@ -1131,8 +1083,7 @@ public class CMAESOptimizer extends
}
/**
- * @param m
- * Input matrix.
+ * @param m Input matrix.
* @return Row matrix representing the sums of the rows.
*/
private static RealMatrix sumRows(final RealMatrix m) {
@@ -1148,10 +1099,9 @@ public class CMAESOptimizer extends
}
/**
- * @param m
- * Input matrix.
- * @return Diagonal n X n matrix if m is a column matrix, Column matrix
- * representing the diagonal if m is a nXn matrix.
+ * @param m Input matrix.
+ * @return the diagonal n-by-n matrix if m is a column matrix or the column
+ * matrix representing the diagonal if m is a n-by-n matrix.
*/
private static RealMatrix diag(final RealMatrix m) {
if (m.getColumnDimension() == 1) {
@@ -1172,14 +1122,10 @@ public class CMAESOptimizer extends
/**
* Copies a column from m1 to m2.
*
- * @param m1
- * Source matrix 1.
- * @param col1
- * Source column.
- * @param m2
- * Target matrix.
- * @param col2
- * Target column.
+ * @param m1 Source matrix 1.
+ * @param col1 Source column.
+ * @param m2 Target matrix.
+ * @param col2 Target column.
*/
private static void copyColumn(final RealMatrix m1, int col1, RealMatrix
m2, int col2) {
for (int i = 0; i < m1.getRowDimension(); i++) {
@@ -1188,11 +1134,9 @@ public class CMAESOptimizer extends
}
/**
- * @param n
- * Number of rows.
- * @param m
- * Number of columns.
- * @return n X m matrix of 1.0-values.
+ * @param n Number of rows.
+ * @param m Number of columns.
+ * @return n-by-m matrix filled with 1.
*/
private static RealMatrix ones(int n, int m) {
double[][] d = new double[n][m];
@@ -1203,11 +1147,9 @@ public class CMAESOptimizer extends
}
/**
- * @param n
- * Number of rows.
- * @param m
- * Number of columns.
- * @return n X m matrix of 0.0-values, diagonal has values 1.0.
+ * @param n Number of rows.
+ * @param m Number of columns.
+ * @return n-by-m matrix of 0.0-values, diagonal has values 1.0.
*/
private static RealMatrix eye(int n, int m) {
double[][] d = new double[n][m];
@@ -1220,24 +1162,19 @@ public class CMAESOptimizer extends
}
/**
- * @param n
- * Number of rows.
- * @param m
- * Number of columns.
- * @return n X m matrix of 0.0-values.
+ * @param n Number of rows.
+ * @param m Number of columns.
+ * @return n-by-m matrix of 0.0-values.
*/
private static RealMatrix zeros(int n, int m) {
return new Array2DRowRealMatrix(n, m);
}
/**
- * @param mat
- * Input matrix.
- * @param n
- * Number of row replicates.
- * @param m
- * Number of column replicates.
- * @return Matrix which replicates the input matrix in both directions.
+ * @param mat Input matrix.
+ * @param n Number of row replicates.
+ * @param m Number of column replicates.
+ * @return a matrix which replicates the input matrix in both directions.
*/
private static RealMatrix repmat(final RealMatrix mat, int n, int m) {
int rd = mat.getRowDimension();
@@ -1252,13 +1189,10 @@ public class CMAESOptimizer extends
}
/**
- * @param start
- * Start value.
- * @param end
- * End value.
- * @param step
- * Step size.
- * @return Sequence as column matrix.
+ * @param start Start value.
+ * @param end End value.
+ * @param step Step size.
+ * @return a sequence as column matrix.
*/
private static RealMatrix sequence(double start, double end, double step) {
int size = (int) ((end - start) / step + 1);
@@ -1272,9 +1206,8 @@ public class CMAESOptimizer extends
}
/**
- * @param m
- * Input matrix.
- * @return Maximum of matrix element values.
+ * @param m Input matrix.
+ * @return the maximum of the matrix element values.
*/
private static double max(final RealMatrix m) {
double max = -Double.MAX_VALUE;
@@ -1290,9 +1223,8 @@ public class CMAESOptimizer extends
}
/**
- * @param m
- * Input matrix.
- * @return Minimum of matrix element values.
+ * @param m Input matrix.
+ * @return the minimum of the matrix element values.
*/
private static double min(final RealMatrix m) {
double min = Double.MAX_VALUE;
@@ -1308,9 +1240,8 @@ public class CMAESOptimizer extends
}
/**
- * @param m
- * Input array.
- * @return Maximum of array values.
+ * @param m Input array.
+ * @return the maximum of the array values.
*/
private static double max(final double[] m) {
double max = -Double.MAX_VALUE;
@@ -1323,9 +1254,8 @@ public class CMAESOptimizer extends
}
/**
- * @param m
- * Input array.
- * @return Minimum of array values.
+ * @param m Input array.
+ * @return the minimum of the array values.
*/
private static double min(final double[] m) {
double min = Double.MAX_VALUE;
@@ -1338,9 +1268,8 @@ public class CMAESOptimizer extends
}
/**
- * @param indices
- * Input index array.
- * @return Inverse of the mapping defined by indices
+ * @param indices Input index array.
+ * @return the inverse of the mapping defined by indices.
*/
private static int[] inverse(final int[] indices) {
int[] inverse = new int[indices.length];
@@ -1351,9 +1280,8 @@ public class CMAESOptimizer extends
}
/**
- * @param indices
- * Input index array.
- * @return Indices in inverse order (last is first)
+ * @param indices Input index array.
+ * @return the indices in inverse order (last is first).
*/
private static int[] reverse(final int[] indices) {
int[] reverse = new int[indices.length];
@@ -1364,9 +1292,8 @@ public class CMAESOptimizer extends
}
/**
- * @param size
- * Length of random array.
- * @return Array of gaussian random numbers.
+ * @param size Length of random array.
+ * @return an array of Gaussian random numbers.
*/
private double[] randn(int size) {
double[] randn = new double[size];
@@ -1377,11 +1304,9 @@ public class CMAESOptimizer extends
}
/**
- * @param size
- * Number of rows.
- * @param popSize
- * Population size.
- * @return 2-dimensional matrix of gaussian random numbers.
+ * @param size Number of rows.
+ * @param popSize Population size.
+ * @return a 2-dimensional matrix of Gaussian random numbers.
*/
private RealMatrix randn1(int size, int popSize) {
double[][] d = new double[size][popSize];
@@ -1393,4 +1318,3 @@ public class CMAESOptimizer extends
return new Array2DRowRealMatrix(d, false);
}
}
-
