Abstract getProblemDimension into InterfaceOptimizationProblem

closes #26
- Clean up problems that implemented getProblemDimension
- Move getProblemDimension to Interface and implement it in AbstractOptimizationProblem
- Output the problem dimension in the Processor (e.g. when running an optimization)

src/eva2/optimization/modules/Processor.java

@@ -19,6 +19,7 @@ import eva2.optimization.statistics.InterfaceStatistics;
 import eva2.optimization.statistics.InterfaceTextListener;
 import eva2.optimization.statistics.StatisticsWithGUI;
 import eva2.optimization.strategies.InterfaceOptimizer;
+import eva2.problems.InterfaceOptimizationProblem;
 import eva2.tools.EVAERROR;
 import eva2.tools.StringTools;
 import eva2.tools.math.RNG;
@@ -402,7 +403,9 @@ public class Processor extends Thread implements InterfaceProcessor, InterfacePo
 
     @Override
     public String getInfoString() {
-        return this.optimizationParameters.getProblem().getName() + "+" + this.optimizationParameters.getOptimizer().getName();
+        InterfaceOptimizationProblem problem = this.optimizationParameters.getProblem();
+
+        return problem.getName() + "{" + problem.getProblemDimension() + "}+" + this.optimizationParameters.getOptimizer().getName();
     }
 
     /**

src/eva2/problems/AbstractDynTransProblem.java

@@ -136,10 +136,6 @@ public abstract class AbstractDynTransProblem extends AbstractSynchronousOptimiz
         }
     }
 
-    public int getProblemDimension() {
-        return problemDimension;
-    }
-
     @Override
     public String getStringRepresentationForProblem(InterfaceOptimizer opt) {
         return "DynTransProblem";

src/eva2/problems/AbstractMultiModalProblemKnown.java

@@ -379,9 +379,4 @@ public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDoub
     public void setDefaultAccuracy(double epsilon) {
         super.setDefaultAccuracy(epsilon);
     }
-
-    @Override
-    public int getProblemDimension() {
-        return problemDimension;
-    }
 }

src/eva2/problems/AbstractOptimizationProblem.java

@@ -24,6 +24,7 @@ import eva2.optimization.population.Population;
 import eva2.optimization.population.PopulationInterface;
 import eva2.optimization.strategies.InterfaceOptimizer;
 import eva2.tools.ToolBox;
+import eva2.util.annotation.Parameter;
 
 import javax.swing.*;
 import java.awt.*;
@@ -71,8 +72,12 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati
 
     protected AbstractEAIndividual template = null;
 
+    @Parameter(name = "defaultAccuracy", description = "A default threshold to identify optima - e.g. the assumed minimal distance between any two optima.")
     private double defaultAccuracy = 0.001; // default accuracy for identifying optima.
 
+    @Parameter(name = "problemDimension", description = "Length of the x vector to be optimized.")
+    protected int problemDimension = 10;
+
     /**
      * This method returns a deep clone of the problem.
      *
@@ -562,10 +567,6 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati
         defaultAccuracy = defAcc;
     }
 
-    public String defaultAccuracyTipText() {
-        return "A default threshold to identify optima - e.g. the assumed minimal distance between any two optima.";
-    }
-
     /**
      * This method allows the CommonJavaObjectEditorPanel to read the
      * name to the current object.
@@ -577,6 +578,11 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati
         return "AbstractOptimizationProblem";
     }
 
+    @Override
+    public int getProblemDimension() {
+        return this.problemDimension;
+    }
+
     /**
      * This method returns a global info string
      *

src/eva2/problems/AbstractProblemBinary.java

@@ -54,13 +54,6 @@ public abstract class AbstractProblemBinary extends AbstractOptimizationProblem
      */
     public abstract double[] eval(BitSet bs);
 
-    /**
-     * Get the problem dimension.
-     *
-     * @return the problem dimension
-     */
-    public abstract int getProblemDimension();
-
     @Override
     public void initializePopulation(Population population) {
         ((InterfaceDataTypeBinary) this.template).setBinaryDataLength(this.getProblemDimension());

src/eva2/problems/AbstractProblemDouble.java

@@ -202,14 +202,6 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
     @Override
     public abstract double[] evaluate(double[] x);
 
-    /**
-     * Get the problem dimension.
-     *
-     * @return the problem dimension
-     */
-    @Override
-    public abstract int getProblemDimension();
-
     @Override
     public void initializePopulation(Population population) {
         initTemplate();

src/eva2/problems/AbstractProblemDoubleOffset.java

@@ -87,14 +87,5 @@ public abstract class AbstractProblemDoubleOffset extends AbstractProblemDouble
     public void setProblemDimension(int t) {
         this.problemDimension = t;
     }
-
-    @Override
-    public int getProblemDimension() {
-        return this.problemDimension;
-    }
-
-    public String problemDimensionTipText() {
-        return "Length of the x vector to be optimized.";
-    }
 }
 

src/eva2/problems/AbstractProblemInteger.java

@@ -118,14 +118,6 @@ public abstract class AbstractProblemInteger extends AbstractOptimizationProblem
         this.problemDimension = n;
     }
 
-    public int getProblemDimension() {
-        return this.problemDimension;
-    }
-
-    public String problemDimensionTipText() {
-        return "Length of the integer vector to be optimized";
-    }
-
     /**
      * This method allows you to choose the EA individual
      *

src/eva2/problems/B1Problem.java

@@ -119,11 +119,6 @@ public class B1Problem extends AbstractProblemBinary implements java.io.Serializ
         this.problemDimension = dim;
     }
 
-    @Override
-    public int getProblemDimension() {
-        return this.problemDimension;
-    }
-
     public String multiRunsTipText() {
         return "Length of the BitSet that is to be optimized.";
     }

src/eva2/problems/ExternalRuntimeProblem.java

@@ -364,15 +364,6 @@ public class ExternalRuntimeProblem extends AbstractOptimizationProblem
         this.initializationRange.adaptRowCount(t);
     }
 
-    @Override
-    public int getProblemDimension() {
-        return this.problemDimension;
-    }
-
-    public String problemDimensionTipText() {
-        return "Domain dimension of the problem";
-    }
-
     /**
      * Length of the x vector at is to be optimized
      *

src/eva2/problems/F15Problem.java

@@ -47,11 +47,6 @@ public class F15Problem extends AbstractProblemDouble implements Serializable, I
         return y;
     }
 
-    @Override
-    public int getProblemDimension() {
-        return problemDimension;
-    }
-
     public void setProblemDimension(int d) {
         problemDimension = d;
     }

src/eva2/problems/F16Problem.java

@@ -9,18 +9,18 @@ import eva2.util.annotation.Description;
  */
 @Description("Vincent function: Multiple optima with increasing densitiy near the lower bounds, therefore decreasing attractor size. All have an equal best fitness of zero")
 public class F16Problem extends AbstractProblemDouble implements InterfaceMultimodalProblem, Interface2DBorderProblem, InterfaceInterestingHistogram {
-    int dim = 10;
+    int problemDimension = 10;
 
     public F16Problem() {
-        dim = 10;
+        problemDimension = 10;
     }
 
     public F16Problem(F16Problem other) {
-        dim = other.dim;
+        problemDimension = other.problemDimension;
     }
 
     public F16Problem(int theDim) {
-        this.dim = theDim;
+        this.problemDimension = theDim;
     }
 
     @Override
@@ -37,13 +37,8 @@ public class F16Problem extends AbstractProblemDouble implements InterfaceMultim
         return res;
     }
 
-    @Override
-    public int getProblemDimension() {
-        return dim;
-    }
-
     public void setProblemDimension(int newDim) {
-        dim = newDim;
+        problemDimension = newDim;
     }
 
     @Override

src/eva2/problems/F17Problem.java

@@ -12,11 +12,11 @@ import eva2.util.annotation.Description;
 @Description("Bohachevsky function, numerous optima on an oval hyperparabola with similar attractor sizes but decreasing fitness towards the bounds")
 public class F17Problem extends AbstractProblemDouble implements
         InterfaceMultimodalProblem, InterfaceInterestingHistogram {
-    int dim = 10;
+    int problemDimension = 10;
 
     public F17Problem() {
         setDefaultRange(10.);
-        dim = 10;
+        problemDimension = 10;
     }
 
     public F17Problem(int dimension) {
@@ -26,7 +26,7 @@ public class F17Problem extends AbstractProblemDouble implements
 
     public F17Problem(F17Problem other) {
         super(other);
-        dim = other.dim;
+        problemDimension = other.problemDimension;
     }
 
     @Override
@@ -42,13 +42,8 @@ public class F17Problem extends AbstractProblemDouble implements
         return res;
     }
 
-    @Override
-    public int getProblemDimension() {
-        return dim;
-    }
-
     public void setProblemDimension(int newDim) {
-        dim = newDim;
+        problemDimension = newDim;
     }
 
     @Override

src/eva2/problems/F18Problem.java

@@ -8,15 +8,15 @@ import eva2.util.annotation.Description;
 @Description("N-Function from Shir&Baeck, PPSN 2006")
 public class F18Problem extends AbstractProblemDouble implements
         InterfaceMultimodalProblem {
-    int dim = 10;
+    int problemDimension = 10;
     double alpha = 1.;
 
     public F18Problem() {
-        dim = 10;
+        problemDimension = 10;
     }
 
     public F18Problem(F18Problem other) {
-        dim = other.dim;
+        problemDimension = other.problemDimension;
     }
 
     @Override
@@ -41,13 +41,8 @@ public class F18Problem extends AbstractProblemDouble implements
         return 1.;
     }
 
-    @Override
-    public int getProblemDimension() {
-        return dim;
-    }
-
     public void setProblemDimension(int newDim) {
-        dim = newDim;
+        problemDimension = newDim;
     }
 
     @Override

src/eva2/problems/F19Problem.java

@@ -15,19 +15,19 @@ import java.util.Random;
 @Description("Fletcher-Powell function")
 public class F19Problem extends AbstractProblemDouble implements
         InterfaceMultimodalProblem, InterfaceInterestingHistogram, InterfaceFirstOrderDerivableProblem {
-    int dim = 10;
+    int problemDimension = 10;
     transient private double[] alphas, As;
     transient private int[] A, B;
     private long randSeed = 23;
 
     public F19Problem() {
         alphas = null;
-        dim = 10;
+        problemDimension = 10;
         setDefaultRange(Math.PI);
     }
 
     public F19Problem(F19Problem other) {
-        dim = other.dim;
+        problemDimension = other.problemDimension;
         alphas = null;
     }
 
@@ -42,17 +42,17 @@ public class F19Problem extends AbstractProblemDouble implements
         // create static random data
         Random rand = new Random();
         rand.setSeed(randSeed);
-        alphas = RNG.randomDoubleArray(rand, -Math.PI, Math.PI, dim);
+        alphas = RNG.randomDoubleArray(rand, -Math.PI, Math.PI, problemDimension);
-        A = RNG.randomIntArray(rand, -100, 100, dim * dim);
+        A = RNG.randomIntArray(rand, -100, 100, problemDimension * problemDimension);
-        B = RNG.randomIntArray(rand, -100, 100, dim * dim);
+        B = RNG.randomIntArray(rand, -100, 100, problemDimension * problemDimension);
         As = transform(alphas);
     }
 
     private double[] transform(double[] x) {
-        double[] v = new double[dim];
+        double[] v = new double[problemDimension];
         Arrays.fill(v, 0.);
-        for (int i = 0; i < dim; i++) {
+        for (int i = 0; i < problemDimension; i++) {
-            for (int j = 0; j < dim; j++) {
+            for (int j = 0; j < problemDimension; j++) {
                 v[i] += get(A, i, j) * Math.sin(x[j]) + get(B, i, j) * Math.cos(x[j]);
             }
         }
@@ -80,7 +80,7 @@ public class F19Problem extends AbstractProblemDouble implements
      * @return
      */
     private int get(int[] M, int i, int j) {
-        return M[i * dim + j];
+        return M[i * problemDimension + j];
     }
 
     @Override
@@ -97,13 +97,8 @@ public class F19Problem extends AbstractProblemDouble implements
         return res;
     }
 
-    @Override
-    public int getProblemDimension() {
-        return dim;
-    }
-
     public void setProblemDimension(int newDim) {
-        dim = newDim;
+        problemDimension = newDim;
         if (alphas != null && (newDim > alphas.length)) { // only recreate if really necessary
             alphas = null;
             A = null;

src/eva2/problems/F20Problem.java

@@ -21,23 +21,23 @@ import java.io.Serializable;
         "The minimum fitness f(x*) is close to (n-1)*r for dimension n and default range r, by which " +
         "this implementation may be shifted to the positive domain.")
 public class F20Problem extends AbstractProblemDouble implements Serializable, InterfaceInterestingHistogram {
-    private int dim = 10;
+    private int problemDimension = 10;
     private boolean shiftFit = false;
 
     public F20Problem() {
         setDefaultRange(512.);
     }
 
-    public F20Problem(int dim, boolean rotate) {
+    public F20Problem(int problemDimension, boolean rotate) {
         this();
-        setProblemDimension(dim);
+        setProblemDimension(problemDimension);
         setDoRotation(rotate);
     }
 
     public F20Problem(F20Problem o) {
         super(o);
         setDefaultRange(512);
-        this.dim = o.dim;
+        this.problemDimension = o.problemDimension;
         this.setShiftFit(o.isShiftFit());
     }
 
@@ -70,13 +70,8 @@ public class F20Problem extends AbstractProblemDouble implements Serializable, I
         return Math.sqrt(Math.abs(x + y + 1));
     }
 
-    @Override
-    public int getProblemDimension() {
-        return dim;
-    }
-
     public void setProblemDimension(int newDim) {
-        dim = newDim;
+        problemDimension = newDim;
     }
 
     @Override

src/eva2/problems/F21Problem.java

@@ -16,7 +16,7 @@ public class F21Problem extends AbstractProblemDouble implements InterfaceMultim
     private double[] heights = null; // will receive random positions within the range
     private double[][] peaks = null; // will receive values in [0,1] as peak height values
     private static final int rndSeed = 23;
-    private int dim = 2;
+    private int problemDimension = 2;
 
     public F21Problem() {
     }
@@ -76,13 +76,8 @@ public class F21Problem extends AbstractProblemDouble implements InterfaceMultim
         return res;
     }
 
-    @Override
-    public int getProblemDimension() {
-        return dim;
-    }
-
     public void setProblemDimension(int d) {
-        dim = d;
+        problemDimension = d;
     }
 
     public String problemDimensionTipText() {

src/eva2/problems/FLensProblem.java

@@ -530,14 +530,6 @@ public class FLensProblem extends AbstractOptimizationProblem
         this.problemDimension = multiruns;
     }
 
-    public int getProblemDimension() {
-        return this.problemDimension;
-    }
-
-    public String problemDimensionTipText() {
-        return "Length of the x vector at is to be optimized.";
-    }
-
     /**
      * This method allows you to toggle the solution representation.
      *

src/eva2/problems/GPFunctionProblem.java

@@ -17,7 +17,7 @@ public class GPFunctionProblem extends AbstractProblemDouble implements Interfac
     InterfaceProgram gpProblem = null;
     GPArea gpArea = new GPArea();
     double[] pos = null;
-    int dim = 2;
+    int problemDimension = 2;
     double scalingStart = 10.;
     double scalingLimit = 20.;
 
@@ -56,8 +56,8 @@ public class GPFunctionProblem extends AbstractProblemDouble implements Interfac
      * @param scLim
      */
     public GPFunctionProblem(InterfaceProgram gpProb, GPArea area, int pDim, double scStart, double scLim) {
-        dim = pDim;
+        problemDimension = pDim;
-        ((ESIndividualDoubleData) template).setDoubleDataLength(dim);
+        ((ESIndividualDoubleData) template).setDoubleDataLength(problemDimension);
         gpProblem = gpProb;
         gpArea = area;
         scalingStart = scStart;
@@ -65,10 +65,10 @@ public class GPFunctionProblem extends AbstractProblemDouble implements Interfac
     }
 
     public GPFunctionProblem(GPFunctionProblem functionProblem) {
-        dim = functionProblem.dim;
+        problemDimension = functionProblem.problemDimension;
         if (functionProblem.pos != null) {
-            pos = new double[dim];
+            pos = new double[problemDimension];
-            System.arraycopy(functionProblem.pos, 0, pos, 0, dim);
+            System.arraycopy(functionProblem.pos, 0, pos, 0, problemDimension);
         }
         gpArea = (GPArea) functionProblem.gpArea.clone();
         gpProblem = functionProblem.gpProblem;
@@ -81,7 +81,7 @@ public class GPFunctionProblem extends AbstractProblemDouble implements Interfac
 
     @Override
     public double[] evaluate(double[] x) {
-        if (x.length != dim) {
+        if (x.length != problemDimension) {
             EVAERROR.errorMsgOnce("mismatching dimension of GPFunctionProblem! Setting to " + x.length);
             setProblemDimension(x.length);
         }
@@ -100,8 +100,8 @@ public class GPFunctionProblem extends AbstractProblemDouble implements Interfac
      * @param newDim
      */
     public void setProblemDimension(int newDim) {
-        dim = newDim;
+        problemDimension = newDim;
-        ((ESIndividualDoubleData) template).setDoubleDataLength(dim);
+        ((ESIndividualDoubleData) template).setDoubleDataLength(problemDimension);
     }
 
     /**
@@ -125,11 +125,6 @@ public class GPFunctionProblem extends AbstractProblemDouble implements Interfac
         }
     }
 
-    @Override
-    public int getProblemDimension() {
-        return dim;
-    }
-
     @Override
     public String getStringRepresentationForProblem(InterfaceOptimizer opt) {
         return "GP find a function problem";

src/eva2/problems/InterfaceOptimizationProblem.java

@@ -53,8 +53,6 @@ public interface InterfaceOptimizationProblem extends InterfaceAdditionalPopulat
      */
     public void evaluate(AbstractEAIndividual individual);
 
-    /******************** Some output methods *******************************************/
-
     /**
      * This method allows the GenericObjectEditorPanel to read the
      * name to the current object.
@@ -95,4 +93,11 @@ public interface InterfaceOptimizationProblem extends InterfaceAdditionalPopulat
      * @return Double value
      */
     public Double getDoublePlotValue(Population pop);
+
+    /**
+     * This method returns the dimension of the problem. Some problem implementations
+     * may add a setProblemDimension() method, but as some problems have a fixed problem
+     * dimension this is not added in this interface.
+     */
+    public int getProblemDimension();
 }

src/eva2/problems/InterfaceProblemDouble.java

@@ -15,13 +15,6 @@ public interface InterfaceProblemDouble {
      */
     public double[] evaluate(double[] x);
 
-    /**
-     * Get the problem dimension.
-     *
-     * @return the problem dimension
-     */
-    public int getProblemDimension();
-
     /**
      * Create a new range array by using the getRangeLowerBound and getRangeUpperBound methods.
      *

src/eva2/problems/MatlabProblem.java

@@ -235,15 +235,6 @@ public class MatlabProblem extends AbstractOptimizationProblem implements Interf
         this.problemDimension = problemDimension;
     }
 
-
-    public int getProblemDimension() {
-        return problemDimension;
-    }
-
-    public String problemDimensionTipText() {
-        return "The dimension of the problem.";
-    }
-
     public void log(String str) {
         if (dos != null) {
             dos.print(str);

src/eva2/problems/TF1Problem.java

@@ -333,14 +333,6 @@ public class TF1Problem extends AbstractMultiObjectiveOptimizationProblem implem
         this.problemDimension = d;
     }
 
-    public int getProblemDimension() {
-        return this.problemDimension;
-    }
-
-    public String problemDimensionTipText() {
-        return "Length of the x vector at is to be optimized.";
-    }
-
     /**
      * This method allows you to choose the EA individual
      *