Some additional manual formatting.

Change-Id: Ie5590535d013aa2f747dd034fa2fcd2ae5c3956b
Reviewed-on: https://gerrit.libreoffice.org/72226
Tested-by: Jenkins
Reviewed-by: Samuel Mehrbrodt <Samuel.Mehrbrodt@cib.de>

nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/deps/DEPSAgent.java

@@ -46,6 +46,7 @@ public class DEPSAgent implements ILibEngine {
 
   // Describes the problem to be solved
   private ProblemEncoder problemEncoder;
+
   // Forms the goodness landscape
   private IGoodnessCompareEngine qualityComparator;
 
@@ -57,8 +58,10 @@ public class DEPSAgent implements ILibEngine {
 
   // the own memory: store the point that generated in old learning cycle
   private BasicPoint pold_t;
+
   // the own memory: store the point that generated in last learning cycle
   private BasicPoint pcurrent_t;
+
   // the own memory: store the personal best point
   private SearchPoint pbest_t;
 
@@ -109,11 +112,12 @@ public class DEPSAgent implements ILibEngine {
   }
 
   public void generatePoint() {
-// generates a new point in the search space (S) based on
-// its memory and the library
+    // generates a new point in the search space (S) based on
+    // its memory and the library
     selectGTBehavior = this.getGTBehavior();
     selectGTBehavior.generateBehavior(trailPoint, problemEncoder);
-// evaluate into goodness information
+
+    // evaluate into goodness information
     problemEncoder.evaluate(trailPoint);
   }
 

nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/deps/behavior/DEGTBehavior.java

@@ -37,9 +37,14 @@ import net.adaptivebox.problem.ProblemEncoder;
 import net.adaptivebox.space.BasicPoint;
 
 public class DEGTBehavior extends AbsGTBehavior implements ILibEngine {
-  private static final int DVNum = 2; // Number of differential vectors, normally be 1 or 2
-  public double FACTOR = 0.5; // scale constant: (0, 1.2], normally be 0.5
-  public double CR = 0.9; // crossover constant: [0, 1], normally be 0.1 or 0.9
+  //Number of differential vectors, normally be 1 or 2
+  private static final int DVNum = 2;
+
+  //scale constant: (0, 1.2], normally be 0.5
+  public double FACTOR = 0.5;
+
+  //crossover constant: [0, 1], normally be 0.1 or 0.9
+  public double CR = 0.9;
 
   // the own memory: store the point that generated in last learning cycle
   private SearchPoint pbest_t;

nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/deps/behavior/PSGTBehavior.java

@@ -64,20 +64,23 @@ import net.adaptivebox.space.DesignSpace;
 
 public class PSGTBehavior extends AbsGTBehavior {
   // Two normally choices for (c1, c2, weight), i.e., (2, 2, 0.4), or (1.494,
-  // 1.494, 0.729)
-  // The first is used in dissipative PSO (cf. [4]) as CL>0, and the second is
-  // achieved by using
-  // constriction factors (cf. [3])
+  // 1.494, 0.729) The first is used in dissipative PSO (cf. [4]) as CL>0, and
+  // the second is achieved by using constriction factors (cf. [3])
   public double c1 = 2;
   public double c2 = 2;
-  public double weight = 0.4; // inertia weight
 
-  public double CL = 0; // See ref[4], normally be 0.001~0.005
+  //inertia weight
+  public double weight = 0.4;
+
+  //See ref[4], normally be 0.001~0.005
+  public double CL = 0;
 
   // the own memory: store the point that generated in old learning cycle
   private BasicPoint pold_t;
+
   // the own memory: store the point that generated in last learning cycle
   private BasicPoint pcurrent_t;
+
   // the own memory: store the personal best point
   private SearchPoint pbest_t;
 
@@ -100,7 +103,8 @@ public class PSGTBehavior extends AbsGTBehavior {
         deltaxb = weight * (pcurrent_t.getLocation()[b] - pold_t.getLocation()[b])
             + c1 * Math.random() * (pbest_t.getLocation()[b] - pcurrent_t.getLocation()[b])
             + c2 * Math.random() * (gbest_t.getLocation()[b] - pcurrent_t.getLocation()[b]);
-// limitation for delta_x
+
+        // limitation for delta_x
         deltaxbm = 0.5 * designSpace.getMagnitudeIn(b);
         if (deltaxb < -deltaxbm) {
           deltaxb = -deltaxbm;
@@ -118,5 +122,4 @@ public class PSGTBehavior extends AbsGTBehavior {
     pold_t.importLocation(pcurrent_t);
     pcurrent_t.importLocation(trailPoint);
   }
-
 }

nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/encode/EvalStruct.java

@@ -42,13 +42,13 @@ public class EvalStruct {
     evalRes[0] = evalRes[1] = 0;
     for (int i = 0; i < evalElems.length; i++) {
       if (evalElems[i].isOptType()) {
-// The objectives (OPTIM type)
-// The multi-objective will be translated into single-objective
+        // The objectives (OPTIM type)
+        // The multi-objective will be translated into single-objective
         evalRes[1] += evalElems[i].evaluateOPTIM(targetValues[i]);
       } else {
-// The constraints (CONS type)
-// If evalRes[0] equals to 0, then be a feasible point, i.e. satisfies
-// all the constraints
+        // The constraints (CONS type)
+        // If evalRes[0] equals to 0, then be a feasible point, i.e. satisfies
+        // all the constraints
         evalRes[0] += evalElems[i].evaluateCONS(targetValues[i]);
       }
     }

nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/goodness/ACRComparator.java

@@ -51,8 +51,8 @@ public class ACRComparator implements IGoodnessCompareEngine, IUpdateCycleEngine
   public ACRComparator(Library lib, int T) {
     socialPool = lib;
     this.T = T;
-// set the (epsilon_t|t=0) as the maximum CONS value among the SearchPoints in
-// the library
+
+    // set the (epsilon_t|t=0) as the maximum CONS value among the SearchPoints in the library
     epsilon_t = lib.getExtremalVcon(true);
   }
 
@@ -74,8 +74,9 @@ public class ACRComparator implements IGoodnessCompareEngine, IUpdateCycleEngine
   }
 
   public void updateCycle(int t) {
-// calculates the ratio
+    // calculates the ratio
     double rn = (double) socialPool.getVconThanNum(epsilon_t) / (double) socialPool.getPopSize();
+
     if (t > TthR * T && T != -1) { // Forcing sub-rule
       epsilon_t *= BETAF;
     } else { // Ratio-keeping sub-rules

nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/goodness/BCHComparator.java

@@ -28,9 +28,7 @@ package net.adaptivebox.goodness;
 
 public class BCHComparator implements IGoodnessCompareEngine {
 
-  /*
-   * check the magnitude of two array, the frontal is more important
-   **/
+  /* check the magnitude of two array, the frontal is more important */
   private static int compareArray(double[] fit1, double[] fit2) {
     for (int i = 0; i < fit1.length; i++) {
       if (fit1[i] > fit2[i]) {

nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/goodness/IGoodnessCompareEngine.java

@@ -29,9 +29,13 @@ public abstract interface IGoodnessCompareEngine {
   int LESS_THAN = 0;
 
   /**
-   * check the magnitude of two IEncodeEngine LARGER_THAN: goodness1 is worse than
-   * goodness2 LESS_THAN: goodness1 is better than goodness2 EQUAL_TO : goodness1
-   * is equal to goodness2
-   **/
+   * check the magnitude of two IEncodeEngine
+   *
+   * LARGER_THAN: goodness1 is worse than goodness2
+   *
+   * LESS_THAN: goodness1 is better than goodness2
+   *
+   * EQUAL_TO : goodness1 is equal to goodness2
+   */
   int compare(double[] goodness1, double[] goodness2);
 }

nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/knowledge/Library.java

@@ -99,5 +99,4 @@ public class Library {
     }
     return num;
   }
-
 }

nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/knowledge/SearchPoint.java

@@ -25,8 +25,7 @@ import net.adaptivebox.space.BasicPoint;
 
 public class SearchPoint extends BasicPoint implements IEncodeEngine {
   // store the encode information for goodness evaluation
-  // encodeInfo[0]: the sum of constraints (if it equals to 0, then be a feasible
-  // point)
+  // encodeInfo[0]: the sum of constraints (if it equals to 0, then be a feasible point)
   // encodeInfo[1]: the value of objective function
   private final double[] encodeInfo = new double[2];
   private double objectiveValue;
@@ -68,5 +67,4 @@ public class SearchPoint extends BasicPoint implements IEncodeEngine {
   public boolean isFeasible() {
     return encodeInfo[0] == 0; // no constraint violations
   }
-
-}
\ No newline at end of file
+}

nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/problem/ProblemEncoder.java

@@ -85,11 +85,13 @@ public abstract class ProblemEncoder {
 
   // evaluate the point into encoded information
   public void evaluate(SearchPoint point) {
-// copy to temp point
+    // copy to temp point
     System.arraycopy(point.getLocation(), 0, this.tempLocation, 0, tempLocation.length);
-// mapping the temp point to original search space S
+
+    // mapping the temp point to original search space S
     designSpace.getMappingPoint(tempLocation);
-// calculate based on the temp point
+
+    // calculate based on the temp point
     calcTargets(tempResponseSet, tempLocation);
     evalStruct.evaluate(point.getEncodeInfo(), tempResponseSet);
     point.setObjectiveValue(tempResponseSet[0]);

nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/sco/SCAgent.java

@@ -43,22 +43,24 @@ import net.adaptivebox.knowledge.SearchPoint;
 
 public class SCAgent {
 
-  // Describes the problem to be solved (encode the point into intermediate
-  // information)
+  // Describes the problem to be solved (encode the point into intermediate information)
   private ProblemEncoder problemEncoder;
+
   // Forms the goodness landscape
   private IGoodnessCompareEngine specComparator;
 
   // the coefficients of SCAgent
   private static final int TaoB = 2;
-  // The early version set TaoW as the size of external library (NL), but 4 is
-  // often enough
+
+  // The early version set TaoW as the size of external library (NL), but 4 is often enough
   private static final int TaoW = 4;
 
   // The referred external library
   private Library externalLib;
+
   // store the point that generated in current learning cycle
   private SearchPoint trailPoint;
+
   // the own memory: store the point that generated in last learning cycle
   private SearchPoint pcurrent_t;
 
@@ -77,9 +79,10 @@ public class SCAgent {
   }
 
   public SearchPoint generatePoint() {
-// generate a new point
+    // generate a new point
     generatePoint(trailPoint);
-// evaluate the generated point
+
+    // evaluate the generated point
     problemEncoder.evaluate(trailPoint);
     return trailPoint;
   }
@@ -87,13 +90,14 @@ public class SCAgent {
   private void generatePoint(ILocationEngine tempPoint) {
     SearchPoint Xmodel, Xrefer, libBPoint;
 
-// choose Selects a better point (libBPoint) from externalLib (L) based
-// on tournament selection
+    // choose Selects a better point (libBPoint) from externalLib (L) based
+    // on tournament selection
     int xb = externalLib.tournamentSelection(specComparator, TaoB, true);
     libBPoint = externalLib.getSelectedPoint(xb);
-// Compares pcurrent_t with libBPoint
-// The better one becomes model point (Xmodel)
-// The worse one becomes refer point (Xrefer)
+
+    // Compares pcurrent_t with libBPoint
+    // The better one becomes model point (Xmodel)
+    // The worse one becomes refer point (Xrefer)
     if (specComparator.compare(pcurrent_t.getEncodeInfo(),
         libBPoint.getEncodeInfo()) == IGoodnessCompareEngine.LARGER_THAN) {
       Xmodel = libBPoint;
@@ -102,19 +106,22 @@ public class SCAgent {
       Xmodel = pcurrent_t;
       Xrefer = libBPoint;
     }
-// observational learning: generates a new point near the model point, which
-// the variation range is decided by the difference of Xmodel and Xrefer
+
+    // observational learning: generates a new point near the model point, which
+    // the variation range is decided by the difference of Xmodel and Xrefer
     inferPoint(tempPoint, Xmodel, Xrefer, problemEncoder.getDesignSpace());
   }
 
   // 1. Update the current point into the external library
   // 2. Replace the current point by the generated point
   public void updateInfo() {
-// Selects a bad point kw from TaoW points in Library
+    // Selects a bad point kw from TaoW points in Library
     int xw = externalLib.tournamentSelection(specComparator, TaoW, false);
-// Replaces kw with pcurrent_t
+
+    // Replaces kw with pcurrent_t
     externalLib.getSelectedPoint(xw).importPoint(pcurrent_t);
-// Replaces pcurrent_t (x(t)) with trailPoint (x(t+1))
+
+    // Replaces pcurrent_t (x(t)) with trailPoint (x(t+1))
     pcurrent_t.importPoint(trailPoint);
   }
 

nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/space/DesignDim.java

@@ -42,5 +42,4 @@ public class DesignDim {
       return paramBound.maxValue - Math.rint((paramBound.maxValue - value) / grain) * grain;
     }
   }
-
 }

nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/space/DesignSpace.java

@@ -70,5 +70,4 @@ public class DesignSpace {
       }
     }
   }
-
 }