Changeset 12893 for branches/HeuristicLab.Problems.GrammaticalOptimization-gkr/HeuristicLab.Problems.GrammaticalOptimization.SymbReg/SymbolicRegressionProblem.cs

Timestamp:

08/24/15 13:56:27 (9 years ago)

Author:

gkronber

Message:

#2283: experiments on grammatical optimization algorithms (maxreward instead of avg reward, ...)

File:

• branches/HeuristicLab.Problems.GrammaticalOptimization-gkr/HeuristicLab.Problems.GrammaticalOptimization.SymbReg/SymbolicRegressionProblem.cs (modified) (7 diffs)

branches/HeuristicLab.Problems.GrammaticalOptimization-gkr/HeuristicLab.Problems.GrammaticalOptimization.SymbReg/SymbolicRegressionProblem.cs

@@ -72 +72 @@
     public string Name { get; private set; }
     private Random random;
-    private double lambda;
 
 
     // lambda should be tuned using CV
-    public SymbolicRegressionProblem(Random random, string partOfName, double lambda = 1.0, bool useConstantOpt = true) {
+    public SymbolicRegressionProblem(Random random, string partOfName, bool useConstantOpt = true) {
       var instanceProviders = new RegressionInstanceProvider[] 
       {new RegressionRealWorldInstanceProvider(),
@@ -106 +105 @@
       this.useConstantOpt = useConstantOpt;
 
-      this.Name = problemData.Name + string.Format("lambda={0:N2}", lambda);
+      this.Name = problemData.Name;
 
       this.N = problemData.TrainingIndices.Count();
@@ -140 +139 @@
 
       this.random = random;
-      this.lambda = lambda;
 
       // initialize ERC values
@@ -167 +165 @@
 
     public double Evaluate(string sentence) {
-      var extender = new ExpressionExtender();
-      sentence = extender.CanonicalRepresentation(sentence);
+      //var extender = new ExpressionExtender();
+      //sentence = extender.CanonicalRepresentation(sentence);
       if (useConstantOpt)
         return OptimizeConstantsAndEvaluate(sentence);
       else {
-        Debug.Assert(SimpleEvaluate(sentence) == SimpleEvaluate(extender.CanonicalRepresentation(sentence)));
+        //Debug.Assert(SimpleEvaluate(sentence) == SimpleEvaluate(extender.CanonicalRepresentation(sentence)));
         return SimpleEvaluate(sentence);
       }
@@ -199 +197 @@
     }
 
+    public bool IsOptimalPhrase(string phrase) {
+      return false;
+    }
 
 
@@ -229 +230 @@
       alglib.ndimensional_pfunc function_cx_1_func = CreatePFunc(compiledFunc);
       alglib.ndimensional_pgrad function_cx_1_grad = CreatePGrad(compiledFunc);
-      for (int t = 0; t < 10; t++) {
+      for (int t = 0; t < 1; t++) {
         double[] cStart = constants.Select(_ => Rand.RandNormal(random) * 10).ToArray();
         double[] cEnd;
@@ -278 +279 @@
         {
           var rowData = new double[d];
-          return HeuristicLab.Common.Extensions.RSq(y, Enumerable.Range(0, N).Select(i => {
+          return Math.Round(HeuristicLab.Common.Extensions.RSq(y, Enumerable.Range(0, N).Select(i => {
             for (int j = 0; j < d; j++) rowData[j] = x[i, j];
             return compiledFunc.Evaluate(c, rowData);
-          }));
+          })), 3);
         }
       }