Changeset 9386 for branches/sluengo/HeuristicLab.Problems.TradeRules/Solution

Timestamp:

04/20/13 14:08:49 (12 years ago)

Author:

sluengo

Message:

 

File:

• branches/sluengo/HeuristicLab.Problems.TradeRules/Solution/TradeRulesSolutionBase.cs (modified) (4 diffs)

branches/sluengo/HeuristicLab.Problems.TradeRules/Solution/TradeRulesSolutionBase.cs

@@ -29 +29 @@
         private const string TrainingMeanErrorResultName = "Mean error (training)";
         private const string TestMeanErrorResultName = "Mean error (test)";
+        private const string TestTradeDaysResultName = "Trade days";
+        private const string TestNumberTradesResultName = "Number of trades";
+        private const string TestTotalTradesResultName = "Total trades";
+
 
         public new IRegressionModel Model
@@ -58 +62 @@
             get { return ((DoubleValue)this[TestCashResultName].Value).Value; }
             private set { ((DoubleValue)this[TestCashResultName].Value).Value = value; }
+        }
+        public double TradeDays
+        {
+            get { return ((DoubleValue)this[TestTradeDaysResultName].Value).Value; }
+            private set { ((DoubleValue)this[TestTradeDaysResultName].Value).Value = value; }
+        }
+        public double NumberTrades
+        {
+            get { return ((DoubleValue)this[TestNumberTradesResultName].Value).Value; }
+            private set { ((DoubleValue)this[TestNumberTradesResultName].Value).Value = value; }
+        }
+        public double TotalTrades
+        {
+            get { return ((DoubleValue)this[TestTotalTradesResultName].Value).Value; }
+            private set { ((DoubleValue)this[TestTotalTradesResultName].Value).Value = value; }
         }
         public double TrainingMeanSquaredError
@@ -129 +148 @@
     protected TradeRulesSolutionBase(IRegressionModel model, IRegressionProblemData problemData)
       : base(model, problemData) {
-          Add(new Result(TrainingCashResultName, "Cash obtained after training period in the stock market", new DoubleValue()));
+      Add(new Result(TrainingCashResultName, "Cash obtained after training period in the stock market", new DoubleValue()));
       Add(new Result(TestCashResultName, "Cash obtained after test period in the stock market", new DoubleValue()));
-      Add(new Result(TrainingMeanSquaredErrorResultName, "Mean of squared errors of the model on the training partition", new DoubleValue()));
-      Add(new Result(TestMeanSquaredErrorResultName, "Mean of squared errors of the model on the test partition", new DoubleValue()));
-      Add(new Result(TrainingMeanAbsoluteErrorResultName, "Mean of absolute errors of the model on the training partition", new DoubleValue()));
-      Add(new Result(TestMeanAbsoluteErrorResultName, "Mean of absolute errors of the model on the test partition", new DoubleValue()));
-      Add(new Result(TrainingSquaredCorrelationResultName, "Squared Pearson's correlation coefficient of the model output and the actual values on the training partition", new DoubleValue()));
-      Add(new Result(TestSquaredCorrelationResultName, "Squared Pearson's correlation coefficient of the model output and the actual values on the test partition", new DoubleValue()));
-      Add(new Result(TrainingRelativeErrorResultName, "Average of the relative errors of the model output and the actual values on the training partition", new PercentValue()));
-      Add(new Result(TestRelativeErrorResultName, "Average of the relative errors of the model output and the actual values on the test partition", new PercentValue()));
-      Add(new Result(TrainingNormalizedMeanSquaredErrorResultName, "Normalized mean of squared errors of the model on the training partition", new DoubleValue()));
-      Add(new Result(TestNormalizedMeanSquaredErrorResultName, "Normalized mean of squared errors of the model on the test partition", new DoubleValue()));
-      Add(new Result(TrainingMeanErrorResultName, "Mean of errors of the model on the training partition", new DoubleValue()));
-      Add(new Result(TestMeanErrorResultName, "Mean of errors of the model on the test partition", new DoubleValue()));
-    }
+      Add(new Result(TestTradeDaysResultName, "Number of trading days", new DoubleValue()));
+      Add(new Result(TestNumberTradesResultName, "Number of trades", new DoubleValue()));
+      Add(new Result(TestTotalTradesResultName, "Total trades", new DoubleValue()));
+     }
 
     [StorableHook(HookType.AfterDeserialization)]
@@ -194 +204 @@
         IEnumerable<double> originalTestValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices);
 
-        OnlineCalculatorError errorState;
-        double trainingMSE = OnlineMeanSquaredErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
-        TrainingMeanSquaredError = errorState == OnlineCalculatorError.None ? trainingMSE : double.NaN;
-        double testMSE = OnlineMeanSquaredErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
-        TestMeanSquaredError = errorState == OnlineCalculatorError.None ? testMSE : double.NaN;
-
-        double trainingMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
-        TrainingMeanAbsoluteError = errorState == OnlineCalculatorError.None ? trainingMAE : double.NaN;
-        double testMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
-        TestMeanAbsoluteError = errorState == OnlineCalculatorError.None ? testMAE : double.NaN;
-
-        double trainingR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
-        TrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR2 : double.NaN;
-        double testR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
-        TestRSquared = errorState == OnlineCalculatorError.None ? testR2 : double.NaN;
-
-        double trainingRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
-        TrainingRelativeError = errorState == OnlineCalculatorError.None ? trainingRelError : double.NaN;
-        double testRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
-        TestRelativeError = errorState == OnlineCalculatorError.None ? testRelError : double.NaN;
-
-        double trainingNMSE = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
-        TrainingNormalizedMeanSquaredError = errorState == OnlineCalculatorError.None ? trainingNMSE : double.NaN;
-        double testNMSE = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
-        TestNormalizedMeanSquaredError = errorState == OnlineCalculatorError.None ? testNMSE : double.NaN;
-
-        double trainingME = OnlineMeanErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
-        TrainingMeanError = errorState == OnlineCalculatorError.None ? trainingME : double.NaN;
-        double testME = OnlineMeanErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
-        TestMeanError = errorState == OnlineCalculatorError.None ? testME : double.NaN;
-
         double trainingCTR = OnlineTradeRulesCalculator.Calculate(estimatedTrainingValues, ProblemData, ProblemData.TrainingIndices);
         TrainingCash = trainingCTR;
         double testCTR = OnlineTradeRulesCalculator.Calculate(estimatedTestValues, ProblemData, ProblemData.TestIndices);
         TestCash = testCTR;
+        double testTradeDays = OnlineTradeRulesCalculator.getTradeDays();
+        TradeDays = testTradeDays;
+        double testNumberTrades = OnlineTradeRulesCalculator.getNumberTrades();
+        NumberTrades = testNumberTrades;
+        double testTotalTradeDays = OnlineTradeRulesCalculator.getTotalTradesDays();
+        TotalTrades = testTotalTradeDays;
+
     }