Changeset 9976 for stable/HeuristicLab.Tests/HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis-3.4/SymbolicDataAnalysisExpressionTreeInterpreterTest.cs

Timestamp:

09/16/13 19:56:42 (11 years ago)

Author:

gkronber

Message:

#2021: merged linear interpreter for symbolic data analysis solutions to stable (r9828,r9830,r9837,r9840,r9871,r9944)

Location:

stable

Files:

• . (modified) (1 prop)
• HeuristicLab.Tests (modified) (1 prop)
• HeuristicLab.Tests/HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis-3.4/SymbolicDataAnalysisExpressionTreeInterpreterTest.cs (modified) (4 diffs)

stable/HeuristicLab.Tests/HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis-3.4/SymbolicDataAnalysisExpressionTreeInterpreterTest.cs

@@ -37 +37 @@
 
     [TestMethod]
-    [TestCategory("Problems.DataAnalysis")]
+    [TestCategory("Problems.DataAnalysis.Symbolic")]
     [TestProperty("Time", "long")]
-    public void SymbolicTimeSeriesPrognosisTreeInterpreterTypeCoherentGrammarPerformanceTest() {
-      TypeCoherentGrammarPerformanceTest(new SymbolicTimeSeriesPrognosisExpressionTreeInterpreter("y"), 12.5e6);
+    public void TimeSeriesPrognosisInterpreterTestTypeCoherentGrammarPerformance() {
+      TestTypeCoherentGrammarPerformance(new SymbolicTimeSeriesPrognosisExpressionTreeInterpreter("y"), 12.5e6);
     }
 
     [TestMethod]
-    [TestCategory("Problems.DataAnalysis")]
+    [TestCategory("Problems.DataAnalysis.Symbolic")]
     [TestProperty("Time", "long")]
-    public void SymbolicTimeSeriesPrognosisTreeInterpreterFullGrammarPerformanceTest() {
-      FullGrammarPerformanceTest(new SymbolicTimeSeriesPrognosisExpressionTreeInterpreter("y"), 12.5e6);
+    public void TimeSeriesPrognosisInterpreterTestFullGrammarPerformance() {
+      TestFullGrammarPerformance(new SymbolicTimeSeriesPrognosisExpressionTreeInterpreter("y"), 12.5e6);
     }
 
     [TestMethod]
-    [TestCategory("Problems.DataAnalysis")]
+    [TestCategory("Problems.DataAnalysis.Symbolic")]
     [TestProperty("Time", "long")]
-    public void SymbolicTimeSeriesPrognosisTreeInterpreterArithmeticGrammarPerformanceTest() {
-      ArithmeticGrammarPerformanceTest(new SymbolicTimeSeriesPrognosisExpressionTreeInterpreter("y"), 12.5e6);
-    }
-
-    private void TypeCoherentGrammarPerformanceTest(ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter interpreter, double nodesPerSecThreshold) {
+    public void TimeSeriesPrognosisInterpreterTestArithmeticGrammarPerformance() {
+      TestArithmeticGrammarPerformance(new SymbolicTimeSeriesPrognosisExpressionTreeInterpreter("y"), 12.5e6);
+    }
+
+    private void TestTypeCoherentGrammarPerformance(ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter interpreter, double nodesPerSecThreshold) {
       var twister = new MersenneTwister(31415);
       var dataset = Util.CreateRandomDataset(twister, Rows, Columns);
@@ -75 +75 @@
     }
 
-    private void FullGrammarPerformanceTest(ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter interpreter, double nodesPerSecThreshold) {
+    private void TestFullGrammarPerformance(ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter interpreter, double nodesPerSecThreshold) {
       var twister = new MersenneTwister(31415);
       var dataset = Util.CreateRandomDataset(twister, Rows, Columns);
@@ -92 +92 @@
     }
 
-    private void ArithmeticGrammarPerformanceTest(ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter interpreter, double nodesPerSecThreshold) {
+    private void TestArithmeticGrammarPerformance(ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter interpreter, double nodesPerSecThreshold) {
       var twister = new MersenneTwister(31415);
       var dataset = Util.CreateRandomDataset(twister, Rows, Columns);
@@ -115 +115 @@
     ///</summary>
     [TestMethod]
-    [TestCategory("Problems.DataAnalysis")]
+    [TestCategory("Problems.DataAnalysis.Symbolic")]
     [TestProperty("Time", "short")]
-    public void SymbolicDataAnalysisExpressionTreeInterpreterEvaluateTest() {
+    public void TimeSeriesPrognosisInterpreterTestEvaluation() {
       Dataset ds = new Dataset(new string[] { "Y", "A", "B" }, new double[,] {
         { 1.0, 1.0, 1.0 },