test refactoring

mlmc/tests/generators/TestNormalDistribution.cpp

-#include "generators/algorithms/NormalDistribution.hpp"
+#include "generators/NormalDistribution.hpp"
 #include "basics/Utilities.hpp"
 #include "TestEnvironment.hpp"
 #include "MeshesCreator.hpp"
@@ -6,94 +6,85 @@
 
 typedef std::vector<SampleID> TestSampleIDs;
 
+/*
+ * Todo: Find explanation for constants in EXPECT, i.e. sqrt(10.0)
+ */
+
 class TestNormalDistribution : public Test {
 protected:
-    TestSampleIDs sampleIDs;
+  TestSampleIDs sampleIDs;
 
-    Meshes *meshes;
+  Meshes *meshes;
 
-    int numSamples = 1e6;
+  int numSamples = 1e6;
 
-    void SetUp() override {
-        meshes = MeshesCreator("Interval").Create();
-        for (int number = 0; number < numSamples; number++)
-            sampleIDs.push_back(SampleID(0, number, false));
-    }
+  void SetUp() override {
+    meshes = MeshesCreator("Interval").Create();
+    for (int number = 0; number < numSamples; number++)
+      sampleIDs.push_back(SampleID(0, number, false));
+  }
 
-    void TearDown() override {
-        delete meshes;
-    }
+  void TearDown() override {
+    delete meshes;
+  }
 };
 
 TEST_F(TestNormalDistribution, NormalDistributionReal) {
-    NormalDistributionReal normalDist(*meshes);
-    RVector results;
-    for (auto &id : sampleIDs) {
-        normalDist.DrawSample(id);
-        results.push_back(normalDist.EvalSample());
-    }
-    double mean = results.Mean();
-    double var = results.Variance();
-    EXPECT_NEAR(0.0, mean, sqrt(1.0 / numSamples));
-    EXPECT_NEAR(1.0, var, sqrt(10.0 / numSamples));
+  NormalDistributionReal normalDist(*meshes);
+  RVector results;
+  for (auto &id : sampleIDs) results.push_back(normalDist.DrawAndEvalSample(id));
+  double mean = results.Mean();
+  double var = results.Variance();
+  EXPECT_NEAR(0.0, mean, sqrt(1.0 / numSamples));
+  EXPECT_NEAR(1.0, var, sqrt(10.0 / numSamples));
 }
 
 TEST_F(TestNormalDistribution, ComplexNormalDistribution) {
-    NormalDistributionComplex normalDist(*meshes);
-    CVector results;
-    for (auto &id : sampleIDs) {
-        normalDist.DrawSample(id);
-        results.push_back(normalDist.EvalSample());
-    }
-    Complex mean = results.Mean();
-    double var = results.Variance();
-    EXPECT_NEAR(0.0, mean.real(), sqrt(1.0 / numSamples));
-    EXPECT_NEAR(0.0, mean.imag(), sqrt(1.0 / numSamples));
-    EXPECT_NEAR(2.0, var, sqrt(100.0 / numSamples));
+  NormalDistributionComplex normalDist(*meshes);
+  CVector results;
+  for (auto &id : sampleIDs) results.push_back(normalDist.DrawAndEvalSample(id));
+  Complex mean = results.Mean();
+  double var = results.Variance();
+  EXPECT_NEAR(0.0, mean.real(), sqrt(1.0 / numSamples));
+  EXPECT_NEAR(0.0, mean.imag(), sqrt(1.0 / numSamples));
+  EXPECT_NEAR(2.0, var, sqrt(100.0 / numSamples));
 }
 
 TEST_F(TestNormalDistribution, NormalDistributionRVector) {
-    NormalDistributionRVector normalDist(*meshes, numSamples);
-    normalDist.DrawSample(sampleIDs[0]);
-    RVector results = normalDist.EvalSample();
-    EXPECT_NEAR(0.0, results.Mean(), sqrt(1.0 / numSamples));
-    EXPECT_NEAR(1.0, results.Variance(), sqrt(10.0 / numSamples));
+  NormalDistributionRVector normalDist(*meshes, numSamples);
+  RVector results = normalDist.DrawAndEvalSample(sampleIDs[0]);
+  EXPECT_NEAR(0.0, results.Mean(), sqrt(1.0 / numSamples));
+  EXPECT_NEAR(1.0, results.Variance(), sqrt(10.0 / numSamples));
 }
 
-
 TEST_F(TestNormalDistribution, NormalDistributionCVector) {
-    NormalDistributionCVector normalDist(*meshes, numSamples);
-    normalDist.DrawSample(sampleIDs[0]);
-    CVector results = normalDist.EvalSample();
-    Complex mean = results.Mean();
-    EXPECT_NEAR(0.0, mean.real(), sqrt(1.0 / numSamples));
-    EXPECT_NEAR(0.0, mean.imag(), sqrt(1.0 / numSamples));
-    EXPECT_NEAR(1.0, results.Variance() / 2.0, sqrt(10.0 / numSamples));
+  NormalDistributionCVector normalDist(*meshes, numSamples);
+  CVector results = normalDist.DrawAndEvalSample(sampleIDs[0]);
+  Complex mean = results.Mean();
+  EXPECT_NEAR(0.0, mean.real(), sqrt(1.0 / numSamples));
+  EXPECT_NEAR(0.0, mean.imag(), sqrt(1.0 / numSamples));
+  EXPECT_NEAR(1.0, results.Variance() / 2.0, sqrt(10.0 / numSamples));
 }
 
-
 TEST_F(TestNormalDistribution, NormalDistributionRMatrix) {
-    NormalDistributionRMatrix normalDist(*meshes, sqrt(numSamples), sqrt(numSamples));
-    normalDist.DrawSample(sampleIDs[0]);
-    RMatrix results = normalDist.EvalSample();
-    EXPECT_NEAR(0.0, results.Mean(), sqrt(1.0 / numSamples));
-    EXPECT_NEAR(1.0, results.Variance(), sqrt(10.0 / numSamples));
+  NormalDistributionRMatrix normalDist(*meshes, sqrt(numSamples), sqrt(numSamples));
+  RMatrix results = normalDist.DrawAndEvalSample(sampleIDs[0]);
+  EXPECT_NEAR(0.0, results.Mean(), sqrt(1.0 / numSamples));
+  EXPECT_NEAR(1.0, results.Variance(), sqrt(10.0 / numSamples));
 }
 
 TEST_F(TestNormalDistribution, NormalDistributionCMatrix) {
-    NormalDistributionCMatrix normalDist(*meshes, sqrt(numSamples), sqrt(numSamples));
-    normalDist.DrawSample(sampleIDs[0]);
-    CMatrix results = normalDist.EvalSample();
-    Complex mean = results.Mean();
-    EXPECT_NEAR(0.0, mean.real(), sqrt(1.0 / numSamples));
-    EXPECT_NEAR(0.0, mean.imag(), sqrt(1.0 / numSamples));
-    EXPECT_NEAR(1.0, results.Variance() / 2.0, sqrt(10.0 / numSamples));
+  NormalDistributionCMatrix normalDist(*meshes, sqrt(numSamples), sqrt(numSamples));
+  CMatrix results = normalDist.DrawAndEvalSample(sampleIDs[0]);
+  Complex mean = results.Mean();
+  EXPECT_NEAR(0.0, mean.real(), sqrt(1.0 / numSamples));
+  EXPECT_NEAR(0.0, mean.imag(), sqrt(1.0 / numSamples));
+  EXPECT_NEAR(1.0, results.Variance() / 2.0, sqrt(10.0 / numSamples));
 }
 
-
 int main(int argc, char **argv) {
-    return MppTest(
-        MppTestBuilder(argc, argv).
-        WithPPM()
-    ).RUN_ALL_MPP_TESTS();
+  return MppTest(
+    MppTestBuilder(argc, argv).
+      WithPPM()
+  ).RUN_ALL_MPP_TESTS();
 }
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