public void CostFunctionThrowsArgumentNullExceptionIfThetasIsNull()
{
// arrange
var dataSetMock = new Mock<IDataSet<bool, double>>();
dataSetMock.Setup(ds => ds.GetTrainingSamplesCount()).Returns(1);
dataSetMock.Setup(ds => ds.GetData()).Returns(new[] { TrainingSample.Create(true, new double[1]) });
var trainer = new LogisticRegressionClassifierTraining(1, 0.0, dataSetMock.Object);
// act
var cost = trainer.CostFunction(null, new double[0]);
// assert
Assert.Fail();
}
public void CostFunctionReturnsCorrectCostAndGradientForTestData2WithOnesInitialThetasSixDegreesPolynomAndRegularizationParameterOne()
{
// arrange
const int Degree = 6;
var mappedFeaturesCount = DataHelper.TwoMappedFeaturesToDegreeCount(Degree);
const double RegularizationParameter = 1.0;
var mappedDataSet2 = DataHelper.MapTwoFeaturesDataSetToDegree(this.dataSet2, Degree);
var classifierTraining = new LogisticRegressionClassifierTraining(mappedFeaturesCount, RegularizationParameter, mappedDataSet2);
IList<double> initialOnesThetas = Enumerable.Repeat(1.0, mappedFeaturesCount + 1).ToArray();
IList<double> grad = new double[mappedFeaturesCount + 1]; // will be updated
// act
var cost = classifierTraining.CostFunction(initialOnesThetas, grad);
// assert
Assert.AreEqual(2.1348483146660664, cost, Delta);
Assert.AreEqual(0.34604507367924525, grad[0], Delta);
Assert.AreEqual(0.085080732840233653, grad[1], Delta);
Assert.AreEqual(0.11852456917131907, grad[2], Delta);
Assert.AreEqual(0.15059159578437173, grad[3], Delta);
Assert.AreEqual(0.015914488662613836, grad[4], Delta);
Assert.AreEqual(0.16811439027324665, grad[5], Delta);
Assert.AreEqual(0.067120936535572842, grad[6], Delta);
Assert.AreEqual(0.032170528551636587, grad[7], Delta);
Assert.AreEqual(0.026043207959570054, grad[8], Delta);
Assert.AreEqual(0.10719727102949513, grad[9], Delta);
Assert.AreEqual(0.097258847173850457, grad[10], Delta);
Assert.AreEqual(0.010984331999342433, grad[11], Delta);
Assert.AreEqual(0.041956570008359576, grad[12], Delta);
Assert.AreEqual(0.0095721182298489117, grad[13], Delta);
Assert.AreEqual(0.12367775873639067, grad[14], Delta);
Assert.AreEqual(0.058955340734334054, grad[15], Delta);
Assert.AreEqual(0.018704087603607224, grad[16], Delta);
Assert.AreEqual(0.017293228450946573, grad[17], Delta);
Assert.AreEqual(0.023526654852795346, grad[18], Delta);
Assert.AreEqual(0.015130387245933704, grad[19], Delta);
Assert.AreEqual(0.098581230325589381, grad[20], Delta);
Assert.AreEqual(0.073283231256194875, grad[21], Delta);
Assert.AreEqual(0.010514468913800548, grad[22], Delta);
Assert.AreEqual(0.022705671362770082, grad[23], Delta);
Assert.AreEqual(0.0090483240173878, grad[24], Delta);
Assert.AreEqual(0.025635476344317537, grad[25], Delta);
Assert.AreEqual(0.00823079217024389, grad[26], Delta);
Assert.AreEqual(0.1060120397374798, grad[27], Delta);
}
public void CostFunctionReturnsCorrectCostAndGradientForTestData2WithZerosInitialThetasSixDegreesPolynomAndRegularizationParameterOne()
{
// arrange
const int Degree = 6;
var mappedFeaturesCount = DataHelper.TwoMappedFeaturesToDegreeCount(Degree);
const double RegularizationParameter = 1.0;
var mappedDataSet2 = DataHelper.MapTwoFeaturesDataSetToDegree(this.dataSet2, Degree);
var classifierTraining = new LogisticRegressionClassifierTraining(mappedFeaturesCount, RegularizationParameter, mappedDataSet2);
IList<double> initialThetas = new double[mappedFeaturesCount + 1];
IList<double> grad = new double[mappedFeaturesCount + 1]; // will be updated
// act
var cost = classifierTraining.CostFunction(initialThetas, grad);
// assert
Assert.AreEqual(0.69314718055994606, cost, Delta);
Assert.AreEqual(0.00847457627118644, grad[0], Delta);
Assert.AreEqual(0.018788093220338989, grad[1], Delta);
Assert.AreEqual(7.777118644068388E-05, grad[2], Delta);
Assert.AreEqual(0.050344639536355922, grad[3], Delta);
Assert.AreEqual(0.011501330787338986, grad[4], Delta);
Assert.AreEqual(0.037664847359550863, grad[5], Delta);
Assert.AreEqual(0.018355987221154262, grad[6], Delta);
Assert.AreEqual(0.0073239339112221545, grad[7], Delta);
Assert.AreEqual(0.00819244468389037, grad[8], Delta);
Assert.AreEqual(0.023476488865153241, grad[9], Delta);
Assert.AreEqual(0.039348623439160173, grad[10], Delta);
Assert.AreEqual(0.0022392390663969436, grad[11], Delta);
Assert.AreEqual(0.012860050337133708, grad[12], Delta);
Assert.AreEqual(0.0030959372024053963, grad[13], Delta);
Assert.AreEqual(0.03930281711039442, grad[14], Delta);
Assert.AreEqual(0.019970746726922388, grad[15], Delta);
Assert.AreEqual(0.0043298323241713673, grad[16], Delta);
Assert.AreEqual(0.0033864390190702, grad[17], Delta);
Assert.AreEqual(0.0058382207780586087, grad[18], Delta);
Assert.AreEqual(0.0044762906651224848, grad[19], Delta);
Assert.AreEqual(0.031007984901327702, grad[20], Delta);
Assert.AreEqual(0.031031244228507674, grad[21], Delta);
Assert.AreEqual(0.0010974023848666578, grad[22], Delta);
Assert.AreEqual(0.0063157079664203581, grad[23], Delta);
Assert.AreEqual(0.00040850300602094216, grad[24], Delta);
Assert.AreEqual(0.0072650431643416884, grad[25], Delta);
Assert.AreEqual(0.001376461747689044, grad[26], Delta);
Assert.AreEqual(0.038793636344838761, grad[27], Delta);
}
public void CostFunctionReturnsCorrectCostAndGradientForTestData1WithZerosInitialThetas()
{
// arrange
const int FeaturesCount = 2;
const double RegularizationParameter = 0.0; // no regularization
var classifierTraining = new LogisticRegressionClassifierTraining(FeaturesCount, RegularizationParameter, this.dataSet1);
IList<double> initialThetas = new double[3];
IList<double> grad = new double[3]; // will be updated
// act
var cost = classifierTraining.CostFunction(initialThetas, grad);
// assert
Assert.AreEqual(0.69314718055994584, cost, Delta);
Assert.AreEqual(-0.1, grad[0], Delta);
Assert.AreEqual(-12.00921658929115, grad[1], Delta);
Assert.AreEqual(-11.262842205513591, grad[2], Delta);
}
public void CostFunctionThrowsArgumentOutOfRangeExceptionIfOneOfTheTrainingSamplesAttributesLengthIsNotEqualToFeaturesCount()
{
// arrange
var dataSetMock = new Mock<IDataSet<bool, double>>();
dataSetMock.Setup(ds => ds.GetTrainingSamplesCount()).Returns(1);
dataSetMock.Setup(ds => ds.GetData()).Returns(new[] { TrainingSample.Create(true, new double[0]) });
var trainer = new LogisticRegressionClassifierTraining(1, 0.0, dataSetMock.Object);
// act
var cost = trainer.CostFunction(new double[2], new double[2]);
// assert
Assert.Fail();
}
public void CostFunctionThrowsNullReferenceExceptionIfOneOfTheTrainingSamplesAttributesIsNull()
{
// arrange
var dataSetMock = new Mock<IDataSet<bool, double>>();
dataSetMock.Setup(ds => ds.GetTrainingSamplesCount()).Returns(1);
dataSetMock.Setup(ds => ds.GetData()).Returns(new[] { TrainingSample.Create(true, (double[])null) });
var trainer = new LogisticRegressionClassifierTraining(1, 0.0, dataSetMock.Object);
// act
var cost = trainer.CostFunction(new double[2], new double[2]);
// assert
Assert.Fail();
}