public void TrainModelAndTestModelTest(string trainFile, string testFile)
{
var trainModel = new TrainModel(2, 0.1);
var trainDataset = new ClassifiedDataset(trainFile, 64, 2);
trainModel.Train(trainDataset.GetClassifiedVectors());
var testModel = new TestModel(2, 0.1);
var testDataset = new UnclassifiedDataset(testFile, 64);
var testData = testDataset.GetDataVectors().Select(v => v.FeatureVector).ToArray();
var results = testModel.Test(trainModel.GetInferredPosterier(), testData);
Assert.That(results.Length, Is.EqualTo(testData.Length));
}
public Machine(int numOfClasses, int numOfFeatures, int[] featureSelection, double noise)
: base(numOfClasses, numOfFeatures, featureSelection, noise)
{
this.trainModel = new TrainModel(numOfClasses, noise);
this.testModel = new TestModel(numOfClasses, noise);
}
public void TrainModelIncrementalTrainTest(string trainFile1, string trainFile2)
{
var model = new TrainModel(2, 0.1);
var dataset1 = new ClassifiedDataset(trainFile1, 64, 2);
model.TrainIncremental(dataset1.GetClassifiedVectors());
var posteriors1 = model.GetInferredPosterier();
var dataset2 = new ClassifiedDataset(trainFile2, 64, 2);
model.TrainIncremental(dataset2.GetClassifiedVectors());
var posteriors2 = model.GetInferredPosterier();
Assert.AreEqual(posteriors1.Length, posteriors2.Length);
Assert.That(posteriors1.Intersect(posteriors2).Count() < posteriors1.Length);
}
public void TrainModelWithFeatureSelectionTest(string trainFile, int[] featureSelection)
{
var model = new TrainModel(2, 0.1);
var dataset = new ClassifiedDataset(trainFile, 64, featureSelection, 2);
model.Train(dataset.GetClassifiedVectors());
var posteriors = model.GetInferredPosterier();
Assert.That(posteriors.Length, Is.EqualTo(2));
foreach (var p in posteriors)
{
Assert.That(p.Dimension, Is.EqualTo(featureSelection.Length));
}
}
