public void TestNaiveBayesOnPrepAttachData()
{
var model = NaiveBayesTests.TrainModel(PrepAttachDataUtility.CreateTrainingStream());
Assert.NotNull(model);
PrepAttachDataUtility.TestModel(model, 0.7897994553107205);
}
public void TestNaiveBayes4()
{
var model = NaiveBayesTests.TrainModel();
var label = "politics";
var context = new string[0];
var e = new Event(label, context);
NaiveBayesTests.TestModel(model, e, 7.0 / 12.0);
}
public void TestNaiveBayes3()
{
var model = NaiveBayesTests.TrainModel();
var label = "politics";
var context = new[] { "bow=united" };
var e = new Event(label, context);
// NaiveBayesTests.TestModel(model, e, 2.0 / 3.0); // Expected value without smoothing
NaiveBayesTests.TestModel(model, e, 0.6655036407766989); // Expected value with smoothing
}
public void TestNaiveBayes2()
{
var model = NaiveBayesTests.TrainModel();
var label = "sports";
var context = new[] { "bow=manchester", "bow=united" };
var e = new Event(label, context);
// NaiveBayesTests.TestModel(model, e, 1.0); // Expected value without smoothing
NaiveBayesTests.TestModel(model, e, 0.9658833555831029); // Expected value with smoothing
}
public void TestNaiveBayes1()
{
var model = NaiveBayesTests.TrainModel();
var label = "politics";
var context = new[] { "bow=united", "bow=nations" };
var e = new Event(label, context);
//NaiveBayesTests.TestModel(model, e, 1.0); // Expected value without smoothing
NaiveBayesTests.TestModel(model, e, 0.9681650180264167); // Expected value with smoothing
}
public void TestTextModelPersistence()
{
var model = NaiveBayesTests.TrainModel();
AbstractModel deserialized;
using (var data = new MemoryStream()) {
using (var writer = new PlainTextNaiveBayesModelWriter(model, new UnclosableStream(data)))
writer.Persist();
data.Seek(0, SeekOrigin.Begin);
using (var reader = new PlainTextNaiveBayesModelReader(data)) {
deserialized = reader.GetModel();
}
}
Assert.NotNull(deserialized);
Assert.IsInstanceOf <NaiveBayesModel>(deserialized);
}
