public void TestQnOnPrepAttachData()
{
var model = new QNTrainer().TrainModel(100,
new TwoPassDataIndexer(PrepAttachDataUtility.CreateTrainingStream(), 1));
TestModel(model, 0.8155484030700668);
}
public void TestTrainModelReturnsAqnModel()
{
using (var eventStream = new RealValueFileEventStream(Tests.GetFullPath(@"/opennlp/data/maxent/real-valued-weights-training-data.txt"), Encoding.UTF8)) {
var di = new OnePassRealValueDataIndexer(eventStream, 1);
var trainer = new QNTrainer();
var model = trainer.TrainModel(Iterations, di);
Assert.NotNull(model);
}
}
public void TestSerializationModel()
{
using (var eventStream = new RealValueFileEventStream(Tests.GetFullPath(@"/opennlp/data/maxent/real-valued-weights-training-data.txt"), Encoding.UTF8)) {
var di = new OnePassRealValueDataIndexer(eventStream, 1);
var trainer = new QNTrainer();
var model = trainer.TrainModel(Iterations, di);
Assert.NotNull(model);
QNModel deserialized;
using (var mem = new MemoryStream()) {
using (var modelWriter = new GenericModelWriter(model, new UnclosableStream(mem))) {
modelWriter.Persist();
modelWriter.Close();
}
mem.Flush();
mem.Seek(0, SeekOrigin.Begin);
using (var modelReader = new GenericModelReader(new BinaryFileDataReader(mem)))
deserialized = modelReader.GetModel() as QNModel;
}
Assert.NotNull(deserialized);
Assert.True(model.Equals(deserialized));
var features2Classify = new [] {
"feature2", "feature3", "feature3",
"feature3", "feature3", "feature3",
"feature3", "feature3", "feature3",
"feature3", "feature3", "feature3"
};
var eval01 = model.Eval(features2Classify);
var eval02 = deserialized.Eval(features2Classify);
Assert.AreEqual(eval01.Length, eval02.Length);
for (var i = 0; i < eval01.Length; i++)
{
Assert.AreEqual(eval01[i], eval02[i], 0.00000001);
}
}
}
public void TestInTinyDevSet()
{
using (
var eventStream = new RealValueFileEventStream(Tests.GetFullPath(@"/opennlp/data/maxent/real-valued-weights-training-data.txt"), Encoding.UTF8)) {
var di = new OnePassRealValueDataIndexer(eventStream, 1);
var trainer = new QNTrainer();
var model = trainer.TrainModel(Iterations, di);
Assert.NotNull(model);
var features2Classify = new[] {
"feature2", "feature3", "feature3",
"feature3", "feature3", "feature3",
"feature3", "feature3", "feature3",
"feature3", "feature3", "feature3"
};
var eval = model.Eval(features2Classify);
Assert.NotNull(eval);
}
}
