public void TrainModelTest(string trainFile, int chunkSize, int numOfChunk)
{
var model = new TrainModel(numOfChunk, 2, 64, 0.1);
var dataset = new ClassifiedDataset(trainFile, 64, 2);
int count = 0;
foreach (var chunk in dataset.GetClassifiedVectorsInChunks(chunkSize))
{
model.Train(chunk, count);
if (++count == numOfChunk)
{
break;
}
}
var weights = model.GetWeights();
Assert.That(weights.Length, Is.EqualTo(2));
foreach (var w in weights)
{
Assert.IsNotNull(w);
}
}
public void TrainModelAndTestModelTest(string trainFile, string testFile, int chunkSize, int numOfChunk)
{
var trainModel = new TrainModel(numOfChunk, 2, 64, 0.1);
var trainDataset = new ClassifiedDataset(trainFile, 64, 2);
var testModel = new TestModel(numOfChunk, 2, 0.1, trainModel.GetWeights());
var testDataset = new UnclassifiedDataset(testFile, 64);
int count = 0;
foreach (var chunk in trainDataset.GetClassifiedVectorsInChunks(chunkSize))
{
trainModel.Train(chunk, count);
if (++count == numOfChunk)
{
break;
}
}
var results = testModel.Test(testDataset.GetDataVectors().Select(v => v.FeatureVector).ToArray(), 0);
}
public Machine(int numOfClasses, int numOfFeatures, int numOfTrainChunk, int[] featureSelection, double noise)
: base(numOfClasses, numOfFeatures, featureSelection, noise)
{
this.trainModel = new TrainModel(numOfTrainChunk, numOfClasses, GetNumOfReturnFeatures(), noise);
this.testModel = new TestModel(1, numOfClasses, noise, this.trainModel.GetWeights());
}
