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 void GetClassifiedVectorsInChunksTest(string filePath, int chunkSize, int expectedNumOfChunk, int expectedLastChunkSize)
{
ClassifiedDataset dataset = new ClassifiedDataset(filePath, 64, 2);
int actualNumOfChunk = 0;
foreach (var chunk in dataset.GetClassifiedVectorsInChunks(chunkSize))
{
actualNumOfChunk++;
Assert.That(chunk.Length, Is.EqualTo(2));
if (actualNumOfChunk == expectedNumOfChunk)
{
Assert.That(chunk.Sum(c => c.Count), Is.EqualTo(expectedLastChunkSize));
}
else
{
Assert.That(chunk.Sum(c => c.Count), Is.EqualTo(chunkSize));
}
}
Assert.That(expectedNumOfChunk, Is.EqualTo(actualNumOfChunk));
}