public bool InitNeuralBackPropagationTest()
{
// InitIrisMapperInJsonFormat_helper();
// Creates learning api object
LearningApi api = new LearningApi(TestHelpers.GetDescriptor());
// Initialize data provider
api.UseCsvDataProvider(m_IrisDataPath, ',', false, 1);
// Use mapper for data, which will extract (map) required columns
api.UseDefaultDataMapper();
// Use MinMax data normalizer
//api.UseMinMaxNormalizer();
// We could also use some other normalizer like Gaus data normalizer
//api.UseGaussNormalizer(m_stats.Select(x => x.Mean).ToArray(), m_stats.Select(x => x.StDev).ToArray());
// Prepares the ML Algoritm and setup parameters
api.UseBackPropagation(1, 0.2, 1.0, null);
api.Run();
IScore status = api.GetScore();
//api.Train(vector)
return(true);
}
public void RunPipelineTest()
{
// Creates learning api object
LearningApi api = new LearningApi(TestHelpers.GetDescriptor());
// Initialize data provider
api.UseCsvDataProvider(m_iris_data_path, ',', 1);
// Use mapper for data, which will extract (map) required columns
api.UseDefaultDataMapper();
// Use MinMax data normalizer
//api.UseMinMaxNormalizer(m_stats.Select(x => x.Min).ToArray(), m_stats.Select(x => x.Max).ToArray());
// We could also use some other normalizer like Gaus data normalizer
//api.UseGaussNormalizer(m_stats.Select(x => x.Mean).ToArray(), m_stats.Select(x => x.StDev).ToArray());
// Prepares the ML Algoritm and setup parameters
api.UseBackPropagation(1, 0.2, 1.0, null);
//start process of learning
api.Run();
// api.Train();
// api.TrainSample();
IScore status = api.GetScore();
//api.Train(vector)
return;
}
public void Save_Test()
{
// Creates learning api object
LearningApi api = new LearningApi(TestHelpers.GetDescriptor());
// Initialize data provider
api.UseCsvDataProvider(m_IrisDataPath, ',', false, 1);
// Use mapper for data, which will extract (map) required columns
api.UseDefaultDataMapper();
// Prepares the ML Algorithm and setup parameters
api.UseBackPropagation(1, 0.2, 1.0, null);
api.Save("model1");
var loadedApi = LearningApi.Load("model1");
Assert.True(((BackPropagationNetwork)loadedApi.Algorithm).learningRate == ((BackPropagationNetwork)api.Algorithm).learningRate);
}