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); }