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);
        }