Пример #1
0
        public void GaussNormalizationTests_test1()
        {
            // Creates learning api object
            LearningApi api = new LearningApi(loadMetaData1());

            //Real dataset must be defined as object type, because data can be numeric, binary and classification
            api.UseActionModule <object[][], object[][]>((input, ctx) =>
            {
                return(GetRealDataSample());
            });

            //this call must be first in the pipeline
            api.UseDefaultDataMapper();

            //
            api.UseGaussNormalizer();

            //
            var result = api.Run() as double [][];


            //Test result for normalization
            var expected = GetGaussNormalizedDataSample();

            for (int i = 0; i < expected.Length; i++)
            {
                for (int j = 0; j < expected[0].Length; j++)
                {
                    Assert.Equal(Math.Round(result[i][j], 4), expected[i][j]);
                }
            }

            //
            return;
        }
Пример #2
0
        public void ContinuousTrainData()
        {
            double[][] initialCentroids = new double[4][];
            initialCentroids[0] = new double[] { 0.2, -4.0 };
            initialCentroids[1] = new double[] { 0.2, -6.0 };
            initialCentroids[2] = new double[] { 0.4, -4.0 };
            initialCentroids[3] = new double[] { 0.4, -6.0 };

            string[] attributes = new string[] { "x", "y" };

            int numAttributes = attributes.Length;  // 2 in this demo (x,y)
            int numClusters   = 4;
            int maxCount      = 300;

            SaveLoadSettings sett;

            var resp = SaveLoadSettings.JSON_Settings(@"C:\Data\Function1.json", out sett, true);

            AnomalyDetectionAPI kmeanApi = new AnomalyDetectionAPI(null, numClusters);

            LearningApi api = new LearningApi(loadMetaData1());

            api.UseActionModule <object[][], object[][]>((input, ctx) =>
            {
                var rawDatalist = getRealDataSample(@"C:\Data\Function1.csv").ToList();

                double[][] oldSamples;

                var nn = kmeanApi.GetPreviousSamples(sett, out oldSamples);

                if (oldSamples != null)
                {
                    foreach (var old in oldSamples)
                    {
                        var row = old.Cast <object>().ToArray();
                        rawDatalist.Add(row);
                    }
                }
                return(rawDatalist.ToArray());
            });

            //this call must be first in the pipeline
            api.UseDefaultDataMapper();

            api.UseGaussNormalizer();

            var rawData = api.Run() as double[][];

            Helpers.WriteToCSVFile(rawData);

            ClusteringSettings Settings = new ClusteringSettings(rawData, maxCount, numClusters, numAttributes, sett, KmeansAlgorithm: 1, InitialGuess: true, Replace: true);

            AnomalyDetectionResponse response = kmeanApi.ImportNewDataForClustering(Settings);
        }
Пример #3
0
        public void Training()
        {
            int cnt = 0;

            double[][] initialCentroids = new double[4][];
            initialCentroids[0] = new double[] { 0.4, 25.0 };
            initialCentroids[1] = new double[] { 0.4, 15.0 };
            initialCentroids[2] = new double[] { 0.6, 15.0 };
            initialCentroids[3] = new double[] { 0.6, 25.0 };

            string[] attributes = new string[] { "x", "y" };

            int numAttributes = attributes.Length;
            int numClusters   = 4;
            int maxCount      = 300;

            ClusteringSettings clusterSettings = new ClusteringSettings(maxCount, numClusters, numAttributes, KmeansAlgorithm: 1, Replace: true);

            AnomalyDetectionAPI      kmeanApi = new AnomalyDetectionAPI(clusterSettings); //AnomalyDetectionAPI(clusterSettings), Constractor should not be null when run Training()
            AnomalyDetectionResponse response;

            // Creates learning api object
            LearningApi api = new LearningApi(loadMetaData1());

            api.UseActionModule <object[][], object[][]>((input, ctx) =>
            {
                var rawDataArray = getData(cnt);

                return(rawDataArray);
            });

            api.UseDefaultDataMapper();
            api.UseGaussNormalizer();


            //
            for (int i = 0; i < 15; i++)
            {
                cnt = i;

                var rawData = api.Run() as double[][];

                response = kmeanApi.Training(rawData, initialCentroids);

                Helpers.WriteToCSVFile(kmeanApi.GetCentroid(), $"Data\\Centroid{i}.csv");

                //response = kmeanApi.Save($"Function{i}.json");
            }
        }
Пример #4
0
        public void ContinuousTrainData2()
        {
            int cnt = 0;

            double[][] initialCentroids = new double[4][];
            initialCentroids[0] = new double[] { 40.0, 10.0 };
            initialCentroids[1] = new double[] { 20.0, 10.0 };
            initialCentroids[2] = new double[] { 40.0, 20.0 };
            initialCentroids[3] = new double[] { 20.0, 20.0 };

            string[] attributes = new string[] { "x", "y" };

            int numAttributes = attributes.Length;  // 2 in this demo (x,y)
            int numClusters   = 4;
            int maxCount      = 300;

            SaveLoadSettings sett;

            var resp = SaveLoadSettings.JSON_Settings(@"C:\Data\Function1.json", out sett, true);

            AnomalyDetectionAPI kmeanApi = new AnomalyDetectionAPI(null, numClusters, initialCentroids);

            LearningApi api = new LearningApi(loadMetaData1());

            api.UseActionModule <object[][], object[][]>((input, ctx) =>
            {
                var rawDatalist = getData(cnt);

                return(rawDatalist);
            });

            //this call must be first in the pipeline
            api.UseDefaultDataMapper();

            api.UseGaussNormalizer();

            for (int i = 0; i < 15; i++)
            {
                cnt = i;

                var rawData = api.Run() as double[][];

                ClusteringSettings Settings = new ClusteringSettings(rawData, maxCount, numClusters, numAttributes, sett, KmeansAlgorithm: 1, InitialGuess: true, Replace: true);

                AnomalyDetectionResponse response = kmeanApi.ImportNewDataForClustering(Settings);
            }
        }
Пример #5
0
        public void TestWithNormalize_GaussAndCentroid()
        {
            double[][] initialCentroids = new double[4][];
            initialCentroids[0] = new double[] { 0.2, -4.0 };
            initialCentroids[1] = new double[] { 0.2, -6.0 };
            initialCentroids[2] = new double[] { 0.4, -4.0 };
            initialCentroids[3] = new double[] { 0.4, -6.0 };

            string[] attributes = new string[] { "x", "y" };
            // Creates learning api object
            LearningApi api = new LearningApi(loadMetaData1());

            //Real dataset must be defined as object type, because data can be numeric, binary and classification
            api.UseActionModule <object[][], object[][]>((input, ctx) =>
            {
                return(getRealDataSample(@"C:\Data\Function15.csv"));
            });

            //this call must be first in the pipeline
            api.UseDefaultDataMapper();

            api.UseGaussNormalizer();

            var rawData = api.Run() as double[][];

            int numAttributes = attributes.Length; // 2 in this demo (height,weight)
            int numClusters   = 4;                 // vary this to experiment (must be between 2 and number data tuples)
            int maxCount      = 300;               // trial and error

            SaveLoadSettings sett;

            var resp = SaveLoadSettings.JSON_Settings(@"C:\Data\Function15.json", out sett, true);

            AnomalyDetectionAPI kmeanApi = new AnomalyDetectionAPI(rawData, numClusters);

            ClusteringSettings Settings = new ClusteringSettings(rawData, maxCount, numClusters, numAttributes, sett, KmeansAlgorithm: 1, InitialGuess: true, Replace: true);

            AnomalyDetectionResponse response = kmeanApi.ImportNewDataForClustering(Settings);
        }
Пример #6
0
        public void TestWithNormalize_Gauss()
        {
            // Creates learning api object
            LearningApi api = new LearningApi(loadMetaData1());

            //Real dataset must be defined as object type, because data can be numeric, binary and classification
            api.UseActionModule <object[][], object[][]>((input, ctx) =>
            {
                return(getRealDataSample(@"C:\Data\First.csv"));
            });

            //this call must be first in the pipeline
            api.UseDefaultDataMapper();

            //
            api.UseGaussNormalizer();

            //
            var result = api.Run() as double[][];

            Helpers.WriteToCSVFile(result);
        }