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;
}
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);
}
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");
}
}
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);
}
}
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);
}
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);
}
