public void Train(List <Point> points)
{
//double[][] initCentroids = new double[2][];
//initCentroids[0] = new double[] { points.First().X, points.First().Y };
//initCentroids[1] = new double[] { points.Last().X, points.Last().Y };
//m_Settings.InitialCentroids = initCentroids;
m_CurrentData = getDataFromPoints(points);
KMeansFunctionRecognitonScore res = m_LearningApi.RunBatch() as KMeansFunctionRecognitonScore;
}
/// <summary>
/// Trains the K-Means algorithm using coordinates.
/// </summary>
/// <param name="points">Coordinate Points</param>
public void Train(List <Coordinates> points)
{
m_CurrentData = GetDataFromPoints(points);
KMeansFunctionRecognitonScore res = m_LearningApi.RunBatch() as KMeansFunctionRecognitonScore;
}
public void Test_FunctionRecognition()
{
int numAttributes = 3;
int numClusters = 2;
int maxCount = 300;
// a value in % representing the tolerance to possible outliers
double tolerance = 0;
// directory to load
string loadDirectory = rootFolder + "Functions\\";
// directory to save
string saveDirectory = rootFolder + "Function Recognition\\";
string TrainedFuncName = "SIN_SIN X";
/*
* // functions' paths
* string[] FunctionPaths = new string[]
* {
* loadDirectory + TrainedFuncName + "\\NRP5-10\\" + TrainedFuncName + " SimilarFunctions Normalized NRP5-10.csv",
* loadDirectory + "COS X\\NRP5-10\\COS X SimilarFunctions Normalized NRP5-10.csv",
* loadDirectory + "Triangular\\NRP5-10\\Triangular SimilarFunctions Normalized NRP5-10.csv",
* loadDirectory + "Square\\NRP5-10\\Square SimilarFunctions Normalized NRP5-10.csv",
* loadDirectory + "Line -X\\NRP5-10\\Line -X SimilarFunctions Normalized NRP5-10.csv"
* };*/
/*
* // functions' paths
* string[] FunctionPaths = new string[]
* {
* loadDirectory + TrainedFuncName + "\\NRP5-10\\" + TrainedFuncName + " SimilarFunctions NRP5-10.csv",
* loadDirectory + "5 SIN 2X\\NRP5-10\\5 SIN 2X SimilarFunctions NRP5-10.csv"
* };*/
/*
* // functions' paths
* string[] FunctionPaths = new string[]
* {
* loadDirectory + TrainedFuncName + "\\NRP5-10\\" + TrainedFuncName + " SimilarFunctions Normalized NRP5-10.csv",
* loadDirectory + "SIN 1.5X\\NRP5-10\\SIN 1.5X SimilarFunctions Normalized NRP5-10.csv",
* loadDirectory + "SIN 2X\\NRP5-10\\SIN 2X SimilarFunctions Normalized NRP5-10.csv"
* };*/
// functions' paths
string[] FunctionPaths = new string[]
{
loadDirectory + TrainedFuncName + "\\NRP5-10\\" + TrainedFuncName + " SimilarFunctions Normalized NRP5-10.csv",
loadDirectory + "SIN_COS X\\NRP5-10\\SIN_COS X SimilarFunctions Normalized NRP5-10.csv",
loadDirectory + "COS_COS X\\NRP5-10\\COS_COS X SimilarFunctions Normalized NRP5-10.csv",
loadDirectory + "COS_SIN X\\NRP5-10\\COS_SIN X SimilarFunctions Normalized NRP5-10.csv"
};
int numTrainFun = 800;
int numTestFun = 200;
double[][] loadedSimFunctions = Helpers.LoadFunctionData(FunctionPaths[0]);
int numLoadedFunc = 0;
ClusteringSettings settings = new ClusteringSettings(maxCount, numClusters, numAttributes, KmeansAlgorithm: 2);
LearningApi api = new LearningApi();
api.UseActionModule <object, double[][]>((funcData, ctx) =>
{
numLoadedFunc++;
return(KMeansAlgorithm.transposeFunction(KMeansAlgorithm.selectFunction(loadedSimFunctions, numLoadedFunc, numAttributes)));
});
api.UseKMeansFunctionRecognitionModule(settings);
KMeansFunctionRecognitonScore res = new KMeansFunctionRecognitonScore();
//train
for (int i = 0; i < numTrainFun; i++)
{
res = api.Run() as KMeansFunctionRecognitonScore;
}
// save the formed clusters (just for plotting the function recognition results)
Helpers.Write2CSVFile(res.Centroids, saveDirectory + "Calculated Centroids.csv");
double[][] tempMaxDistance = new double[1][];
tempMaxDistance[0] = res.InClusterMaxDistance;
Helpers.Write2CSVFile(tempMaxDistance, saveDirectory + "Calculated Max Distance.csv");
// save the trained clusters in a persistant location (just for plotting the clusters)
Helpers.Write2CSVFile(res.Centroids, saveDirectory + TrainedFuncName + "\\Calculated Centroids C" + numClusters + ".csv");
Helpers.Write2CSVFile(tempMaxDistance, saveDirectory + TrainedFuncName + "\\Calculated Max Distance C" + numClusters + ".csv");
// start testing for function recognition
double[] testingResults = new double[numTestFun * FunctionPaths.Length];
double[][] data;
for (int l = 0; l < FunctionPaths.Length; l++)
{
loadedSimFunctions = Helpers.LoadFunctionData(FunctionPaths[l]);
for (int i = 0; i < numTestFun; i++)
{
data = KMeansAlgorithm.transposeFunction(KMeansAlgorithm.selectFunction(loadedSimFunctions, numTrainFun + i + 1, numAttributes));
var predictionResult = api.Algorithm.Predict(data, null) as KMeansFunctionRecognitionResult;
if (predictionResult.Result)
{
testingResults[i + l * numTestFun] = 1;
}
else
{
testingResults[i + l * numTestFun] = 0;
}
}
}
// save results
double[][] tempFunResults = new double[1][];
tempFunResults[0] = testingResults;
Helpers.Write2CSVFile(tempFunResults, saveDirectory + "Results.csv");
double[][] TFMat = createTrueFalseMatrix(testingResults, FunctionPaths.Length);
Helpers.Write2CSVFile(TFMat, saveDirectory + "TrueFalseMatrix.csv");
}
