public override IClusteringProblemData ImportData(string path) { var csvFileParser = new TableFileParser(); csvFileParser.Parse(path, csvFileParser.AreColumnNamesInFirstLine(path)); Dataset dataset = new Dataset(csvFileParser.VariableNames, csvFileParser.Values); // turn of input variables that are constant in the training partition var allowedInputVars = new List<string>(); var trainingIndizes = Enumerable.Range(0, (csvFileParser.Rows * 2) / 3); if (trainingIndizes.Count() >= 2) { foreach (var variableName in dataset.DoubleVariables) { if (dataset.GetDoubleValues(variableName, trainingIndizes).Range() > 0) allowedInputVars.Add(variableName); } } else { allowedInputVars.AddRange(dataset.DoubleVariables); } ClusteringProblemData clusteringData = new ClusteringProblemData(dataset, allowedInputVars); int trainingPartEnd = trainingIndizes.Last(); clusteringData.TrainingPartition.Start = trainingIndizes.First(); clusteringData.TrainingPartition.End = trainingPartEnd; clusteringData.TestPartition.Start = trainingPartEnd; clusteringData.TestPartition.End = csvFileParser.Rows; clusteringData.Name = Path.GetFileName(path); return clusteringData; }
public override ILogModellingProblemData ImportData(string path) { var csvFileParser = new TableFileParser(); csvFileParser.Parse(path, csvFileParser.AreColumnNamesInFirstLine(path)); var dataset = new Dataset(csvFileParser.VariableNames, csvFileParser.Values); string caseIDVar = dataset.VariableNames.First(); string timestampVar = dataset.VariableNames.First(); string activityVar = dataset.VariableNames.First(); ILogModellingProblemData logData = new LogModellingProblemData(dataset, caseIDVar, timestampVar, activityVar); IEnumerable <int> trainingIndizes = Enumerable.Range(0, (csvFileParser.Rows * 2) / 3); int trainingPartEnd = trainingIndizes.Last(); //TODO: when (if not removed) separating test and training, group by caseid logData.TrainingPartition.Start = trainingIndizes.First(); logData.TrainingPartition.End = trainingPartEnd; logData.TestPartition.Start = trainingPartEnd; logData.TestPartition.End = csvFileParser.Rows; logData.Name = Path.GetFileName(path); return(logData); }
public override IRegressionProblemData ImportData(string path) { TableFileParser csvFileParser = new TableFileParser(); csvFileParser.Parse(path, csvFileParser.AreColumnNamesInFirstLine(path)); Dataset dataset = new Dataset(csvFileParser.VariableNames, csvFileParser.Values); string targetVar = dataset.DoubleVariables.Last(); // turn off input variables that are constant in the training partition var allowedInputVars = new List<string>(); var trainingIndizes = Enumerable.Range(0, (csvFileParser.Rows * 2) / 3); if (trainingIndizes.Count() >= 2) { foreach (var variableName in dataset.DoubleVariables) { if (dataset.GetDoubleValues(variableName, trainingIndizes).Range() > 0 && variableName != targetVar) allowedInputVars.Add(variableName); } } else { allowedInputVars.AddRange(dataset.DoubleVariables.Where(x => !x.Equals(targetVar))); } IRegressionProblemData regressionData = new RegressionProblemData(dataset, allowedInputVars, targetVar); var trainingPartEnd = trainingIndizes.Last(); regressionData.TrainingPartition.Start = trainingIndizes.First(); regressionData.TrainingPartition.End = trainingPartEnd; regressionData.TestPartition.Start = trainingPartEnd; regressionData.TestPartition.End = csvFileParser.Rows; regressionData.Name = Path.GetFileName(path); return regressionData; }
public override ITimeSeriesPrognosisProblemData ImportData(string path) { TableFileParser csvFileParser = new TableFileParser(); csvFileParser.Parse(path, csvFileParser.AreColumnNamesInFirstLine(path)); Dataset dataset = new Dataset(csvFileParser.VariableNames, csvFileParser.Values); string targetVar = csvFileParser.VariableNames.Last(); IEnumerable<string> allowedInputVars = dataset.DoubleVariables.Where(x => !x.Equals(targetVar)); ITimeSeriesPrognosisProblemData timeSeriesPrognosisData = new TimeSeriesPrognosisProblemData(dataset, allowedInputVars, targetVar); int trainingPartEnd = csvFileParser.Rows * 2 / 3; timeSeriesPrognosisData.TrainingPartition.Start = 0; timeSeriesPrognosisData.TrainingPartition.End = trainingPartEnd; timeSeriesPrognosisData.TestPartition.Start = trainingPartEnd; timeSeriesPrognosisData.TestPartition.End = csvFileParser.Rows; int pos = path.LastIndexOf('\\'); if (pos < 0) timeSeriesPrognosisData.Name = path; else { pos++; timeSeriesPrognosisData.Name = path.Substring(pos, path.Length - pos); } return timeSeriesPrognosisData; }
public override ITimeSeriesPrognosisProblemData ImportData(string path) { TableFileParser csvFileParser = new TableFileParser(); csvFileParser.Parse(path, csvFileParser.AreColumnNamesInFirstLine(path)); Dataset dataset = new Dataset(csvFileParser.VariableNames, csvFileParser.Values); string targetVar = csvFileParser.VariableNames.Last(); IEnumerable <string> allowedInputVars = dataset.DoubleVariables.Where(x => !x.Equals(targetVar)); ITimeSeriesPrognosisProblemData timeSeriesPrognosisData = new TimeSeriesPrognosisProblemData(dataset, allowedInputVars, targetVar); int trainingPartEnd = csvFileParser.Rows * 2 / 3; timeSeriesPrognosisData.TrainingPartition.Start = 0; timeSeriesPrognosisData.TrainingPartition.End = trainingPartEnd; timeSeriesPrognosisData.TestPartition.Start = trainingPartEnd; timeSeriesPrognosisData.TestPartition.End = csvFileParser.Rows; int pos = path.LastIndexOf('\\'); if (pos < 0) { timeSeriesPrognosisData.Name = path; } else { pos++; timeSeriesPrognosisData.Name = path.Substring(pos, path.Length - pos); } return(timeSeriesPrognosisData); }
public override IRegressionProblemData ImportData(string path) { TableFileParser csvFileParser = new TableFileParser(); csvFileParser.Parse(path, csvFileParser.AreColumnNamesInFirstLine(path)); Dataset dataset = new Dataset(csvFileParser.VariableNames, csvFileParser.Values); string targetVar = dataset.DoubleVariables.Last(); // turn off input variables that are constant in the training partition var allowedInputVars = new List <string>(); var trainingIndizes = Enumerable.Range(0, (csvFileParser.Rows * 2) / 3); if (trainingIndizes.Count() >= 2) { foreach (var variableName in dataset.DoubleVariables) { if (dataset.GetDoubleValues(variableName, trainingIndizes).Range() > 0 && variableName != targetVar) { allowedInputVars.Add(variableName); } } } else { allowedInputVars.AddRange(dataset.DoubleVariables.Where(x => !x.Equals(targetVar))); } IRegressionProblemData regressionData = new RegressionProblemData(dataset, allowedInputVars, targetVar); var trainingPartEnd = trainingIndizes.Last(); regressionData.TrainingPartition.Start = trainingIndizes.First(); regressionData.TrainingPartition.End = trainingPartEnd; regressionData.TestPartition.Start = trainingPartEnd; regressionData.TestPartition.End = csvFileParser.Rows; regressionData.Name = Path.GetFileName(path); return(regressionData); }
public override IClusteringProblemData ImportData(string path) { var csvFileParser = new TableFileParser(); csvFileParser.Parse(path, csvFileParser.AreColumnNamesInFirstLine(path)); Dataset dataset = new Dataset(csvFileParser.VariableNames, csvFileParser.Values); // turn of input variables that are constant in the training partition var allowedInputVars = new List <string>(); var trainingIndizes = Enumerable.Range(0, (csvFileParser.Rows * 2) / 3); if (trainingIndizes.Count() >= 2) { foreach (var variableName in dataset.DoubleVariables) { if (dataset.GetDoubleValues(variableName, trainingIndizes).Range() > 0) { allowedInputVars.Add(variableName); } } } else { allowedInputVars.AddRange(dataset.DoubleVariables); } ClusteringProblemData clusteringData = new ClusteringProblemData(dataset, allowedInputVars); int trainingPartEnd = trainingIndizes.Last(); clusteringData.TrainingPartition.Start = trainingIndizes.First(); clusteringData.TrainingPartition.End = trainingPartEnd; clusteringData.TestPartition.Start = trainingPartEnd; clusteringData.TestPartition.End = csvFileParser.Rows; clusteringData.Name = Path.GetFileName(path); return(clusteringData); }