private static void TestNaiveBayes() { var data = LoadDataFromfCSV("Data.csv"); var fixedData = TableFixedData.FromTableData(data); var samples = TableFixedData.ToSample(fixedData); var columnsTypes = fixedData.ColumnDataTypes; var algorithm = new NaiveBayesClassifierOld(fixedData); var algorithm1 = new NaiveBayesClassifier(samples, fixedData.ClassesValue.Length, columnsTypes); var dataRow = data.ToList()[2]; var className = algorithm.Compute(dataRow); var classId = algorithm1.Compute(fixedData.GetSample(dataRow)); var className1 = fixedData.ClassesValue[classId]; int missed = 0; for (int index = 0; index < 50; index++) { var row = data.ToList()[index]; var estimatedClassName = algorithm.Compute(row); if (estimatedClassName != row.Class) { missed++; } } }
private static void TestNaiveBayes() { var data = LoadDataFromfCSV("Data.csv"); var fixedData = TableFixedData.FromTableData(data); var samples = TableFixedData.ToSample(fixedData); var columnsTypes = fixedData.ColumnDataTypes; var algorithm = new NaiveBayesClassifierOld(fixedData); var algorithm1 = new NaiveBayesClassifier(samples,fixedData.ClassesValue.Length,columnsTypes); var dataRow = data.ToList()[2]; var className = algorithm.Compute(dataRow); var classId = algorithm1.Compute(fixedData.GetSample(dataRow)); var className1 = fixedData.ClassesValue[classId]; int missed = 0; for (int index = 0; index < 50; index++) { var row = data.ToList()[index]; var estimatedClassName = algorithm.Compute(row); if (estimatedClassName != row.Class) { missed++; } } }