C# (CSharp) NaiveBayesClassifierOld Beispiele

Beispiel #1

Datei: Program.cs Projekt: ranjancse26/DataMining

        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++;
                }
            }
        }

Beispiel #2

Datei: Program.cs Projekt: AdamStefan/DataMining

        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++;
                }

            }
        }