public void TrainClassifier(DataTable table, int iterator)
{
if (iterator == 0)
{
dataSet.Tables.Add(table);
}
//Pridedame lenteles stulpeli su gauso koeficientu
DataTable GaussianDistribution = new DataTable();
if (iterator == 0)
{
GaussianDistribution = dataSet.Tables.Add("Gaussian");
}
GaussianDistribution.Columns.Add(table.Columns[0].ColumnName);
//Vidurkio ir pasiskirstymo stulpeliai
for (int i = 1; i < table.Columns.Count; i++)
{
GaussianDistribution.Columns.Add(table.Columns[i].ColumnName + "Mean");
GaussianDistribution.Columns.Add(table.Columns[i].ColumnName + "Variance");
}
//Vykdomi skaiciavimai ir perduodami i lentele
var results = (from myRow in table.AsEnumerable()
group myRow by myRow.Field <string>(table.Columns[0].ColumnName) into g
select new { Name = g.Key, Count = g.Count() }).ToList();
for (int j = 0; j < results.Count; j++)
{
DataRow row = GaussianDistribution.Rows.Add();
row[0] = results[j].Name;
int a = 1;
for (int i = 1; i < table.Columns.Count; i++)
{
row[a] = Funkcijos.Mean(SelectRows(table, i, string.Format("{0} = '{1}'", table.Columns[0].ColumnName, results[j].Name)));
row[++a] = Funkcijos.Pasklidimas(SelectRows(table, i, string.Format("{0} = '{1}'", table.Columns[0].ColumnName, results[j].Name)));
a++;
}
}
// dataSet.Tables.Remove("Gaussian");
// dataSet.Tables.Remove(table);
}
public string Classify(double[] obj)
{
Dictionary <string, double> score = new Dictionary <string, double>();
var results = (from myRow in dataSet.Tables[0].AsEnumerable()
group myRow by myRow.Field <string>(dataSet.Tables[0].Columns[0].ColumnName) into g
select new { Name = g.Key, Count = g.Count() }).ToList();
for (int i = 0; i < results.Count; i++)
{
List <double> subScoreList = new List <double>();
int a = 1, b = 1;
for (int k = 1; k < dataSet.Tables["Gaussian"].Columns.Count; k = k + 2)
{
double mean = Convert.ToDouble(dataSet.Tables["Gaussian"].Rows[i][a]);
double variance = Convert.ToDouble(dataSet.Tables["Gaussian"].Rows[i][++a]);
double result = Funkcijos.GausoSkirstinys(obj[b - 1], mean, Funkcijos.SquareRoot(variance));
subScoreList.Add(result);
a++; b++;
}
double finalScore = 0;
for (int z = 0; z < subScoreList.Count; z++)
{
if (finalScore == 0)
{
finalScore = subScoreList[z];
continue;
}
finalScore = finalScore * subScoreList[z];
}
//*0.5
score.Add(results[i].Name, finalScore);
}
double maxOne = score.Max(c => c.Value);
var name = (from c in score
where c.Value == maxOne
select c.Key).First();
return(name);
}
