public void TrainClassifier(DataTable table)
{
dataSet2.Tables.Add(table);
DataTable GaussianDistribution = dataSet2.Tables.Add("Gaussian");
GaussianDistribution.Columns.Add(table.Columns[0].ColumnName);
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");
}
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] = VarianceAndMean.Mean(SelectRows(table, i, string.Format("{0} = '{1}'", table.Columns[0].ColumnName, results[j].Name)));
row[++a] = VarianceAndMean.Variance(SelectRows(table, i, string.Format("{0} = '{1}'", table.Columns[0].ColumnName, results[j].Name)));
a++;
}
}
}
//الميثود الخاصة بعملية التصنيف
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 = VarianceAndMean.NormalDist(obj[b - 1], mean, VarianceAndMean.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];
}
score.Add(results[i].Name, finalScore * 0.5);
}
//اختبار اكبر قيمة احتمال للاصابة بالمرض
double maxOne = score.Max(c => c.Value);
double minOne = score.Min(c => c.Value);
var name = (from c in score
where c.Value == maxOne
select c.Key).First();
//خرج الخوارزمية وهي قيمة الحالة وقيمة الاحتمال
string r = "";
if (name == "1")
{
r = "Positive";
}
else
{
r = "Negative";
}
return("The Result is " + r + " and the probability is " + maxOne);
}
//الميثود الخاص بعملية تدريب الخوارزمية على البيانات
public void TrainClassifier(DataTable table2)
{
dataSet.Tables.Add(table2);
//table
//استخدام التوزع الغوصي
DataTable GaussianDistribution = dataSet.Tables.Add("Gaussian");
GaussianDistribution.Columns.Add(table2.Columns[0].ColumnName);
//columns
for (int i = 1; i < table2.Columns.Count; i++)
{
GaussianDistribution.Columns.Add(table2.Columns[i].ColumnName + "Mean");
GaussianDistribution.Columns.Add(table2.Columns[i].ColumnName + "Variance");
}
//calc data
var results = (from myRow in table2.AsEnumerable()
group myRow by myRow.Field <string>(table2.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 < table2.Columns.Count; i++)
{
//حساب التبياين
row[a] = VarianceAndMean.Mean(SelectRows(table2, i, string.Format("{0} = '{1}'", table2.Columns[0].ColumnName, results[j].Name)));
//حساب المتوسط الحسابي
row[++a] = VarianceAndMean.Variance(SelectRows(table2, i, string.Format("{0} = '{1}'", table2.Columns[0].ColumnName, results[j].Name)));
a++;
}
}
}
