/// <summary>
/// Simple Euclidean distance
/// cf. http://en.wikipedia.org/wiki/Euclidean_distance
/// Consider also: Manhattan, Chebyshev & Minkowski distances
/// </summary>
/// <param name="P1"></param>
/// <param name="P2"></param>
/// <returns></returns>
private double GetEuclideanD(Case P1, Case P2)
{
EuclideanSimilarity sim = new EuclideanSimilarity(); // the Pythagorean theorem we calculate the distance between two points
return(sim.Dissimilarity(P1, P2)); // vyzvraschaem distance
//return sim.Compare(P1, P2); // vyzvraschaem distance
}
private double distance(Case a, Case b) // the distance between two points
{
EuclideanSimilarity sim = new EuclideanSimilarity(); // the Pythagorean theorem we calculate the distance between two points
// return sim.Compare(a, b); // vyzvraschaem distance 1/distance
return(sim.Dissimilarity(a, b));
}
private void button3_Click_1(object sender, EventArgs e)
{
if (add_file())
{
List <Case> centroids = new List <Case>();
centroids = Db.get_Centroids(choice.Tablename_Standardization, choice.CaseName);
for (int i = 0; i < ListCases.Count; i++)
{
// Case s_case = new Case(0, ListCases[0].GetCaseName(), ListCases[0].GetCaseDescription());
List <Case> restandrize_array = choice.Re_standrize(ListCases[i]);//, out s_case);
Case s_case = restandrize_array.Last();
Case result_centroid = choice.Updated_cluster_Min_Centroid(s_case, centroids, restandrize_array);
if (result_centroid != null)
{
Feature f = (Feature)result_centroid.GetFeatures()[0];
s_case.GetFeature("cluster").SetFeatureValue(f.GetFeatureValue()); // اسناد العنقود المناسب للعنصر الجديد
Db.update_cluster(choice.Tablename_Standardization, s_case.GetFeature("id").GetFeatureValue().ToString(), Convert.ToInt32(f.GetFeatureValue()));
Db.update_cluster(choice.TableName, s_case.GetFeature("id").GetFeatureValue().ToString(), Convert.ToInt32(f.GetFeatureValue()));
double ess = 0;
// من ضمن الداتا سيت المقييسة يتم حساب المراكز لجميع العناقيد
for (int k = 0; k < Convert.ToInt32(choice.Clusters_Num); k++)
{
List <Case> S_Arr_Cluster = Db.get_cases_condition_cluster(choice.Tablename_Standardization, choice.CaseName, "cluster", k.ToString());
Feature cen = S_Arr_Cluster[0].GetFeature("cluster");
Case new_centroid = Kmeans.ReCalcCentroid(S_Arr_Cluster, Convert.ToInt32(cen.GetFeatureValue()));
Db.insert_case_to_table(new_centroid, choice.Tablename_Standardization);
EuclideanSimilarity es = new EuclideanSimilarity();
double d = 0;
foreach (Case c in S_Arr_Cluster)
{
d += es.Dissimilarity(c, new_centroid);
}
ess += d;
}
MessageBox.Show(ess.ToString());
}
}
dataGridView_old.DataSource = Db.get_table_contetnt(choice.TableName);
}
}
private void button1_Click_1(object sender, EventArgs e)
{
textBox1.Text = Db.Get_CostFunction(listBox1.SelectedValue.ToString()).ToString();
string t = listBox1.SelectedValue.ToString().ToString();
double sse = 0;
EuclideanSimilarity sim = new EuclideanSimilarity();
string c = listBox1.Text;
List <Case> cases = Db.get_cases_condition(Db.get_standriazation(t), c);
List <Case> centroids = Db.get_Centroids(Db.get_standriazation(t), c);
foreach (Case a in cases)
{
foreach (Case ce in centroids)
{
if (a.GetFeature("cluster").GetFeatureValue().ToString() == ce.GetFeature("id").GetFeatureValue().ToString())
{
sse += sim.Dissimilarity(a, ce); break;
}
}
}
textBox1.Text = sse.ToString();
}
private void button2_Click_1(object sender, EventArgs e)
{
if (add_file())
{
List <Case> centroids = new List <Case>();
centroids = Db.get_Centroids(choice.Tablename_Standardization, choice.CaseName);
List <string> Ids = new List <string>();
for (int i = 0; i < ListCases.Count; i++)
{
//Case s_case = new Case(0, ListCases[0].GetCaseName(), ListCases[0].GetCaseDescription());
// تقييس الداتا سيت بناءا على العنصر الجديد
var watch = System.Diagnostics.Stopwatch.StartNew();
List <Case> restandrize_array = choice.Re_standrize(ListCases[i]);
// العنصر الجديد بعد التقييس
Case s_case = restandrize_array.Last();
// نرى إلى أي عنقود ستمم اضافته
Case result_centroid = choice.Updated_cluster(s_case, centroids, restandrize_array);
watch.Stop();
var elapsedMs = watch.ElapsedMilliseconds;
label2.Text = "with clustring time " + elapsedMs.ToString();
// سيتم انضمامه لعنقود موجود
if (result_centroid != null)
{
Feature f = (Feature)result_centroid.GetFeatures()[0];
s_case.GetFeature("cluster").SetFeatureValue(f.GetFeatureValue()); // اسناد العنقود المناسب للعنصر الجديد
Db.update_cluster(choice.Tablename_Standardization, s_case.GetFeature("id").GetFeatureValue().ToString(), Convert.ToInt32(f.GetFeatureValue()));
Db.update_cluster(choice.TableName, s_case.GetFeature("id").GetFeatureValue().ToString(), Convert.ToInt32(f.GetFeatureValue()));
Ids.Add(f.GetFeatureValue().ToString());
} // end if
else // عنقود جديد
{
int k = Convert.ToInt32(choice.Clusters_Num) + 1;
// اضافة عنقود جديد
Db.update_cluster(choice.Tablename_Standardization, s_case.GetFeature("id").GetFeatureValue().ToString(), k);
Db.update_cluster(choice.TableName, s_case.GetFeature("id").GetFeatureValue().ToString(), k);
Case new_centroid = s_case;
new_centroid.GetFeature("cluster").SetFeatureValue(-1);
Db.insert_case_to_table(new_centroid, choice.Tablename_Standardization);
choice.Clusters_Num = k.ToString();
Db.update_clusterNum(choice.CaseName, k);
}
} // end for all new cases
if (Ids != null)
{
Db.del_centroids(choice.Tablename_Standardization, Ids);
double ess = 0;
// من ضمن الداتا سيت المقييسة يتم حساب المراكز لجميع العناقيد
foreach (string id in Ids)
{
List <Case> S_Arr_Cluster = Db.get_cases_condition_cluster(choice.Tablename_Standardization, choice.CaseName, "cluster", id);
Feature cen = S_Arr_Cluster[0].GetFeature("cluster");
Case new_centroid = Kmeans.ReCalcCentroid(S_Arr_Cluster, Convert.ToInt32(cen.GetFeatureValue()));
EuclideanSimilarity es = new EuclideanSimilarity();
double d = 0;
foreach (Case c in S_Arr_Cluster)
{
d += es.Dissimilarity(c, new_centroid);
}
ess += d;
Db.insert_case_to_table(new_centroid, choice.Tablename_Standardization);
}
// حساب distorion error
MessageBox.Show(ess.ToString());
}
dataGridView_old.DataSource = Db.get_table_contetnt(choice.TableName);
}
}