private void button13_Click_2(object sender, EventArgs e)
{
int c;
double[] ranks;
int dG_With = 0;
ahp_sol_no_clustering = new MyAHPModel(CriteriaArr.GetLength(0), mybestchoice.Cases.Count);
ahp_sol_no_clustering.AddCriteria(CriteriaArr);
ahp_sol_no_clustering.AddCriterionRatedChoices(ChoicesArr_Non);
if (ahp_sol_no_clustering.CalculatedCriteria())
{
ranks = ahp_sol_no_clustering.CalculatedChoices(out c);
if (c != -1)
{
MessageBox.Show("عدم تناسق في تفضيلات البدائل المدخلة للمعيار رقم" + c.ToString());
return;
}
}
else
{
MessageBox.Show("عدم تناسق في تفضيلات المعايير المدخلة");
return;
}
dg_results_no_clustering.Columns.Add("SolutionID", "Solution ID");
dg_results_no_clustering.Columns.Add("SolutionWeight", "Solution Weight");
dg_results_no_clustering.Columns.Add("SolutionSim", "Solution Similarity");
for (int i = 0; i < ranks.Length; i++)
{
dg_results_no_clustering.Rows.Add();
dg_results_no_clustering.Rows[dG_With].Cells[0].Value = mybestchoice.Cases[i].GetFeature("id").GetFeatureValue().ToString();
dg_results_no_clustering.Rows[dG_With].Cells[1].Value = Math.Round(ranks[i] * 100, 2);
EuclideanSimilarity s = new EuclideanSimilarity();
List <Case> sols = Db.get_cases_condition(mybestchoice.Tablename_Standardization, mybestchoice.CaseName);
dg_results_no_clustering.Rows[dG_With].Cells[2].Value = Math.Round(s.Similarity(sols[i], mybestchoice.StandrizeProblem) * 100, 2);
dG_With++;
}
dg_results_no_clustering.Sort(dg_results_no_clustering.Columns["SolutionWeight"], ListSortDirection.Descending);
}
private void button11_Click_1(object sender, EventArgs e)
{
// display
int dG_NonR = 0;
dG_Non.Columns.Clear();
//dG_Non.Columns.Add("Porblem No ", "Porblem No ");
dG_Non.Columns.Add("SolNum", "Sol Num ");
dG_Non.Columns.Add("Weight ", "Weight ");
dG_Non.Columns.Add("Sim ", "Sim ");
List <Case> solutions = statistics.FindAlternative_WithClustering(statistics.exp_random_cases[problem_num]);
// call method
listBox1.Items.Add("Finding Alternatices by specifying best cluster");
double[] ranks;
int cri = 0;
Dictionary <int, double[, ]> choices;
double[,] criteriaarr;
MyAHPModel myahp = new MyAHPModel(statistics.count_citeria, solutions.Count);
criteriaarr = GenerateComparison.CriteriaComparisonMatrix;
myahp.AddCriteria(criteriaarr);
if (myahp.CalculatedCriteria())
{
choices = GenerateComparison.Create_All_Criteria_Choice_Comparison_Array(solutions);
myahp.AddCriterionRatedChoices(choices);
ranks = myahp.CalculatedChoices(out cri);
for (int i = 0; i < ranks.Length; i++)
{
dG_Non.Rows.Add();
dG_Non.Rows[dG_NonR].Cells[0].Value = solutions[i].GetFeature("id").GetFeatureValue().ToString();
dG_Non.Rows[dG_NonR].Cells[1].Value = Math.Round(ranks[i] * 100, 2);
EuclideanSimilarity s = new EuclideanSimilarity();
dG_Non.Rows[dG_NonR].Cells[2].Value = Math.Round(s.Similarity(solutions[i], statistics.exp_random_cases[problem_num]) * 100, 2);
dG_NonR++;
}
}
dG_Non.Sort(dG_Non.Columns[1], ListSortDirection.Descending);
// display Criteria
if (dG_Prob.Columns.Count == 0)
{
dG_Prob.Columns.Add("Criteria", " Criteria ");
dG_Prob.Columns.Add("AHP", "AHP");
dG_Prob.Columns.Add("FuzzyAHP", "Fuzzy AHP");
for (int i = 1; i < solutions[0].GetFeatures().Count - 1; i++)
{
dG_Prob.Rows.Add();
Feature f = (Feature)solutions[0].GetFeatures()[i];
dG_Prob.Rows[i - 1].Cells[0].Value = f.GetFeatureName().ToString();
dG_Prob.Rows[i - 1].Cells[1].Value = Math.Round(myahp.CriteriaWeights[i - 1] * 100, 2);
}
}
else
{
for (int i = 1; i < solutions[0].GetFeatures().Count - 1; i++)
{
Feature f = (Feature)solutions[0].GetFeatures()[i];
dG_Prob.Rows[i - 1].Cells[1].Value = Math.Round(myahp.CriteriaWeights[i - 1] * 100, 2);
}
}
dG_Prob.AutoResizeColumns();
dG_Prob.AutoSizeColumnsMode = DataGridViewAutoSizeColumnsMode.AllCells;
dG_Non.AutoResizeColumns();
dG_Non.AutoSizeColumnsMode = DataGridViewAutoSizeColumnsMode.AllCells;
}
