private void test_button(object sender, EventArgs e)
{
string algorithm = comboBox1.Text;
if (ac == null)
{
List <Itemset> eclatlitems = new List <Itemset>();
try
{
AllKeyWords = readFromFile(keyword_txt.Text);
}
catch (FileNotFoundException)
{
MessageBox.Show("Keywords error!");
}
try
{
eclatlitems = load_model("AR.model");
}
catch (FileNotFoundException)
{
MessageBox.Show("AR.model dont exist!");
}
ac = new AR(eclatlitems, AllKeyWords);
}
ac.Classification_Test(testmessages, algorithm);
write_mat(ac.testdocRule, ac.testlabels, "test");
///*******************************************************************/
//ac.Classification_Test(test_docword, algorithm);
///*******************************************************************/
List <string> result = readFromFile("RESULTES_" + algorithm);
double accuracy;
int TPos = 0;
int TNeg = 0;
int FPos = 0;
int FNeg = 0;
for (int i = 0; i < result.Count; i++)
{
if (result[i] == "True")
{
if (testlabel[i] == 1)
{
TPos++;
}
else
{
FPos++;
}
}
else
{
if (testlabel[i] == 0)
{
TNeg++;
}
else
{
FNeg++;
}
}
}
accuracy = (double)(TPos + TNeg) / result.Count;
double percision = (double)TPos / (TPos + FPos);
double recall = (double)TPos / (TPos + FNeg);
string[] row = { accuracy.ToString("0.0000"), TPos.ToString(), FPos.ToString(), TNeg.ToString(), FNeg.ToString(), percision.ToString("0.0000"), recall.ToString("0.0000") };
dataGridView1.Rows.Add(row);
}
