private void button2_Click(object sender, EventArgs e)
{
List <string> lstAttributes = new List <string>();
foreach (object item in listBox2.Items)
{
lstAttributes.Add(item.ToString());
}
DataTableEncodingManager encodingManager = new DataTableEncodingManager(_EncodingDictionary, lstAttributes, _DataTableToEncode, checkBox1.Checked, checkBox2.Checked);
encodingManager.EncodingCompleted += new WorkerCompletedEventHandler(encodingManager_EncodingCompleted);
encodingManager.EncodingProgress += new WorkerProgressUpdateEventHandler(encodingManager_EncodingProgress);
encodingManager.Run();
}
private void button5_Click(object sender, EventArgs e)
{
//Perform Prediction
CreateNeuralNetwork();
DataTable dt = new DataTable("Runtime Result");
dt.Columns.Add("Pattern");
dt.Columns.Add("Position");
dt.Columns.Add("Prediction");
DataTable peptideDataTable;
List <string> listOfTargetResidue = new List <string>();
listOfTargetResidue.Add(txtTargetResidues.Text);
int sizeOfPeptide = int.Parse(textBox2.Text);
peptideDataTable = ResidueBasedPeptideGenerator.ExtractPeptide(txtSequence.Text, listOfTargetResidue, sizeOfPeptide, false);
List <string> listOfInputAttributesStringCollectionType = new List <string>();
List <string> listOfInputAttributesListType = new List <string>();
for (int index = (-1 * sizeOfPeptide); index <= sizeOfPeptide; index++)
{
listOfInputAttributesStringCollectionType.Add("P" + index.ToString());
listOfInputAttributesListType.Add("P" + index.ToString());
}
DataTableEncodingManager encodingManager = new DataTableEncodingManager(codeDictionary, listOfInputAttributesListType, peptideDataTable, true, true);
encodingManager.Run();
DataTable encodedPeptideDataTable = encodingManager._EncodedDataTable;
int binaryStringLenghtPepAminoAcid = 0;
foreach (object key in codeDictionary.Keys)
{
if (codeDictionary[key.ToString()].Length > binaryStringLenghtPepAminoAcid)
{
binaryStringLenghtPepAminoAcid = codeDictionary[key.ToString()].Length;
}
}
listOfInputAttributesStringCollectionType = new List <string>();
int ctr;
for (int index = (-1 * sizeOfPeptide); index <= sizeOfPeptide; index++)
{
for (ctr = 1; ctr <= binaryStringLenghtPepAminoAcid; ctr++)
{
listOfInputAttributesStringCollectionType.Add("P" + index.ToString() + "_" + ctr.ToString());
}
}
runtimeEngine = new RuntimeEngine(1, ANN, listOfInputAttributesStringCollectionType);
DataRow row;
string pattern;
float predictedValue;
float patternIndex = 1;
float totalPattern = encodedPeptideDataTable.Rows.Count;
richTextBox1.Text = "Prediction Evaluation Started...";
DataRow encodedPatternRrow;
DataRow patternRow;
dataGridView2.DataSource = encodedPeptideDataTable;
for (patternIndex = 0; patternIndex < totalPattern; patternIndex++)
{
encodedPatternRrow = encodedPeptideDataTable.Rows[(int)patternIndex];
patternRow = peptideDataTable.Rows[(int)patternIndex];
pattern = "";
for (ctr = 0; ctr < listOfInputAttributesListType.Count; ctr++)
{
pattern = pattern + patternRow[listOfInputAttributesListType[ctr]].ToString();
}
//row
runtimeEngine.PatternRow = encodedPatternRrow;
predictedValue = runtimeEngine.Run();
row = dt.NewRow();
row["Pattern"] = pattern;
row["Prediction"] = predictedValue.ToString();
row["Position"] = patternRow["Position"].ToString();
dt.Rows.Add(row);
SetMessage(pattern + " => " + predictedValue.ToString());
richTextBox1.Text = Convert.ToString((patternIndex + 1)) + " - " + pattern + " => " + predictedValue.ToString() + Environment.NewLine;
}
dataGridView2.DataSource = dt;
}