private void generateButton_Click(object sender, RoutedEventArgs e)
{
Dictionary <string, int> classesDict = new Dictionary <string, int>();
double[][] inputs;
inputs = paramPassList.Select(list => list.ToArray()).ToArray();
var knn = new KNearestNeighbors(k: 4);
List <int> groupClasses = new List <int>();
int clasessCount = 0;
string currGroup = groupList[0].groupName;
classesDict.Add(currGroup, clasessCount);
foreach (var group in groupList)
{
if (!currGroup.Equals(group.groupName))
{
clasessCount++;
currGroup = group.groupName;
classesDict.Add(currGroup, clasessCount);
}
for (int i = 0; i < group.groupSize; i++)
{
groupClasses.Add(clasessCount);
objNumber++;
}
}
objNumber++;
int[] outputs;
outputs = groupClasses.ToArray();
// We learn the algorithm:
knn.Learn(inputs, outputs);
var cm = GeneralConfusionMatrix.Estimate(knn, inputs, outputs);
// We can use it to estimate measures such as
double error = cm.Error; // should be
double acc = cm.Accuracy; // should be
double kappa = cm.Kappa; // should be
List <int> testOutputsList = new List <int>();
List <double[]> testInputsList = new List <double[]>();
using (var dialog = new System.Windows.Forms.FolderBrowserDialog())
{
System.Windows.Forms.DialogResult result = dialog.ShowDialog();
if (result == System.Windows.Forms.DialogResult.OK)
{
string resultsToWrite = "";
foreach (var item in groupScoreList)
{
int counter = 0;
for (int i = 0; i < item.groupSize; i++)
{
while (counter != 100)
{
List <double> next = new List <double>();
for (int j = 0; j < paramSize; j++)
{
next.Add(rnd.Next(2));
}
double[] paramValues = next.ToArray();
double scoreValue = knn.Score(paramValues, classesDict[item.groupName]);
if ((scoreValue * 100) > item.groupScore)
{
resultsToWrite += "Obiekt" + objNumber + " - " + item.groupName + ",";
objNumber++;
foreach (var para in paramValues)
{
resultsToWrite += para.ToString() + ",";
}
resultsToWrite = resultsToWrite.TrimEnd(',');
resultsToWrite += Environment.NewLine;
testInputsList.Add(paramValues); //////add to test inputs
testOutputsList.Add(classesDict[item.groupName]); ///////and outputs
break;
}
}
counter = 0;
}
}
var knntest = new KNearestNeighbors(k: 4);
knntest.Learn(testInputsList.ToArray(), testOutputsList.ToArray());
var cmtest = GeneralConfusionMatrix.Estimate(knntest, testInputsList.ToArray(), testOutputsList.ToArray());
// We can use it to estimate measures such as
double errortest = cmtest.Error; // should be
double acctest = cmtest.Accuracy; // should be
double kappatest = cmtest.Kappa; // should be
int percent70 = (int)(outputs.Length * 0.7);
int percent30 = outputs.Length - percent70;
int[] randompicks70 = new int[percent70];
int[] randompicks30 = new int[percent30];
int random;
for (int i = 0; i < percent70; i++)
{
do
{
random = rnd.Next(outputs.Length);
} while (randompicks70.Contains(random));
randompicks70[i] = random;
}
int random30counter = 0;
for (int i = 0; i < outputs.Length; i++)
{
if (!randompicks70.Contains(i))
{
randompicks30[random30counter] = i;
random30counter++;
}
}
int[] outputs70 = new int[percent70];
int[] outputs30 = new int[percent30];
double[][] inputs70 = new double[percent70][];
double[][] inputs30 = new double[percent30][];
for (int i = 0; i < percent70; i++)
{
inputs70[i] = inputs[randompicks70[i]];
outputs70[i] = outputs[randompicks70[i]];
}
for (int i = 0; i < percent30; i++)
{
inputs30[i] = inputs[randompicks30[i]];
outputs30[i] = outputs[randompicks30[i]];
}
var knn70percent = new KNearestNeighbors(k: 4);
knn70percent.Learn(inputs70, outputs70);
var cm70percent = GeneralConfusionMatrix.Estimate(knn70percent, inputs70, outputs70);
// We can use it to estimate measures such as
double error70percent = cm70percent.Error; // should be
double acc70percent = cm70percent.Accuracy; // should be
double kappa70percent = cm70percent.Kappa; // should be
double score70 = 0;
double scoretest = 0;
for (int i = 0; i < inputs30.Length; i++)
{
var testvalue1 = knn70percent.Score(inputs30[i], outputs30[i]);
var testvalue2 = knntest.Score(inputs30[i], outputs30[i]);
score70 += testvalue1;
scoretest += testvalue2;
}
score70 = score70 / inputs30.Length;
scoretest = scoretest / inputs30.Length;
ReadFromFileValidation validateWindow = new ReadFromFileValidation(cm, cmtest, cm70percent, score70, scoretest);
validateWindow.Show();
try
{
string path = dialog.SelectedPath + "\\\\" + "ExtendedExamples.txt";
System.IO.File.WriteAllText(path, resultsToWrite);
}
catch (Exception)
{
MessageBox.Show("Coś Poszło nie tak", "Wynik Generacji", MessageBoxButton.OK, MessageBoxImage.Warning);
throw;
}
MessageBox.Show("Wygenerowano Plik", "Wynik Generacji", MessageBoxButton.OK);
}
}
}