public static ScreenData[] GetDataFromFile(string filepath)
{
string[] lines = File.ReadAllLines(filepath);
ScreenData[] gData = new ScreenData[lines.Length];
//for (int i = 0; i < lines.Length; i++)
//{
// string[] sVals = lines[i].Split(';');
// int[] iVals = { Convert.ToInt32(sVals[0]), Convert.ToInt32(sVals[1]), Convert.ToInt32(sVals[2]) };
// gData[i] = new ScreenData { Red = iVals[0], White = iVals[1], Kill = iVals[2] };
//}
return(gData);
}
private void Worker_LearnThresholds_DoWork(object sender, DoWorkEventArgs e)
{
LearningHelper learninghelper = new LearningHelper();
int samplescollected = 0;
while (!Worker_LearnThresholds.CancellationPending)
{
while (GetKillNumber() == 0 && !Worker_LearnThresholds.CancellationPending)
{
ScreenData scrData = GetScreenData();
if (GetKillNumber() == 0)
{
scrData.Kill = false;
learninghelper.AddData(scrData);
samplescollected++;
}
}
List <int> thisKill = new List <int>();
while (GetKillNumber() > 0 && !Worker_LearnThresholds.CancellationPending)
{
ScreenData scrData = GetScreenData();
if (GetKillNumber() > 0)
{
scrData.Kill = true;
learninghelper.AddData(scrData);
samplescollected++;
}
}
}
Worker_LearnThresholds.ReportProgress(0, String.Format("Collected a total of {0} samples", samplescollected));
Worker_LearnThresholds.ReportProgress(0, "Creating model from samples....");
double accuracy = learninghelper.LearnFromData(5000);
Worker_LearnThresholds.ReportProgress(0, String.Format("Model created. Accuracy on 20% of testdata is {0}", accuracy));
learninghelper.SaveModel(@"./learning/KillDetectionModel.mdl");
}
public bool CheckForKill(ScreenData values)
{
model.Predict(values);
return(values.Kill);
}
public void AddData(ScreenData screendata)
{
data.Add(screendata);
}
