Ejemplo n.º 1
0
 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);
 }
Ejemplo n.º 2
0
        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");
        }
Ejemplo n.º 3
0
 public bool CheckForKill(ScreenData values)
 {
     model.Predict(values);
     return(values.Kill);
 }
Ejemplo n.º 4
0
 public void AddData(ScreenData screendata)
 {
     data.Add(screendata);
 }