public static int TrainAndEvaulate(string type, string path)
{
if (string.IsNullOrWhiteSpace(path))
{
return(-1);
}
// 1 in x of these will be picked up (the smaller the number the more of them
var takeOnly = 0;
var rand = new Random();
var actionData = new DataSet()
{
TrainingCountMax = 150000
};
var xyData = new DataSet()
{
TrainingCountMax = 10000
};
var angleData = new DataSet()
{
TrainingCountMax = 150000
};
int trainingCount = 0, testCount = 0;
var lastFile = "";
var duplicates = new HashSet <int>();
foreach (var kvp in AITraining.GetTrainingFiles(path))
{
var file = kvp.Key;
var count = kvp.Value;
lastFile = file;
if (count > 0)
{
// for debugging
if (takeOnly > 0 && --takeOnly == 0)
{
break;
}
// once enough data has been gathered, break
if (actionData.TrainingCountMax + xyData.TrainingCountMax + angleData.TrainingCountMax == 0)
{
break;
}
foreach (var d in AITraining.GetTraingingData(file))
{
if (d.Result)
{
var dm = d.AsModelDataSet();
var hash = dm.ComputeHash();
// void duplicates
if (duplicates.Contains(hash))
{
continue;
}
// sample ~15% for test
if (rand.Next() % 6 == 0)
{
// test data set
testCount++;
if (actionData.TrainingCountMax > 0)
{
actionData.Test.Add(dm);
}
switch ((ActionEnum)d.Action)
{
case ActionEnum.Attack:
if (angleData.TrainingCountMax > 0)
{
angleData.Test.Add(dm);
}
break;
case ActionEnum.Move:
if (xyData.TrainingCountMax > 0)
{
xyData.Test.Add(dm);
}
break;
}
}
else
{
// training data set
trainingCount++;
if (actionData.TrainingCountMax > 0)
{
actionData.Training.Add(dm);
actionData.TrainingCountMax--;
}
switch ((ActionEnum)d.Action)
{
case ActionEnum.Attack:
if (angleData.TrainingCountMax > 0)
{
angleData.Training.Add(dm);
angleData.TrainingCountMax--;
}
break;
case ActionEnum.Move:
if (xyData.TrainingCountMax > 0 && !duplicates.Contains(hash))
{
xyData.Training.Add(dm);
xyData.TrainingCountMax--;
}
break;
}
}
// add as a potential collision
duplicates.Add(hash);
} // is result
} // foreach TrainingData
} // if count > 0
} // foreach file
Console.WriteLine("Last file considered {0}", lastFile);
Console.WriteLine("Training data set ({0} items) and test data set ({1} items)", trainingCount, testCount);
Console.WriteLine(" Training: Action({0}) XY({1}) Angle({2})", actionData.Training.Count, xyData.Training.Count, angleData.Training.Count);
Console.WriteLine(" Test: Action({0}) XY({1}) Angle({2})", actionData.Test.Count, xyData.Test.Count, angleData.Test.Count);
// train
shootMup.Bots.Model actions = null;
if (type.Equals("ml", StringComparison.OrdinalIgnoreCase))
{
actions = new ModelMLNet(actionData.Training, ModelValue.Action);
}
else
{
actions = new ModelOpenCV(actionData.Training, ModelValue.Action);
}
actions.Save(Path.Combine(path, string.Format("action.{0}.model", type)));
// evaluate
var eval = actions.Evaluate(actionData.Test, ModelValue.Action);
Console.WriteLine("Actions RMS={0} R^2={1}", eval.RMS, eval.RSquared);
// train
shootMup.Bots.Model xy = null;
if (type.Equals("ml", StringComparison.OrdinalIgnoreCase))
{
xy = new ModelMLNet(xyData.Training, ModelValue.XY);
}
else
{
xy = new ModelOpenCV(xyData.Training, ModelValue.XY);
}
xy.Save(Path.Combine(path, string.Format("xy.{0}.model", type)));
// evaluate
eval = xy.Evaluate(xyData.Test, ModelValue.XY);
Console.WriteLine("XY RMS={0} R^2={1}", eval.RMS, eval.RSquared);
// train
shootMup.Bots.Model angle = null;
if (type.Equals("ml", StringComparison.OrdinalIgnoreCase))
{
angle = new ModelMLNet(angleData.Training, ModelValue.Angle);
}
else
{
angle = new ModelOpenCV(angleData.Training, ModelValue.Angle);
}
angle.Save(Path.Combine(path, string.Format("angle.{0}.model", type)));
// evaluate
eval = angle.Evaluate(angleData.Test, ModelValue.Angle);
Console.WriteLine("Angle RMS={0} R^2={1}", eval.RMS, eval.RSquared);
return(0);
}
