public static int Check(string type, string path)
{
// load each model and then give a few predictions and check the results
var rand = new Random();
var data = new List <TrainingData>();
var done = false;
foreach (var kvp in AITraining.GetTrainingFiles(path))
{
if (done)
{
break;
}
var file = kvp.Key;
var count = kvp.Value;
if (count > 0)
{
foreach (var d in AITraining.GetTraingingData(file))
{
if (done)
{
break;
}
if (d.Result && d.Z == 0)
{
// sample ~15%
if (rand.Next() % 6 == 0)
{
// test data set
data.Add(d);
}
if (data.Count > 1000)
{
done = true;
}
}
}
}
}
foreach (var modelType in new ModelValue[]
{
ModelValue.Action,
ModelValue.XY,
ModelValue.Angle
}
)
{
var map = new Dictionary <string, int>();
var modelPath = "";
if (modelType == ModelValue.Action)
{
modelPath = string.Format("action.{0}.model", type);
}
else if (modelType == ModelValue.XY)
{
modelPath = string.Format("xy.{0}.model", type);
}
else if (modelType == ModelValue.Angle)
{
modelPath = string.Format("angle.{0}.model", type);
}
else
{
throw new Exception("Unknown model type : " + modelType);
}
Model model = null;
if (type.Equals("ml", StringComparison.OrdinalIgnoreCase))
{
model = new ModelMLNet(Path.Combine(path, modelPath));
}
else
{
model = new ModelOpenCV(Path.Combine(path, modelPath));
}
var delta = 0d;
var count = 0;
var timer = new Stopwatch();
timer.Start();
foreach (var d in data)
{
var key = "";
if (false && modelType == ModelValue.XY)
{
float xdelta, ydelta;
model.Predict(d.AsModelDataSet(), out xdelta, out ydelta);
delta += Math.Abs(xdelta - d.Xdelta);
delta += Math.Abs(ydelta - d.Ydelta);
count += 2;
key = string.Format("{0},{1} {2},{3}", Math.Round(d.Xdelta, 1), Math.Round(d.Ydelta, 1), Math.Round(xdelta, 1), Math.Round(ydelta, 1));
}
else
{
var value = model.Predict(d.AsModelDataSet());
if (modelType == ModelValue.Action)
{
delta += Math.Abs(value - (float)d.Action);
key = string.Format("{0},{1}", d.Action, Math.Round(value));
}
else if (modelType == ModelValue.Angle)
{
delta += Math.Abs(value - d.Angle);
key = string.Format("{0},{1}", Math.Round(d.Angle), Math.Round(value));
}
else if (modelType == ModelValue.XY)
{
var moveAngle = Collision.CalculateAngleFromPoint(0, 0, d.Xdelta, d.Ydelta);
delta += Math.Abs(value - moveAngle);
key = string.Format("{0},{1}", Math.Round(moveAngle), Math.Round(value));
}
}
if (!map.ContainsKey(key))
{
map.Add(key, 0);
}
map[key]++;
}
timer.Stop();
Console.WriteLine("{0} has an average delta of {1:f2}. This ran in {2}ms or {3:f2}ms per prediction", modelType, delta / (double)count, timer.ElapsedMilliseconds, (float)timer.ElapsedMilliseconds / (float)data.Count);
foreach (var kvp in map.OrderByDescending(k => k.Value))
{
Console.WriteLine("\t{0}\t{1}", kvp.Key, kvp.Value);
}
}
return(0);
}
public static int Serialize(string path, string type, int remaining)
{
// validate type
var action = ActionEnum.None;
switch (type.ToLower())
{
case "angle": action = ActionEnum.Attack; break;
case "xy": action = ActionEnum.Move; break;
}
if (remaining < 0)
{
remaining = Int32.MaxValue;
}
// load each model and then give a few predictions and check the results
var duplicates = new HashSet <int>();
using (var writer = File.CreateText(Path.Combine(path, "data.csv")))
{
foreach (var kvp in AITraining.GetTrainingFiles(path))
{
var file = kvp.Key;
var count = kvp.Value;
if (remaining <= 0)
{
break;
}
if (count > 0)
{
foreach (var d in AITraining.GetTraingingData(file))
{
if (remaining <= 0)
{
break;
}
if (d.Result && d.Z == 0)
{
var dm = d.AsModelDataSet();
var hash = dm.ComputeHash();
// check type
if (action != ActionEnum.None && action != (ActionEnum)d.Action)
{
continue;
}
// void duplicates
if (duplicates.Contains(hash))
{
continue;
}
// write to disk
for (int i = 0; i < dm.Features(); i++)
{
writer.Write("{0},", dm.Feature(i));
}
var outcome = dm.Action;
switch (action)
{
case ActionEnum.Move: outcome = dm.MoveAngle; break;
case ActionEnum.Attack: outcome = dm.FaceAngle; break;
}
writer.WriteLine("{0}", outcome);
// update
remaining--;
duplicates.Add(hash);
}
}
}
}
} // using writer
return(0);
}
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);
}