public void MC_ExamResult_Test()
{
MultiLogisticClassifier regr = new MultiLogisticClassifier(5, 3);
regr.AddSample(new double[] { 0, 1, 0, 1, 0 }, new double[] { 1, 0, 0 });
regr.AddSample(new double[] { 0, 1, 0, 1, 0 }, new double[] { 1, 0, 0 });
regr.AddSample(new double[] { 0, 1, 0, 1, 0 }, new double[] { 1, 0, 0 });
regr.AddSample(new double[] { 0, 1, 0, 1, 0 }, new double[] { 1, 0, 0 });
regr.AddSample(new double[] { 0, 0, 1, 0, 1 }, new double[] { 0, 1, 0 });
regr.AddSample(new double[] { 0, 1, 0, 1, 0 }, new double[] { 1, 0, 0 });
regr.AddSample(new double[] { 0, 1, 0, 1, 0 }, new double[] { 1, 0, 0 });
regr.AddSample(new double[] { 0, 1, 0, 1, 0 }, new double[] { 1, 0, 0 });
regr.AddSample(new double[] { 0, 1, 0, 1, 0 }, new double[] { 1, 0, 0 });
regr.AddSample(new double[] { 0, 1, 0, 1, 0 }, new double[] { 1, 0, 0 });
regr.AddSample(new double[] { 0, 1, 0, 1, 0 }, new double[] { 1, 0, 0 });
regr.AddSample(new double[] { 0, 1, 0, 1, 0 }, new double[] { 1, 0, 0 });
regr.AddSample(new double[] { 0, 1, 0, 1, 0 }, new double[] { 1, 0, 0 });
regr.AddSample(new double[] { 1, 0, 0, 1, 0 }, new double[] { 1, 0, 0 });
regr.AddSample(new double[] { 1, 0, 0, 1, 0 }, new double[] { 1, 0, 0 });
regr.AddSample(new double[] { 1, 0, 0, 1, 0 }, new double[] { 1, 0, 0 });
regr.AddSample(new double[] { 1, 0, 0, 1, 0 }, new double[] { 0, 0, 1 });
regr.AddSample(new double[] { 1, 0, 0, 0, 0 }, new double[] { 0, 0, 1 });
regr.AddSample(new double[] { 1, 0, 0, 0, 0 }, new double[] { 0, 0, 1 });
regr.AddSample(new double[] { 1, 0, 0, 0, 0 }, new double[] { 0, 0, 1 });
regr.AddSample(new double[] { 1, 0, 0, 0, 0 }, new double[] { 0, 0, 1 });
regr.AddSample(new double[] { 1, 0, 0, 0, 0 }, new double[] { 0, 0, 1 });
regr.AddSample(new double[] { 1, 0, 0, 0, 0 }, new double[] { 0, 1, 0 });
int times = 1000;
regr.Alpha = 3;
regr.Lambda = 0.01;
var results = regr.Train(times);
for (int i = 0; i < times; i++)
{
if (i % 100 != 0)
{
continue;
}
Debug.Print(results[i].Cost.ToString());
for (int t = 0; t < results[i].Theta.GetLength(0); t++)
{
string theta_string = string.Empty;
for (int s = 0; s < results[i].Theta.GetLength(1); s++)
{
theta_string += results[i].Theta[t, s].ToString() + ", ";
}
Debug.Print("\t" + theta_string);
}
}
Assert.IsTrue(MLMath.AreEqual(regr.Predict(new double[] { 0, 1, 0, 1, 0 }), new double[] { 1, 0, 0 }));
Assert.IsTrue(MLMath.AreEqual(regr.Predict(new double[] { 0, 0, 1, 0, 1 }), new double[] { 0, 1, 0 }));
Assert.IsTrue(MLMath.AreEqual(regr.Predict(new double[] { 1, 0, 0, 1, 0 }), new double[] { 1, 0, 0 }));
Assert.IsTrue(MLMath.AreEqual(regr.Predict(new double[] { 1, 0, 0, 0, 0 }), new double[] { 0, 0, 1 }));
}
public override PlayerAction Play(Tank tank)
{
//var theta =
MultiLogisticClassifier mc = new MultiLogisticClassifier(40, 6);
mc.Theta = theta;
var x = FeatureGenerator.CollectFeatures(tank, map, Opponent);
var predict = mc.Predict(x);
int resultIndex = 0;
for (int i = 0; i < predict.Length; i++)
{
if (predict[i] == 1)
{
resultIndex = i;
break;
}
}
switch (resultIndex)
{
case 0:
return(PlayerActionHelper.GetAttackAction(tank));
case 1:
return(PlayerActionHelper.GetForwardAction(tank));
case 2:
return(PlayerActionHelper.GetTurnToAction(tank, Direction.Up));
case 3:
return(PlayerActionHelper.GetTurnToAction(tank, Direction.Right));
case 4:
return(PlayerActionHelper.GetTurnToAction(tank, Direction.Down));
case 5:
return(PlayerActionHelper.GetTurnToAction(tank, Direction.Left));
default:
return(PlayerActionHelper.GetForwardAction(tank));
}
}
public void TrainTest()
{
List <double[]> x = FeatureExtension.LoadMatrix("F:\\x.txt");
List <double[]> y = FeatureExtension.LoadMatrix("F:\\y.txt");
MultiLogisticClassifier regr = new MultiLogisticClassifier(FeatureGenerator.FeatureCount, 6, 3, 0);
int m = 1000; //x.Count();
for (int i = 0; i < m; i++)
{
regr.AddSample(x[i], y[i]);
}
int times = 2000;
var results = regr.Train(times);
for (int i = 0; i < times; i++)
{
if (i % 100 != 0)
{
continue;
}
Debug.Print(results[i].Cost.ToString());
for (int t = 0; t < results[i].Theta.GetLength(0); t++)
{
string theta_string = string.Empty;
for (int s = 0; s < results[i].Theta.GetLength(1); s++)
{
theta_string += results[i].Theta[t, s].ToString() + ", ";
}
Debug.Print("\t" + theta_string);
}
}
List <double[]> x_test = FeatureExtension.LoadMatrix("F:\\x_test.txt");
List <double[]> y_test = FeatureExtension.LoadMatrix("F:\\y_test.txt");
int m_test = 100; x_test.Count();
//List<double[]> x_test = x;
//List<double[]> y_test = y;
//int m_test = 100; //x_test.Count();
int correct_test = 0;
int error_compare = 0;
for (int i = 0; i < m_test; i++)
{
if (error_compare < 20)
{
double[] p1 = regr.Predict(x_test[i]);
if (MLMath.AreEqual(p1, y[i]))
{
correct_test += 1;
}
else
{
double[] p2 = regr.PredictByPercentage(x_test[i]);
Debug.Print(y[i].ToLine());
Debug.Print(p1.ToLine());
Debug.Print(p2.ToLine());
error_compare++;
}
}
}
double precise = correct_test * 1.0 / m_test;
Debug.Print(precise.ToString());
var theta = results.Last().Theta;
FeatureExtension.SaveMatrix(theta, "F:\\theta.txt");
}
