static void ReduceVectors(CudaDataSet <int> teaching, CudaDataSet <int> test)
{
using (CudaContext context = new CudaContext())
{
int popSize = 100;
DeviceDataSet <int> deviceTeaching = new DeviceDataSet <int>(teaching);
DeviceDataSet <int> deviceTest = new DeviceDataSet <int>(test);
FlattArray <byte> initialPopulation;
VectorReductionAccuracy acc = new VectorReductionAccuracy(context, deviceTeaching, deviceTest, popSize)
{
K = 5,
CountToPass = 3
};
VectorReductionFitness fitnessFunc =
new VectorReductionFitness(context, acc, popSize, deviceTeaching.length)
{
Alpha = 0.7f
};
//Drop3 drop = new Drop3();
//drop.CasheSize = 5;
//drop.K = 3;
//Profiler.Start("Drop3");
//var indexesToStay = drop.Apply(teaching, context);
//Profiler.Stop("Drop3");
//byte[] parrent = new byte[teaching.Vectors.GetLength(0)];
//foreach (var item in indexesToStay)
//{
// parrent[item] = 1;
//}
initialPopulation = CreateRandomPopulation(popSize, deviceTeaching.length);
//CreatePopulationBasedOnParent(parrent, popSize, 0.2f, 0.05f);
var d = new Evolutionary2(context, fitnessFunc, initialPopulation)
{
Elitism = 0.001f,
MutationRate = 0.001f
};
for (int i = 0; i < 30; i++)
{
Profiler.Start("iteration");
d.CreateNewPopulation();
Profiler.Stop("iteration");
}
var best = d.FindFitest();
Console.WriteLine(acc.GenAccuracy(best.index) / (float)deviceTest.length);
Console.WriteLine(fitnessFunc.GenLength(best.index));
Profiler.Print();
}
}
static void ReduceDimension(CudaDataSet <int> teaching, CudaDataSet <int> test)
{
using (CudaContext context = new CudaContext())
{
int popSize = 20;
int genLength = teaching.Vectors.GetLength(1);
DeviceDataSet <int> deviceTeaching = new DeviceDataSet <int>(teaching);
DeviceDataSet <int> deviceTest = new DeviceDataSet <int>(test);
FlattArray <byte> initialPopulation;
DimensionReductionAccuracy accuracy = new DimensionReductionAccuracy(context, deviceTeaching, deviceTest, popSize)
{
K = 3,
CountToPass = 2
};
DimensionReductionFitness fitnessFunc =
new DimensionReductionFitness(context, accuracy, popSize, genLength)
{
Alpha = 1f
};
Console.WriteLine(accuracy.BaseAccuracy());
Console.WriteLine();
initialPopulation = CreateRandomPopulation(popSize, deviceTeaching.attributeCount);
var d = new Evolutionary2(context, fitnessFunc, initialPopulation)
{
CrossOverRate = 0.5f,
MutationRate = 0.02f,
Elitism = 0.2f
};
for (int i = 0; i < 200; i++)
{
Profiler.Start("iteration");
d.CreateNewPopulation();
Profiler.Stop("iteration");
}
var best = d.FindFitest();
// Console.WriteLine(best.fitness);
var acc = accuracy.GenAccuracy(best.index);
var len = fitnessFunc.GenLength(best.index);
var gen = d.genGen(best.index);
foreach (var item in gen)
{
Console.Write(item + " ");
}
Console.WriteLine($"accuracy: {acc / (float)deviceTest.length} length: {len / (float)genLength}");
Console.WriteLine();
// Profiler.Print();
}
}
static void ReduceVectorsRegresion(CudaDataSet <float> teaching, CudaDataSet <float> test)
{
using (CudaContext context = new CudaContext())
{
int popSize = 100;
var deviceTeaching = new DeviceDataSet <float>(teaching);
var deviceTest = new DeviceDataSet <float>(test);
FlattArray <byte> initialPopulation;
var acc = new VectorReductionAccuracyRegresion(context, deviceTeaching, deviceTest, popSize)
{
K = 3,
CountToPass = 2
};
VectorReductionFitness fitnessFunc =
new VectorReductionFitness(context, acc, popSize, deviceTeaching.length)
{
Alpha = 0.7f
};
initialPopulation = CreateRandomPopulation(popSize, deviceTeaching.length);
var d = new Evolutionary2(context, fitnessFunc, initialPopulation)
{
Elitism = 0.1f,
MutationRate = 0.05f
};
for (int i = 0; i < 10; i++)
{
Profiler.Start("iteration");
d.CreateNewPopulation();
Profiler.Stop("iteration");
}
Console.WriteLine(acc.BaseAccuracy());
var best = d.FindFitest();
Console.WriteLine(acc.GenAccuracy(best.index));
Console.WriteLine(fitnessFunc.GenLength(best.index) / (float)deviceTeaching.length);
Profiler.Print();
}
}