bool Reforce(TablePredictor original, TablePredictor factoryTester, Random rdm, int breath)
{
int variationCount = ((int)short.MaxValue) * 2 * 256; //All entries, times 2 (prediction,state) , times 256 possible states
int startVariation = rdm.Next(variationCount);
int count = 0;
for (int i = startVariation; i < variationCount; i = (i + 1) % variationCount)
{
count++;
if (count > breath)
{
return(false);
}
TablePredictor.variationOverride(factoryTester, i);
factoryTester.ResetState();
factoryTester.ResetMetrics();
factoryTester.testPredict(test);
if (original.accuracy < factoryTester.accuracy)
{
TablePredictor.variationOverride(original, i);
original.predictionCount = factoryTester.predictionCount;
original.errorCount = factoryTester.errorCount;
return(true); //return both the original and the factory tester with the same variation
}
TablePredictor.variationUndo(factoryTester, i, original);
}
return(false);
}
void VFLearner()
{
Stopwatch stopwatch = new Stopwatch();
stopwatch.Start();
Random rdm = new Random(0);
TablePredictor testTable = new TablePredictor(symbolCount, population[0].stateSize);
TablePredictor temp;
int printRun = 100000;
double crossRatio = 0.0;
double randomRatio = 0.1;
double mutatedRatio = 1 - (crossRatio + randomRatio);
double mutationIntensity = 0.5;
int runs = int.MaxValue;
double decision;
int parent;
for (int i = 0; i < runs; i++)
{
cycle++;
if (i % printRun == 0)
{
population[0].SaveToFile("Run0-" + (i / printRun) + "v0.tp");
Console.WriteLine("m.i.: " + ("" + mutationIntensity).PadRight(10).Substring(0, 6));
printPopulation(1);
}
decision = rdm.NextDouble();
if (decision < mutatedRatio)
{
//Mutation
parent = rdm.Next(populationCount);
TablePredictor.mutateOverride(testTable, population[parent], rdm, mutationIntensity);
testTable.birthTime = cycle;
}
else if (decision < mutatedRatio + crossRatio)
{
throw new Exception("NO CROSS ALLOWED");
//Cross
int parent1 = rdm.Next(populationCount);
int parent2 = rdm.Next(populationCount);
TablePredictor.crossOverride(testTable, population[parent1], population[parent2], rdm);
testTable.birthTime = cycle;
if (testTable.species == population[parent1].species)
{
parent = parent1;
}
else
{
parent = parent2;
}
}
else
{
TablePredictor.randomOverride(testTable, rdm);
parent = -1;
}
testTable.testPredict(test);
if (testTable.accuracy > population[populationCount - 1].accuracy)
{
if (parent != -1)
{
//Children analysis
if (population[parent].accuracy < testTable.accuracy)
{
//Parent beat
//Soft change for sucessful mutation intensity (x2 because of random average behaviour)
mutationIntensity = 0.8 * mutationIntensity + 0.2 * (2 * testTable.mutationIntensity);
if (mutationIntensity > 0.9)
{
mutationIntensity = 0.9; //maximum allowed mutation intensity
}
temp = population[parent];
population[parent] = testTable;
testTable = temp;
//Reorder
for (int j = parent; j > 0; j--)
{
if (population[j - 1].accuracy < population[j].accuracy)
{
temp = population[j - 1];
population[j - 1] = population[j];
population[j] = temp;
}
}
}
}
else
{
//Random win, order
for (int j = 0; j < populationCount; j++)
{
if (testTable.accuracy > population[j].accuracy)
{
temp = population[j];
population[j] = testTable;
testTable = temp;
}
}
}
}
}
stopwatch.Stop();
long elapsedTime = stopwatch.ElapsedMilliseconds;
Console.WriteLine("Genetic Algorithms runtime at: \nMiliseconds: " + elapsedTime + "\nSeconds: " + elapsedTime / 1000f + "\nMinutes" + (elapsedTime / 1000f) / 60f);
printPopulation(populationCount);
TablePredictor best = population[0];
Console.WriteLine("Rank : 0 " + best.getStats());
best.SaveToFile("Potato.tp");
TablePredictor lel = TablePredictor.LoadFromFile("Potato.tp");
lel.testPredict(test);
Console.WriteLine("Loaded: " + lel.getStats());
}
