public Qaib(MainWindow window)
{
//Load test bytes
test = load();
//truncate test bytes
byte[] truncatedTest = new byte[256];
for (int i = 0; i < truncatedTest.Length; i++)
{
truncatedTest[i] = test[i];
}
test = truncatedTest;
test = CharToSymbol(test);
basicPredictTablePrint(test);
//print Test bytes metrics
Console.WriteLine("Test text loaded with: " + test.Length + " Bytes.");
Console.WriteLine("Test text loaded with: " + test.Length / 1024f + " KBytes.");
Console.WriteLine("Test text loaded with: " + (test.Length / 1024f) / 1014f + " MBytes.");
//Set char alphabet as test for translation display
//test = new byte[256];
//for (int i = 0; i < 256; i++)
// test[i] = (byte)i;
bool printTestStrings = false;
if (printTestStrings)
{
string inputText = "";
for (int i = 0; i < test.Length; i++)
{
inputText += (char)test[i];
}
Console.WriteLine("\nTest string:");
Console.WriteLine(inputText);
test = CharToSymbol(test);
string symbolBytes = "";
for (int i = 0; i < test.Length; i++)
{
symbolBytes += ("" + (int)test[i]).PadLeft(4);
}
Console.WriteLine("\nTest symbol bytes:");
Console.WriteLine(symbolBytes);
test = SymbolToChar(test);
string symbolText = "";
for (int i = 0; i < test.Length; i++)
{
symbolText += (char)test[i];
}
Console.WriteLine("\nTest symbol texted:");
Console.WriteLine(symbolText);
}
//test = load();
//test = CharToSymbol(test);
TablePredictor f0 = TablePredictor.LoadFromFile("AAABakef0.tp");
byte[] g0 = f0.predictStates(test);
Console.WriteLine(f0.getStats());
test = g0;
symbolCount = 64;
//more layers
TablePredictor f1 = TablePredictor.LoadFromFile("aaap.tp");
byte[] g1 = f1.predictStates(g0);
Console.WriteLine(f1.getStats());
//TablePredictor f2 = TablePredictor.LoadFromFile("Bakef2.tp");
//byte[] g2 = f2.predictStates(g1);
//Console.WriteLine(f2.getStats());
//TablePredictor f3 = TablePredictor.LoadFromFile("Bakef3.tp");
//byte[] g3 = f3.predictStates(g2);
//Console.WriteLine(f3.getStats());
byte[] display;
//display = f3.project(g3);
//display = f2.project(g2);
display = f1.project(g1);
display = f0.project(display);
display = SymbolToChar(display);
string os = "";
for (int i = 0; i < display.Length; i++)
{
os += (char)display[i];
}
Console.WriteLine("\nTest symbol texted:");
Console.WriteLine(os);
//Setup2();
}
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());
}
