public static AutoStatePredictor loadFromFile(string file)
{
byte[] saveArray = System.IO.File.ReadAllBytes(file);
AutoStatePredictor load = null;
//version check
if (saveArray[0] == 0)
{
int symbolSize = BitConverter.ToInt32(saveArray, 1);
int stateSize = BitConverter.ToInt32(saveArray, 5);
load = new AutoStatePredictor(symbolSize, stateSize);
load.trainingDepth = BitConverter.ToInt32(saveArray, 9);
for (int i = 0; i < load.table.Length; i++)
{
load.table[i] = saveArray[13 + i];
}
}
else
{
throw new Exception("Invalid file version.");
}
return(load);
}
public static void leapOverride(AutoStatePredictor destination, AutoStatePredictor original, Random rdm, double intensity)
{
destination.reset();
//Copy original to destination
original.table.CopyTo(destination.table, 0);
destination.trainingDepth = original.trainingDepth + 1;
//Choose the number of changes to make to the original based on intensity
//not very optimal
int changeCount = rdm.Next(2 + (int)(original.table.Length * intensity));
destination.lastLeapCount = changeCount;
//Make changes
int index;
for (int i = 0; i < changeCount; i++)
{
//Choose random table index for change
index = rdm.Next(destination.table.Length);
//Check if index is prediction image or state image
destination.table[index] = (byte)rdm.Next(destination.symbolSize); //New random prediction at index
}
destination.computeStateTransitionsTable();
}
public void train(SymbolCorpus trainSet, int cycles)
{
Random rdm = new Random();
AutoStatePredictor predictor = new AutoStatePredictor(symbolSize, stateSize);
//Make copy of current
copyInto(this, predictor);
if (predictor.trainingDepth == 0)
{
randomOverride(predictor, rdm);
//Read and predict analysis
predictor.testPredict(trainSet);
//TODO Generate some more randoms and choose best - might not be needed since leap Intensity is so high at the beginning
}
//Initialize training variables
AutoStatePredictor testTable = new AutoStatePredictor(predictor.symbolSize, predictor.stateSize);
AutoStatePredictor temp;
double leapIntensity = predictor.lastLeapIntensity * 2;
if (leapIntensity > 0.9)
{
leapIntensity = 0.9;
}
for (int i = 0; i < cycles; i++)
{
//Mutation
leapOverride(testTable, predictor, rdm, leapIntensity);
//Read and predict analysis
testTable.testPredict(trainSet);
if (testTable.accuracy > predictor.accuracy)
{
//Best predictor found
//Soft change with memory for sucessful mutations intensity (x2 because of random average behaviour)
leapIntensity = 0.8 * leapIntensity + 0.2 * (2 * testTable.lastLeapIntensity);
if (leapIntensity > 0.9)
{
leapIntensity = 0.9; //maximum allowed mutation intensity
}
//Swap predictors for memory usage
temp = predictor;
predictor = testTable;
testTable = temp;
}
}
copyInto(predictor, this);
}
public static void randomOverride(AutoStatePredictor destination, Random rdm)
{
destination.reset();
destination.trainingDepth = 0;
for (int i = 0; i < destination.table.Length; i++)
{
destination.table[i] = (byte)rdm.Next(destination.symbolSize); //New random prediction at index
}
destination.computeStateTransitionsTable();
}
public static void copyInto(AutoStatePredictor original, AutoStatePredictor destination)
{
original.table.CopyTo(destination.table, 0);
destination.errorCount = original.errorCount;
destination.lastLeapCount = original.lastLeapCount;
destination.predictionCount = original.predictionCount;
destination.state = original.state;
destination.stateSize = original.stateSize;
destination.symbolSize = original.symbolSize;
destination.tableStride = original.tableStride;
destination.trainingDepth = original.trainingDepth;
original.stateMetrics.CopyTo(destination.stateMetrics, 0);
destination.stateStride = original.stateStride;
original.stateTransitionTable.CopyTo(destination.stateTransitionTable, 0);
destination.lossCount = original.lossCount;
original.lossTransitionTable.CopyTo(destination.lossTransitionTable, 0);
}
