public int PredictionEnTransformXor(Foreseer foreseer)
{
IntField orig = this.Clone();
foreseer.Initialize(orig);
int wrongPredictions = 0;
int p = 0;
for (int y = 0; y < Height; y++)
{
for (int x = 0; x < Width; x++, p++)
{
int predicted = foreseer.Foresee(orig, x, y);
Data[p] = orig.Data[p] ^ predicted;
if (Data[p] != 0)
{
wrongPredictions++;
}
foreseer.Learn(orig, x, y, orig.Data[p], predicted);
}
}
return(wrongPredictions);
}
public override void Learn(IntField image, int x, int y, int actual, int predicted)
{
_fallback.Learn(image, x, y, actual, predicted);
foreach (var area in _curAreas)
{
ColorFrequencies cf;
if (!_history.TryGetValue(area, out cf))
{
_history.Add(area, cf = new ColorFrequencies());
}
cf.Learn(actual);
}
}
public override void Learn(IntField image, int x, int y, int actual, int predicted)
{
if (_curArea == null)
{
_fallback.Learn(image, x, y, actual, predicted);
return;
}
if (!_history.ContainsKey(_curArea))
{
_history.Add(_curArea, new ColorFrequencies());
}
_history[_curArea].Learn(actual);
}
public void PredictionDeTransformXor(Foreseer foreseer)
{
IntField diff = this.Clone();
foreseer.Initialize(this);
int p = 0;
for (int y = 0; y < Height; y++)
{
for (int x = 0; x < Width; x++, p++)
{
int predicted = foreseer.Foresee(this, x, y);
Data[p] = diff.Data[p] ^ predicted;
foreseer.Learn(this, x, y, this.Data[p], predicted);
}
}
}
public void Prediction(Foreseer foreseer)
{
IntField orig = this.Clone();
foreseer.Initialize(orig);
int p = 0;
for (int y = 0; y < Height; y++)
{
for (int x = 0; x < Width; x++, p++)
{
int predicted = foreseer.Foresee(orig, x, y);
Data[p] = predicted;
foreseer.Learn(orig, x, y, orig.Data[p], predicted);
}
}
}
public void PredictionEnTransformDiff(Foreseer foreseer, int modulus)
{
IntField orig = this.Clone();
foreseer.Initialize(orig);
int p = 0;
for (int y = 0; y < Height; y++)
{
for (int x = 0; x < Width; x++, p++)
{
int predicted = foreseer.Foresee(orig, x, y);
Data[p] = orig.Data[p] - predicted;
if (Data[p] < 0)
{
Data[p] += modulus;
}
foreseer.Learn(orig, x, y, orig.Data[p], predicted);
}
}
}
public override void Learn(IntField image, int x, int y, int actual, int predicted)
{
if (_curArea == null)
{
_fallback.Learn(image, x, y, actual, predicted);
return;
}
if (!_history.ContainsKey(_curArea))
{
_history.Add(_curArea, new outcomes());
}
if (actual == _curMostFreqSymbol)
{
_history[_curArea].MostFrequent++;
}
else
{
_history[_curArea].Other++;
}
}