static void LoadXOR(out HashSet <TrainingData> data, int length, int nData)
{
data = new HashSet <TrainingData>();
ContinuousUniform rand = new ContinuousUniform(0, 1);
for (var i = 0; i < nData; i++)
{
TrainingData td = new TrainingData(1, 1);
td[0] = new TrainingData.TrainingPair(DenseVector.Create(1, rand.Sample() > 0.5 ? 1 : 0), DenseVector.Create(1, 0.5));
for (var j = 1; j < length; j++)
{
TrainingData.TrainingPair p = new TrainingData.TrainingPair();
p.Data = DenseVector.Create(1, rand.Sample() > 0.5 ? 1 : 0);
if (td[j - 1].Data[0] == p.Data[0])
{
p.Response = DenseVector.Create(1, 0);
}
else
{
p.Response = DenseVector.Create(1, 1);
}
td[j] = p;
}
data.Add(td);
}
}
internal override void Learn(HashSet <TrainingData> trainSet, int batchSize = 1)
{
batchSize = Math.Min(batchSize, trainSet.Count);
Vector <double> output;
double cost = 0;
int nBatch = 0;
foreach (TrainingData trainSeq in trainSet)
{
// Start over for each different training sequence provided.
WipeMemory();
PreviousResponse = null;
for (var i = 0; i < trainSeq.Count; i++)
{
// Process the pair
TrainingData.TrainingPair pair = trainSeq[i];
output = Process(pair.Data);
cost += CostFunc.Of(trainSeq[i].Response, output) / (batchSize * MaxMemory);
// If we have completely overwriten our short term memory, then
// update the weights based on how we performed this time.
if (i > 0 && i % MaxMemory == 0)
{
PropogateError(trainSeq.SubSequence(Math.Min(i - MaxMemory, 0), i), batchSize);
}
// Count batches by number of error propogations
if (i % (batchSize * MaxMemory) == 0)
{
nBatch++;
LastCost = cost;
Hook?.Invoke(nBatch, this);
ApplyError();
cost = 0;
}
// Keep the last... uhh. this is a PIPI (Parallel Implementation Prone to Inconsistency)
// See this.Process
if (ForceOutput)
{
PreviousResponse = pair.Response;
}
else
{
PreviousResponse = output;
}
if (Abort)
{
Abort = false;
return;
}
}
}
}
static void LoadSinSeq(out HashSet <TrainingData> data, double dt, int seqLength, int nData)
{
data = new HashSet <TrainingData>();
ContinuousUniform rand = new ContinuousUniform(0, 2 * Math.PI);
for (var i = 0; i < nData; i++)
{
double theta = rand.Sample();
TrainingData td = new TrainingData(0, 1);
for (var j = 0; j < seqLength; j++)
{
TrainingData.TrainingPair pair = new TrainingData.TrainingPair();
theta += dt;
pair.Response = DenseVector.Create(1, Math.Sin(theta));
td[j] = pair;
}
data.Add(td);
}
}
