private void InitializeData()
{
TrainData.Clear();
var outMin = double.MaxValue;
var outMax = double.MinValue;
for (var i = Min; i <= Max; i += Step)
{
var expected = Function(i);
if (expected > outMax)
{
outMax = expected;
}
if (expected < outMin)
{
outMin = expected;
}
var point = new TrainingData {
Input = new Array3D(Normalize(i, Min, Max)), Expected = new Array3D(expected)
};
TrainData.Add(point);
}
foreach (var data in TrainData)
{
data.Expected[0] = Normalize(data.Expected[0], outMin, outMax);
}
TestData.Clear();
for (var i = 0; i < TrainData.Count; i++)
{
var x = Rand.NextDouble() * (Max - Min) + Min;
TestData.Add(new TrainingData {
Input = new Array3D(Normalize(x, Min, Max)), Expected = new Array3D(Normalize(Function(x), outMin, outMax))
});
}
}
public void SuffleData()
{
TestData.Clear();
TrainData.Clear();
foreach (var item in RawData)
{
if (Random.R.NextDouble(0, 1) < TestRatio)
{
TestData.Add(item.Key, item.Value);
}
else
{
TrainData.Add(item.Key, item.Value);
}
}
if (TestData.Count == 0)
{
var item = TrainData.Last();
TestData.Add(item.Key, item.Value);
TrainData.Remove(item.Key);
}
}
private void AddAsTrainData(DataEntry entry)
{
TrainData.Add(entry);
Vocabulary.AddRange(entry.stemmedClaim);
}
