private void ComputeFilteredImage()
{
FilteredImage image = (FilteredImage)PreviousLayer.GetData();
FilteredImageChannel[] newChannels = new FilteredImageChannel[image.NumberOfChannels];
int size = image.Size;
for (int i = 0; i < image.NumberOfChannels; i++)
{
double[,] newValues = new double[size, size];
for (int valuesI = 0; valuesI < size; valuesI++)
{
for (int valuesJ = 0; valuesJ < size; valuesJ++)
{
if (GlobalRandom.GetRandomDouble() < Rate)
{
newValues[valuesI, valuesJ] = 0;
}
else
{
newValues[valuesI, valuesJ] = image.Channels[i].Values[valuesI, valuesJ];
}
}
}
newChannels[i] = new FilteredImageChannel(size, newValues);
}
Output = new FilteredImage(image.NumberOfChannels, newChannels);
}
private void InitializeRandom()
{
Weights = new double[NumberOfWeights];
for (int i = 0; i < NumberOfWeights; i++)
{
Weights[i] = GlobalRandom.GetRandomWeight() / NumberOfWeights;
}
Bias = 0;
}
private List <InputOutputPair> Shuffle(List <InputOutputPair> set)
{
for (int i = 0; i < set.Count; i++)
{
int x = GlobalRandom.GetRandomInt(i, set.Count);
InputOutputPair aux = set[x];
set[x] = set[i];
set[i] = aux;
}
return(set);
}
private void InitializeRandom()
{
Values = new double[Size, Size];
int squaredSize = Size * Size;
for (int i = 0; i < Size; i++)
{
for (int j = 0; j < Size; j++)
{
Values[i, j] = GlobalRandom.GetRandomWeight() / squaredSize;
}
}
}
private void ComputeFlattenedImage()
{
FlattenedImage previous = (FlattenedImage)PreviousLayer.GetData();
double[] newValues = new double[previous.Size];
for (int i = 0; i < previous.Size; i++)
{
if (GlobalRandom.GetRandomDouble() < Rate)
{
newValues[i] = 0;
}
else
{
newValues[i] = previous.Values[i];
}
}
Output = new FlattenedImage(previous.Size, newValues);
}
