public static Data IterateCells(CA ca, Data cellData, List <int> labels, float threshold)
{
var imageData = cellData.GetData();
int y = 0;
foreach (float[] row in imageData)
{
int x = 0;
foreach (float pixel_value in row)
{
int[] fp = new int[2];
fp[0] = x;
fp[1] = y;
Position p = new Position(fp);
CellState s = cellData.GetCellState(p);
ca.ProcessCell(cellData, labels, p, s);
x++;
}
y++;
}
return(cellData);
}
public CellState[][] Train(List <float[]> data, float threshold)
{
Data d = new Data(data);
float maxIntensity = d.GetMaxIntensity();
CA ca = new CA(maxIntensity, threshold);
List <int> labels = d.SeedValues(20, 40);
for (int i = 0; i < 400; i++)
{
int y = 0;
foreach (float[] row in data)
{
int x = 0;
foreach (float pixel_value in row)
{
int[] fp = new int[2];
fp[0] = x;
fp[1] = y;
Position p = new Position(fp);
CellState s = d.GetCellState(p);
ca.ProcessCell(d, labels, p, s);
x++;
}
y++;
}
"Iteration: {0} Num state transitions: {1}".Cout(i, ca.NumStateTransitions);
if (ca.NumStateTransitions == 0)
{
break;
}
ca.NumStateTransitions = 0;
}
return(d.CellStates);
}