public Classification ICMInfer(ImageData test_input)
{
Classification curr_classification = LogisticInfer(test_input); //Muahaha better initialization. I don't think this is hacky.
//for(int x = 0; x < ImageData.x_sites; x++) for(int y = 0; y < ImageData.y_sites; y++) curr_classification[x,y] = Label.OFF;
bool converged = false;
int loopcount = 0;
while(!converged && loopcount < 300)
{
loopcount++;
int changecount = 0;
converged = true;
for(int x = 0; x < test_input.XSites; x++) for(int y = 0; y < test_input.YSites; y++)
{
Label old = curr_classification[x,y];
//We have prob 1 vs prob 0. Prob n \propto exp(A + sum over neighbors of I calculated at n)
//So we can just calculate A + sum over neighbors of I for each labeling of the site, and
//assign to the site whichever is higher.
var sitefeatures = Transformer.Transform(test_input[x,y]);
if(x == 6 && y == 10)
{
Console.WriteLine("Components of the dot product w*sitefeatures:");
for(int i = 0; i < sitefeatures.Count; i++)
{
Console.WriteLine("{0}th component: {1}", i, W[i]*sitefeatures[i]);
}
}
double on_association = MathWrapper.Log(MathWrapper.Sigma(W.DotProduct(sitefeatures)));
double off_association = MathWrapper.Log(MathWrapper.Sigma(-1 * W.DotProduct(sitefeatures)));
double on_interaction = 0d;
double off_interaction = 0d;
foreach(Tuple<int,int> t in test_input.GetNeighbors(x,y))
{
DenseVector mu;
if(ImageData.IsEarlier(x,y,t.Item1,t.Item2))mu = Crosser.Cross(test_input[x,y],test_input[t.Item1,t.Item2]);
else mu = Crosser.Cross(test_input[t.Item1,t.Item2], test_input[x,y]);
//Console.WriteLine("Magnitude of Interaction: {0}",v.DotProduct(mu));
if(curr_classification[t.Item1,t.Item2] == Label.ON)
{
on_interaction += V.DotProduct(mu);
off_interaction -= V.DotProduct(mu);
}
else
{
on_interaction -= V.DotProduct(mu);
off_interaction += V.DotProduct(mu);
}
}
if(on_association + on_interaction > off_association + off_interaction)
{
/*Console.WriteLine("On Association: {0}",on_association);
Console.WriteLine("Off Association: {0}",off_association);*/
curr_classification[x,y] = Label.ON;
}
else
{
curr_classification[x,y] = Label.OFF;
}
if(curr_classification[x,y] != old)
{
converged = false;
changecount += 1;
}
}
Console.WriteLine("Number of changes in this round of ICM: {0}",changecount);
}
return curr_classification;
}
