static void Main(string[] args)
{
InputLoader loader = new InputLoader();
loader.LoadFile("digits.csv");
Stopwatch sw = new Stopwatch();
var heursiticDetection = new HeuristicDetection(10, 5, quantity: 50, numberOfPoints: 500);
var hypothesis = new CurrentHypothesis();
foreach (var input in loader.AllElements())
{
///For every new input we extract n points of interest
///And create a feature vector which characterizes the spatial relationship between these features
///For every heuristic we get a dictionary of points of interest
DetectedPoints v = heursiticDetection.getFeatureVector(input.Item1);
///Compare this feature vector agaist each of the other feature vectors we know about
sw.Reset();
sw.Start();
TestResult r = hypothesis.Predict(v);
Debug.Print("Prediction: " + sw.Elapsed.Milliseconds.ToString());
var best = r.BestResult();
if (best != null && best.Item2 != 0)
{
LogProgress(best.Item1, input.Item2);
}
sw.Reset();
sw.Start();
hypothesis.Train(v, input.Item2, r);
Debug.Print("Training: " + sw.Elapsed.Milliseconds.ToString());
//heursiticDetection.pointsOfInterest.Add(HeuristicDetection.Generate(10, 5, 10));
}
}
public DetectedPoints getFeatureVector(int[][] p)
{
DetectedPoints output = new DetectedPoints();
foreach (var a in pointsOfInterest)
{
output[a.IndexVal] = a.Locate(p);
}
return(output);
}
public TestResult Predict(DetectedPoints pts)
{
TestResult result = new TestResult();
Dictionary <Label, double> results = new Dictionary <Label, double>();
foreach (var a in library)
{
double comparison = a.Value.Compare(pts);
results[a.Key] = comparison;
}
result.Add(results);
return(result);
}
internal void Sanitize(DetectedPoints pts)
{
foreach (var heur in pts.Keys)
{
foreach (var eval in pts[heur].Keys)
{
if (!this[heur].ContainsKey(eval))
{
continue;
}
//We want to compare two geometrical pixel spaces:
if (comparePixelSpaces(pts[heur][eval], this[heur][eval]) > 1000)
{
this[heur].Remove(eval);
}
//int union = pts[heur][eval].Union(this[heur][eval]).Count();
//double spaceSize = (pts[heur][eval].Count() + this[heur][eval].Count() - union) + .0001;
//comparison += ((double)union) / spaceSize;
}
}
}
///We are interested in the spatial relationship between detected points across different
///pieces of input contrlling for their labels
public double Compare(DetectedPoints pts)
{
double comparison = 0;
foreach (var heur in pts.Keys)
{
foreach (var eval in pts[heur].Keys)
{
if (!this[heur].ContainsKey(eval))
{
continue;
}
//We want to compare two geometrical pixel spaces:
comparison += comparePixelSpaces(pts[heur][eval], this[heur][eval]);
//int union = pts[heur][eval].Union(this[heur][eval]).Count();
//double spaceSize = (pts[heur][eval].Count() + this[heur][eval].Count() - union) + .0001;
//comparison += ((double)union) / spaceSize;
}
}
return(comparison);
}
internal void Train(DetectedPoints pts, Label label, TestResult result)
{
if (!library.ContainsKey(label))
{
library[label] = pts;
}
else
{
var baseVal = library[label].Compare(pts);
foreach (var a in library)
{
if (a.Key == label)
{
continue;
}
var a1 = a.Value.Compare(pts);
if (a1 > baseVal)
{
a.Value.Sanitize(pts);
}
}
}
}
