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));
}
}
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 HeuristicDetection(int maxOperations, int maxRadius, int quantity, int numberOfPoints)
{
this.rand = new Random();
this.MaxNumberOfOperations = maxOperations;
this.MaxRadius = maxRadius;
this.Quantity = quantity;
pointsOfInterest = new List <PointsOfInterest>(numberOfPoints);
this.NumberOfPoints = numberOfPoints;
for (int i = 0; i < this.NumberOfPoints; i++)
{
int numberOfOperations = rand.Next(1, this.MaxNumberOfOperations);
int radius = rand.Next(1, this.MaxRadius);
pointsOfInterest.Add(HeuristicDetection.Generate(numberOfOperations, radius, quantity));
}
}
