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 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);
}
}
}
}
/// <summary>
/// At this point we pretend that we don't know the correct output label for the most recent piece of data
/// </summary>
public TestResult Predict()
{
TestResult result = new TestResult();
List<int> indicesToRegenerate = new List<int>();
int i = 0;
foreach (var a in Features) {
double eval = a.Evaluate(featureContext);
var output = a.Apply(eval, utilThreshold);
//if (output.Count() == 0 && a.stats.Count() > 3) {
// indicesToRegenerate.Add(i);
//}
i++;
result.Add(output);
}
for (int j = 0; j < indicesToRegenerate.Count(); j++) {
Features[indicesToRegenerate[j]].Regenerate(30, this.Width, this.Height);
}
return result;
}
internal void Add(TestResult output)
{
throw new NotImplementedException();
}
///Knows how successul we have been
///Probability returned for correct assignment vs. probability
///returned for an incorrect assignment
internal void Push(TestResult result, Label Label)
{
throw new NotImplementedException();
}
