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;
}
public TestResult Test()
{
sw.Reset();
TestResult result = new TestResult();
foreach (var a in InternalModels)
{
a.SetContext(Context);
TestResult output = a.Test();
result.Add(output);
}
TestComplete(this, new EventArgs());
this.TimeToCompleteLastTest = sw.Elapsed;
this.ResultSet = true;
this.LastResult = result;
return(result);
}
/// <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;
}
/// <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);
}