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); }