public double GradeResults(ITrainedNeuralNetwork network, List <TrainingData> testData) { int numberCorrect = 0; foreach (TrainingData t in testData) { double[] outputs = network.Execute(t.X); if (EquivalentOutputs(outputs, t.Y)) { numberCorrect++; } } return(numberCorrect / (testData.Count * 1.0)); }
public double GradeResults(ITrainedNeuralNetwork network, List <TrainingData> dataToGrade) { int correct = 0; foreach (TrainingData testDataInstance in dataToGrade) { double[] result = network.Execute(testDataInstance.X); if ((result[0] >= .5 && testDataInstance.Y[0] == 1) || (result[0] < .5 && testDataInstance.Y[0] == 0)) { correct++; } } return(correct / (dataToGrade.Count * 1.0)); }