public static double Correct(IEnumerable <Observation> validationSet, BasicClassifier classifier) { return(validationSet .AsParallel() .Select(obs => Score(obs, classifier)) .Average()); }
static void Main() { const string dataPath = @"..\..\..\Data"; var manhattan = new ManhattanDistance(); var trainingPath = $@"{dataPath}\trainingsample.csv"; var training = DataReader.ReadObservations(trainingPath); var classifier = new BasicClassifier(manhattan); classifier.Train(training); var validationPath = $@"{dataPath}\validationsample.csv"; var validation = DataReader.ReadObservations(validationPath); var correct = Evaluator.Correct(validation, classifier); Console.WriteLine("Correctly classified: {0:P2}", correct); // Test functional style int Dist(int[] x, int[] y) => (int)manhattan.Between(x, y); var funcClassifier = new FunctionalExample(Dist); funcClassifier.Train(training); var functionalCorrect = Evaluator.Correct(validation, funcClassifier); Console.WriteLine("Correctly classified (func): {0:P2}", functionalCorrect); Console.ReadLine(); }
static void Main(string[] args) { Console.WriteLine(" Manhattan #"); var start = DateTime.Now; var distance = new ManhattanDistance(); var classifier = new BasicClassifier(distance); var trainingPath = @"..\..\..\Data\trainingsample.csv"; var training = DataReader.ReadObservations(trainingPath); classifier.Train(training); var validationPath = @"..\..\..\Data\validationsample.csv"; var validation = DataReader.ReadObservations(validationPath); var correct = Evaluator.Correct(validation, classifier); var finish = DateTime.Now; var elapsed = finish - start; Console.WriteLine("Correctly classified: {0:P2}", correct); Console.WriteLine($"Elapsed time = {elapsed.Seconds}sec {elapsed.Milliseconds}ms"); Console.ReadLine(); }
public static double Correct( IEnumerable<Observation> validationSet, BasicClassifier classifier) { return validationSet .Select(obs => Score(obs, classifier)) .Average(); }
static void Main(string[] args) { var distance = new ManhattanDistance(); var classifier = new BasicClassifier(distance); var trainingPath = @"..\..\..\Data\trainingsample.csv"; var training = DataReader.ReadObservations(trainingPath); classifier.Train(training); var validationPath = @"..\..\..\Data\validationsample.csv"; var validation = DataReader.ReadObservations(validationPath); var correct = Evaluator.Correct(validation, classifier); Console.WriteLine($"Correctly classified:{correct}"); }
static void Main(string[] args) { var distance = new ManhattanDistance(); var classifier = new BasicClassifier(distance); var trainingPath = @"E:\personal\deepLearning\DigitsRecognizer\data\trainingsample.csv"; var training = DataReader.ReadObservations(trainingPath); classifier.Train(training); var validationPath = @"E:\personal\deepLearning\DigitsRecognizer\data\validationsample.csv"; var validation = DataReader.ReadObservations(validationPath); var correct = Evaluator.Correct(validation, classifier); Console.WriteLine("Correctly classified: {0:P2}", correct); Console.ReadLine(); }
static void Main(string[] args) { var distance = new ManhattanDistance(); var classifier = new BasicClassifier(distance); var trainingPath = @"..\..\..\Data\trainingsample.csv"; var training = DataReader.ReadObservations(trainingPath); classifier.Train(training); var validationPath = @"..\..\..\Data\validationsample.csv"; var validation = DataReader.ReadObservations(validationPath); var correct = Evaluator.Correct(validation, classifier); Console.WriteLine("Correctly classified: {0:P2}", correct); Console.ReadLine(); }
static void Main(string[] args) { var distanceAlgorithm = new ManhattanDistance(); var classifier = new BasicClassifier(distanceAlgorithm); var trainingPath = @"G:\Projects\MachineLearning\DigitRecognizer\CSharp\Data\trainingsample.csv"; var trainingSet = DataReader.ReadObservations(trainingPath); classifier.Train(trainingSet); var validationPath = @"G:\Projects\MachineLearning\DigitRecognizer\CSharp\Data\validationsample.csv"; var validationSet = DataReader.ReadObservations(validationPath); var correctPercentage = Evaluator.Correct(validationSet, classifier); Console.WriteLine("Percentage of correctly classified images: {0:P2}", correctPercentage); Benchmark(() => Evaluator.Correct(validationSet, classifier), 1); Console.ReadLine(); }
static void Main(string[] args) { var distance = new ManhattanDistance(); var classifier = new BasicClassifier(distance); var trainingPath = @"C:\Development\TestApps\MachineLearning\Chapter1\DigitRecognizer\Data\trainingsample.csv"; var training = DataReader.ReadObservations(trainingPath); classifier.Train(training); Console.WriteLine("Total Training Records: {0}", training.Count()); var validationPath = @"C:\Development\TestApps\MachineLearning\Chapter1\DigitRecognizer\Data\validationsample.csv"; var validation = DataReader.ReadObservations(validationPath); Console.WriteLine("Total Validation Records: {0}", validation.Count()); var correct = Evaluator.Correct(validation, classifier); Console.WriteLine("Correctly classified: {0:P2}", correct); Console.ReadLine(); }