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();
}
