public void Test_Manhatten_Distance_Metric()
{
//arrange
int[] pixels1 = { -5, -6, -7 };
int[] pixels2 = { -1, 8, 7 };
ManhattanDistance md = new ManhattanDistance();
//act
var result = md.Between(pixels1, pixels2);
//assert
Assert.AreEqual(result, 32);
}
static void Main(string[] args)
{
var distance = new ManhattanDistance();
var classifier = new BasicClassifier(distance);
var trainingPath = Path.Combine(Directory.GetParent(Directory.GetCurrentDirectory() + "../../..").ToString(), "DigitsRecognizerTests", "datasamples", "digits", "trainingsample.csv");
var training = DataReader.ReadObservations(trainingPath);
classifier.Train(training);
var validationPath = Path.Combine(Directory.GetParent(Directory.GetCurrentDirectory() + "../../..").ToString(), "DigitsRecognizerTests", "datasamples", "digits", "validationsample.csv");
var validation = DataReader.ReadObservations(validationPath);
var correct = Evaluator.Correct(validation, classifier);
Console.WriteLine("Correctly classified: {0:P2}", correct);
Console.ReadLine();
}
public void Test_model_Evaluator_scoring()
{
// arrange
var distance = new ManhattanDistance();
var classifier = new BasicClassifier(distance);
string trainingPath = Path.Combine(TestContext.DeploymentDirectory, "digits", "trainingsample.csv");
var trainingData = DataReader.ReadObservations(trainingPath);
classifier.Train(trainingData);
// act
var result = Evaluator.Score(trainingData[0], classifier);
// assert
Assert.AreEqual(result,1.0);
}
public void Test_Base_Classifier_training()
{
//arrange
var distance = new ManhattanDistance();
var classifier = new BasicClassifier(distance);
string trainingPath = Path.Combine(TestContext.DeploymentDirectory, "digits", "trainingsample.csv");
var trainingData = DataReader.ReadObservations(trainingPath);
classifier.Train(trainingData);
//act
string labelResult = classifier.Predict(trainingData[0].Pixels);
//assert
Assert.IsFalse(string.IsNullOrEmpty(labelResult));
}
static void Main(string[] args)
{
var distance = new ManhattanDistance();
var classifier = new BasicClassifier(distance);
var dataPath = System.IO.Path.GetDirectoryName(System.Reflection.Assembly.GetExecutingAssembly().Location) + @"\train.csv";
Observation[] trainingdata = DataReader.ReadObservations(dataPath);
//List<int> Selectionlist = new List<int>();
//for (int i = 0; i < data.Length; i++)
//{
// Selectionlist.Add(i);
//}
//Observation[] trainingData = new Observation[(int)(data.Length * .8)];
//for (int i = 0; i < trainingData.Length; i++)
//{
// Random r = new Random();
// int rInt = r.Next(0, Selectionlist.Count);
// trainingData[i] = data[rInt];
// Selectionlist.RemoveAt(rInt);
//}
classifier.Train(trainingdata);
var testdataPath = Path.GetDirectoryName(System.Reflection.Assembly.GetExecutingAssembly().Location) + @"\test.csv";
Observation[] testdata = DataReader.ReadObservations(testdataPath);
Evaluator evaluator = new Evaluator();
evaluator.Predict(testdata, classifier);
StringBuilder stringBuilder = new StringBuilder();
for (int i = 0; i < testdata.Length; i++)
{
writeProperty(stringBuilder, (i + 1).ToString(), true, false);
writeProperty(stringBuilder, testdata[i].Label, false, true);
}
File.WriteAllText(Path.GetDirectoryName(System.Reflection.Assembly.GetExecutingAssembly().Location) + @"\sample_submission.csv", stringBuilder.ToString());
Console.ReadLine();
}
static void Main(string[] args)
{
var distance = new ManhattanDistance();
var classifier = new BasicClassifier(distance);
var trainingPath = @"C:\Vikas\Knowledge\MachineLearning\DigitsRecognizer\trainingsample.csv";
var training = DataReader.ReadObservations(trainingPath);
classifier.Train(training);
var validationPath = @"C:\Vikas\Knowledge\MachineLearning\DigitsRecognizer\validationsample.csv";
var validation = DataReader.ReadObservations(validationPath);
var correct = Evaluator.Correct(validation, classifier);
Console.WriteLine("Correctly classified: {0:p2}", correct);
Console.ReadLine();
}