public void NaiveBayesIrisTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var irisis = dataSetLoader.SelectIrises();
for (double i = 0; i < 1; i = i + 1)
{
NaiveBayesClassifier naiveBayes =
new NaiveBayesClassifier(irisis, new LinearBayesKernel(irisis));
var irisesTests = dataSetLoader.SelectIrises();
var trueCounter = 0;
var counter = 0;
foreach (var item in irisesTests)
{
var outputValue = naiveBayes.Classify(item.Item1);
if (outputValue == item.Item2)
{
trueCounter++;
}
Debug.WriteLine(string.Format("Value {0} - Predicted {1} = {2}",
item.Item2, outputValue, (outputValue == item.Item2) ? "true" : "false"));
counter++;
}
Debug.WriteLine(string.Format(" i = {0} Data {1} - True {2} Verhältnis: {3}", i,
counter.ToString(), trueCounter.ToString(), (Convert.ToDouble(trueCounter) / Convert.ToDouble(counter)).ToString()));
}
}
public void CreditDataNaiveBayesTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var creditData = dataSetLoader.SelectCreditData();
NaiveBayesClassifier naiveBayes =
new NaiveBayesClassifier(creditData);
var creditDataTest = dataSetLoader.SelectCreditData();
var trueCounter = 0;
var counter = 0;
foreach (var item in creditDataTest)
{
var outputValue = naiveBayes.Classify(item.Item1);
if (outputValue == item.Item2)
{
trueCounter++;
}
Debug.WriteLine(string.Format("Value {0} - Predicted {1} = {2}",
item.Item2, outputValue, (outputValue == item.Item2) ? "true" : "false"));
counter++;
}
Debug.WriteLine(string.Format("Data {0} - True {1} Verhältnis: {2}",
counter.ToString(), trueCounter.ToString(), (Convert.ToDouble(trueCounter) / Convert.ToDouble(counter)).ToString()));
}
public IEnumerable <Tuple <LanguageInfo, double> > Identify(string text)
{
var extractor = new CharacterNGramExtractor(MaxNGramLength, OnlyReadFirstNLines);
var tokens = extractor.GetFeatures(text);
var likelyLanguages = _classifier.Classify(tokens);
return(likelyLanguages);
}
public void ClassifyTest()
{
var classifier = new NaiveBayesClassifier();
var trainingData = new List <Tuple <string, IEnumerable <string> > >
{
new Tuple <string, IEnumerable <string> >("Food", new[] { "apple", "orange" }),
new Tuple <string, IEnumerable <string> >("Food", new[] { "apple", "cake", "banana" }),
new Tuple <string, IEnumerable <string> >("Animal", new[] { "cat", "dog" }),
new Tuple <string, IEnumerable <string> >("Animal", new[] { "bird", "cat", "apple" }),
};
classifier.Train(trainingData);
var category = classifier.Classify(new[] { "dog" });
Assert.AreEqual("Animal", category);
category = classifier.Classify(new[] { "apple", "banana" });
Assert.AreEqual("Food", category);
}
public void NaiveBayesGaussianTest()
{
Tuple <double[], double> tuple1 = new Tuple <double[], double>(new double[] { 5.0, 1.0, 1.0 }, 0);
Tuple <double[], double> tuple2 = new Tuple <double[], double>(new double[] { 1.0, 5.0, 1.0 }, 1);
Tuple <double[], double> tuple3 = new Tuple <double[], double>(new double[] { 1.0, 1.0, 5.0 }, 2);
List <Tuple <double[], double> > movies =
new List <Tuple <double[], double> >()
{
tuple1, tuple2, tuple3
};
NaiveBayesClassifier naiveBayes =
new NaiveBayesClassifier(movies, new GaussianBayesKernel(movies));
var result = naiveBayes.Classify(new double[] { 5.0, 1.0, 1.0 });
result = naiveBayes.Classify(new double[] { 1.0, 5.0, 1.0 });
result = naiveBayes.Classify(new double[] { 1.0, 1.0, 5.0 });
result = naiveBayes.Classify(new double[] { 0.5, 9.0, 0.1 });
result = naiveBayes.Classify(new double[] { 0.4, 0.2, 2.1 });
result = naiveBayes.Classify(new double[] { 0.3, 0.3, 2.5 });
}
