public void TestClassify()
{
var wds = new SimpleWordsDataSource();
var classifier = new BayesianClassifier(wds);
var sentence = new[] { "This", "is", "a", "sentence", "about", "java" };
Assert.AreEqual(IClassifierConstants.NEUTRAL_PROBABILITY, classifier.Classify(ICategorizedClassifierConstants.DEFAULT_CATEGORY, sentence), 0d);
wds.SetWordProbability(new WordProbability("This", .5d));
wds.SetWordProbability(new WordProbability("is", .5d));
wds.SetWordProbability(new WordProbability("a", .5d));
wds.SetWordProbability(new WordProbability("sentence", .2d));
wds.SetWordProbability(new WordProbability("about", .5d));
wds.SetWordProbability(new WordProbability("java", .99d));
Assert.AreEqual(.96d, classifier.Classify(ICategorizedClassifierConstants.DEFAULT_CATEGORY, sentence), .009d);
}
public void TestCalculateOverallProbability()
{
var prob = 0.3d;
var wp1 = new WordProbability("myWord1", prob);
var wp2 = new WordProbability("myWord2", prob);
var wp3 = new WordProbability("myWord3", prob);
var wps = new[] { wp1, wp2, wp3 };
var errorMargin = 0.0001d;
var xy = (prob * prob * prob);
var z = (1-prob)*(1-prob)*(1-prob);
var result = xy/(xy + z);
var classifier = new BayesianClassifier();
Assert.AreEqual(result, classifier.CalculateOverallProbability(wps), errorMargin);
}