public void TWCNaiveBayesianAlgorithm_PredictTokens_NoPriors()
{
// arrange
var sample = getSample();
var CLS1 = sample.CachedClasses.ElementAt(0);
var CLS2 = sample.CachedClasses.ElementAt(1);
var prep = getDefaultPreprocessor();
var alg = new TWCNaiveBayesianAlgorithm()
{
TFWeightingScheme = Registry.TFWeightingScheme.LogNormalization,
IDFWeightingScheme = Registry.IDFWeightingScheme.Standart,
UsePriors = false, // !!!
Preprocessor = prep
};
// act
alg.Train(sample);
var result1 = alg.PredictTokens(testDocs()[0], 2);
var result2 = alg.PredictTokens(testDocs()[1], 2);
// assert
Assert.AreEqual(2, result1.Length);
Assert.AreEqual(CLS1, result1[0].Class);
Assert.AreEqual(0.2830035D, result1[0].Score, EPS);
Assert.AreEqual(CLS2, result1[1].Class);
Assert.AreEqual(0.2188154D, result1[1].Score, EPS);
Assert.AreEqual(CLS2, result2[0].Class);
Assert.AreEqual(1.0367927D, result2[0].Score, EPS);
Assert.AreEqual(CLS1, result2[1].Class);
Assert.AreEqual(0.9308995D, result2[1].Score, EPS);
}
public void TWCNaiveBayesianAlgorithm_PredictTokens()
{
// arrange
var sample = getSample();
var CLS1 = sample.CachedClasses.ElementAt(0);
var CLS2 = sample.CachedClasses.ElementAt(1);
var prep = getDefaultPreprocessor();
var alg = new TWCNaiveBayesianAlgorithm()
{
TFWeightingScheme = Registry.TFWeightingScheme.LogNormalization,
IDFWeightingScheme = Registry.IDFWeightingScheme.Standart,
UsePriors = true,
Preprocessor = prep
};
// act
alg.Train(sample);
var result1 = alg.PredictTokens(testDocs()[0], 2);
var result2 = alg.PredictTokens(testDocs()[1], 2);
// assert
Assert.AreEqual(2, result1.Length);
Assert.AreEqual(CLS1, result1[0].Class);
Assert.AreEqual(-0.1224616D, result1[0].Score, EPS);
Assert.AreEqual(CLS2, result1[1].Class);
Assert.AreEqual(-0.8797969D, result1[1].Score, EPS);
Assert.AreEqual(CLS1, result2[0].Class);
Assert.AreEqual(0.5254344D, result2[0].Score, EPS);
Assert.AreEqual(CLS2, result2[1].Class);
Assert.AreEqual(-0.0618195D, result2[1].Score, EPS);
}
