public List <Tuple <string, double> > Classify(Sentence sentence, ClassifyOptions options)
{
var encoder = new OneHotEncoder();
encoder.Words = words;
encoder.Encode(sentence);
var results = new List <Tuple <string, double> >();
// calculate prop
labelDist.ForEach(lf =>
{
var prob = nb.CalPosteriorProb(lf.Value, sentence.Vector, lf.Prob, condProbDictionary);
results.Add(new Tuple <string, double>(lf.Value, prob));
});
/*Parallel.ForEach(labelDist, (lf) =>
* {
* nb.Y = lf.Value;
* lf.Prob = nb.PosteriorProb();
* });*/
double total = results.Select(x => x.Item2).Sum();
return(results.Select(x => new Tuple <string, double>(x.Item1, x.Item2 / total)).ToList());
}
public void OneHotTest()
{
var reader = new FasttextDataReader();
var sentences = reader.Read(new ReaderOptions
{
DataDir = Path.Combine(Configuration.GetValue <String>("MachineLearning:dataDir"), "Text Classification", "cooking.stackexchange"),
FileName = "cooking.stackexchange.txt"
});
var tokenizer = new TokenizerFactory(new TokenizationOptions {
}, SupportedLanguage.English);
tokenizer.GetTokenizer <TreebankTokenizer>();
var newSentences = tokenizer.Tokenize(sentences.Select(x => x.Text).ToList());
for (int i = 0; i < newSentences.Count; i++)
{
newSentences[i].Label = sentences[i].Label;
}
sentences = newSentences.ToList();
var encoder = new OneHotEncoder();
encoder.Sentences = sentences;
encoder.EncodeAll();
}
public void Train(List <Sentence> sentences, ClassifyOptions options)
{
var tfidf = new TfidfFeatureExtractor();
tfidf.Dimension = options.Dimension;
tfidf.Sentences = sentences;
tfidf.CalBasedOnCategory();
var encoder = new OneHotEncoder();
encoder.Sentences = sentences;
encoder.Words = tfidf.Keywords();
words = encoder.EncodeAll();
var featureSets = sentences.Select(x => new Tuple <string, double[]>(x.Label, x.Vector)).ToList();
labelDist = featureSets.GroupBy(x => x.Item1)
.Select(x => new Probability
{
Value = x.Key,
Freq = x.Count()
})
.OrderBy(x => x.Value)
.ToList();
nb.LabelDist = labelDist;
nb.FeatureSet = featureSets;
// calculate prior prob
labelDist.ForEach(l => l.Prob = nb.CalPriorProb(l.Value));
// calculate posterior prob
// loop features
var featureCount = nb.FeatureSet[0].Item2.Length;
labelDist.ForEach(label =>
{
for (int x = 0; x < featureCount; x++)
{
for (int v = 0; v < features.Length; v++)
{
string key = $"{label.Value} f{x} {features[v]}";
condProbDictionary[key] = nb.CalCondProb(x, label.Value, features[v]);
}
}
});
}
