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]);
}
}
});
}
public void SVMClassifierTrain(List <Sentence> sentences, ClassifyOptions options, SvmType svm = SvmType.C_SVC, KernelType kernel = KernelType.RBF, bool probability = true, string outputFile = null)
{
var tfidf = new TfIdfFeatureExtractor();
tfidf.Dimension = options.Dimension;
tfidf.Sentences = sentences;
tfidf.CalBasedOnCategory();
featuresInTfIdf = tfidf.Keywords();
// copy test multiclass Model
Problem train = new Problem();
train.X = GetData(sentences, options).ToArray();
train.Y = GetLabels(sentences).ToArray();
train.Count = train.X.Count();
train.MaxIndex = train.X[0].Count();//int.MaxValue;
Parameter param = new Parameter();
transform = RangeTransform.Compute(train);
Problem scaled = transform.Scale(train);
param.Gamma = 1.0 / 3;
param.SvmType = svm;
param.KernelType = kernel;
param.Probability = probability;
int numberOfClasses = train.Y.OrderBy(x => x).Distinct().Count();
if (numberOfClasses == 1)
{
Console.Write("Number of classes must greater than one!");
}
if (svm == SvmType.C_SVC)
{
for (int i = 0; i < numberOfClasses; i++)
{
param.Weights[i] = 1;
}
}
model = Training.Train(scaled, param);
Console.Write("Training finished!");
}
