public FeaturesWithLabel FeatureSetsGenerator(Vec sentenceVectors, String label)
{
var labeledFeatureSet = new FeaturesWithLabel();
int j = 1;
foreach (double node in sentenceVectors.VecNodes)
{
Feature feature = new Feature((j++).ToString(), node.ToString());
labeledFeatureSet.Features.Add(feature);
}
labeledFeatureSet.Label = label;
return(labeledFeatureSet);
}
public double[][] Predict(FeaturesWithLabel featureSet, ClassifyOptions options)
{
Problem predict = new Problem();
List <FeaturesWithLabel> featureSets = new List <FeaturesWithLabel>();
featureSets.Add(featureSet);
predict.X = GetData(featureSets).ToArray();
predict.Y = new double[1];
predict.Count = predict.X.Count();
predict.MaxIndex = 300;
RangeTransform transform = options.Transform;
Problem scaled = transform.Scale(predict);
return(Prediction.PredictLabelsProbability(options.Model, scaled));
}
public List <FeaturesWithLabel> FeatureSetsGenerator(List <Vec> sentenceVectors, List <String> labels)
{
var res = new List <FeaturesWithLabel>();
int j;
for (int i = 0; i < labels.Count; i++)
{
string curLabel = labels[i];
Vec curVec = sentenceVectors[i];
var labeledFeatureSet = new FeaturesWithLabel();
j = 1;
foreach (double node in curVec.VecNodes)
{
Feature feature = new Feature((j++).ToString(), node.ToString());
labeledFeatureSet.Features.Add(feature);
}
labeledFeatureSet.Label = curLabel;
res.Add(labeledFeatureSet);
}
return(res);
}