//LogisticRegression reg;
public LogClassifier(string fileName, int countLayers, int countEpoch)
{
Codebook = new BagOfWords()
{
MaximumOccurance = 1
};
int samples = 0;
var dictionary = Utilities.ReadHostFile(fileName, ref samples);
Samples = samples;
if (dictionary.Item1.Length != 0 && dictionary.Item2.Length != 0)
{
Codebook.Learn(dictionary.Item1);
double[][] inputs = Codebook.Transform(dictionary.Item1);
int count = inputs.Count();
//var learner = new IterativeReweightedLeastSquares<LogisticRegression>()
//{
// Tolerance = 1e-4, // Let's set some convergence parameters
// Iterations = 10, // maximum number of iterations to perform
// Regularization = 0
//};
//reg = learner.Learn(inputs, outputs2);
double[][] outputs = Utilities.BoolToDouble(dictionary.Item2);
classifier = new SimpleClassifierNN(inputs, outputs, count, countLayers, countEpoch);
var trainingResult = classifier.Train(inputs, outputs);
Error = trainingResult.Item1;
TrainingTime = trainingResult.Item2;
}
}
public LogClassifier(string networkFileName, string dictionaryFileName)
{
Codebook = new BagOfWords()
{
MaximumOccurance = 1
};
int samples = 0;
var dictionary = Utilities.ReadHostFile(dictionaryFileName, ref samples);
if (dictionary.Item1.Length != 0)
{
Codebook.Learn(dictionary.Item1);
}
classifier = new SimpleClassifierNN(networkFileName);
}
