/// <summary>
/// Trains the classifier and computes the training error if option provided.
/// </summary>
/// <param name="trainingData">The training data that will be used to train classifier.</param>
/// <param name="trainingLabels">The training labels related to provided training data.</param>
/// <param name="calculateError">The boolean check to tell if the training error should be calculated.</param>
public override void Train(List <double[]> trainingData, List <int> trainingLabels, bool calculateError = true)
{
Model = LearningAlgorithm.Learn(trainingData.ToArray(), trainingLabels.ToArray());
if (calculateError == true)
{
CalculateTrainingError(trainingData, trainingLabels);
}
}
public override void Train(List <double[]> trainingData, List <double> trainingLabels, bool calculateError = true)
{
int[] TrainingLabels = TypeCasters.DoubleArrayToInt(trainingLabels).ToArray();
double[][] TrainingData = trainingData.ToArray();
Model = LearningAlgorithm.Learn(TrainingData, TrainingLabels);
if (calculateError == true)
{
CalculateTrainingError(trainingData, trainingLabels);
}
}
public void Train(List <TrainingValue> trainingData)
{
List <DecisionVariable> trainingVariables = new List <DecisionVariable>();
for (int i = 0; i < featureSize; i++)
{
trainingVariables.Add(DecisionVariable.Continuous(i.ToString()));
}
tree = new DecisionTree(inputs: trainingVariables, classes: 2);
double[][] featuresArray = new double[trainingData.Count][];
int[] labels = new int[trainingData.Count];
for (int i = 0; i < featuresArray.Length; i++)
{
featuresArray[i] = trainingData[i].Features;
labels[i] = Convert.ToInt32(trainingData[i].State);
}
switch (type)
{
case ClassifierType.DecisionTree:
C45Learning teacher = new C45Learning(tree);
teacher.Learn(featuresArray, labels);
break;
case ClassifierType.LDA:
LinearDiscriminantAnalysis lda = new LinearDiscriminantAnalysis();
pipeline = lda.Learn(featuresArray, labels);
break;
case ClassifierType.SVM:
LinearCoordinateDescent svmLearner = new LinearCoordinateDescent();
svm = svmLearner.Learn(featuresArray, labels);
break;
case ClassifierType.Bayes:
NaiveBayesLearning <NormalDistribution> learner = new NaiveBayesLearning <NormalDistribution>();
bayes = learner.Learn(featuresArray, labels);
break;
}
Trained = true;
}
/// <summary>
/// Loads the trained model.
/// </summary>
/// <param name="path">The location from where to load the trained model.</param>
public override void Load(string path)
{
Model = Accord.IO.Serializer.Load <LinearDiscriminantAnalysis.Pipeline>(path);
}
