public static MultinomialLogisticRegression Train(IDataTable table, ILinearAlgebraProvider lap,
int trainingIterations, float trainingRate, float lambda,
Func <float, bool> costCallback = null)
{
var trainingData = table.ConvertToBinaryClassification();
// train the classifiers on each training data set
var classifier = new List <LogisticRegression>();
var label = new List <string>();
foreach (var item in trainingData)
{
classifier.Add(item.Table.TrainLogisticRegression(lap, trainingIterations, trainingRate,
lambda, costCallback));
label.Add(item.Classification);
}
// build the model
return(new MultinomialLogisticRegression
{
Classification = label.ToArray(),
Model = classifier.ToArray(),
FeatureColumn = Enumerable.Range(0, table.ColumnCount).
Where(c => c != table.TargetColumnIndex).ToArray()
});
}