public GridParameterSelection(TaskFactory taskFactory, ITrainingModel training, GridSearchParameters parameters)
{
Guard.NotNull(() => parameters, parameters);
Guard.NotNull(() => taskFactory, taskFactory);
Guard.NotNull(() => training, training);
SearchParameters = parameters;
Training = training;
this.taskFactory = taskFactory;
}
public IParameterSelection Create(TrainingHeader header, IArffDataSet dataset)
{
Guard.NotNull(() => header, header);
Guard.NotNull(() => dataset, dataset);
Parameter defaultParameter = new Parameter();
defaultParameter.KernelType = header.Kernel;
defaultParameter.CacheSize = 200;
defaultParameter.SvmType = header.SvmType;
var model = new TrainingModel(header);
if (!header.GridSelection)
{
return(new NullParameterSelection(defaultParameter, model));
}
GridSearchParameters searchParameters;
logger.Info("Investigate LibLinear");
if (header.Kernel == KernelType.Linear)
{
var gamma = GetList(1, 1, 1);
if (dataset.Header.Total > (dataset.TotalDocuments * 10))
{
logger.Info("Selecting Linear features >> instances");
defaultParameter.Shrinking = false;
}
else
{
logger.Warn("Investigate LibLinear");
}
var training = problemFactory.Construct(dataset).GetProblem();
defaultParameter.Weights = WeightCalculation.GetWeights(training.Y);
foreach (var classItem in defaultParameter.Weights)
{
logger.Info($"Using class [{classItem.Key}] with weight [{classItem.Value}]");
}
searchParameters = new GridSearchParameters(3, GetList(-1, 2, 1), gamma, defaultParameter);
}
else
{
searchParameters = new GridSearchParameters(3, GetList(-5, 15, 2), GetList(-15, 3, 2), defaultParameter);
}
return(new GridParameterSelection(taskFactory, model, searchParameters));
}
