public void Train()
{
log.Info("Test");
var problem = LoadData();
parameters.Weights = WeightCalculation.GetWeights(problem.Y);
model.Train(problem, parameters);
}
public void GetWeights(int[] values, double[] result)
{
var weights = WeightCalculation.GetWeights(values);
for (int i = 0; i < result.Length; i = i + 2)
{
Assert.AreEqual(result[i + 1], Math.Round(weights[(int)result[i]], 2));
}
}
public void PerformCrossValidation(bool scaling, double expected)
{
log.Info("Test");
var problem = LoadData(scaling);
parameters.Weights = WeightCalculation.GetWeights(problem.Y);
double test = model.PerformCrossValidation(problem, parameters, 5);
Assert.AreEqual(expected, Math.Round(test, 2));
}
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));
}
public async Task Cancel()
{
log.Info("Cancel");
var problem = LoadData();
parameters.Weights = WeightCalculation.GetWeights(problem.Y);
CancellationTokenSource source = new CancellationTokenSource();
parameters.Token = source.Token;
var task = Task.Run(() => model.PerformCrossValidation(problem, parameters, 5));
await Task.Delay(100).ConfigureAwait(false);
source.Cancel();
try
{
task.Wait(5000);
}
catch
{
}
Assert.IsTrue(task.IsFaulted);
}
