public void Train(int[] y, double[][] x, CancellationToken token)
{
log.LogInformation("Training SVM...");
var gridsearch = new GridSearch <SupportVectorMachine, double[], int>
{
ParameterRanges =
new GridSearchRangeCollection
{
//new GridSearchRange("complexity", new[] { 0.001, 0.01, 0.1, 1, 10, 100, 1000 }),
new GridSearchRange("C", new[] { 0.001, 0.01, 0.1, 1, 10 }),
},
Learner = p => new LinearDualCoordinateDescent
{
//Complexity = p["complexity"],
Loss = Loss.L2,
Kernel = new Linear(p["C"])
},
Loss = (actual, expected, m) => new ZeroOneLoss(expected).Loss(actual)
};
gridsearch.Token = token;
if (ParallelHelper.Options != null)
{
gridsearch.ParallelOptions = ParallelHelper.Options;
}
GridSearchResult <SupportVectorMachine, double[], int> result = gridsearch.Learn(x, y);
Model = result.BestModel;
GridSearchParameterCollection parameters = result.BestParameters;
var error = result.BestModelError;
log.LogInformation("SVM Trained. Threshold: {0} Constant: {1} Error: {2} ...", Model.Threshold, parameters[0].Value, error);
}
public async Task Train(DataSet dataset, CancellationToken token)
{
logger.LogDebug("Train");
IProcessingTextBlock[] data = dataset.Positive.Concat(dataset.Negative).ToArray();
int[] yData = dataset.Positive.Select(item => 1).Concat(dataset.Negative.Select(item => - 1)).ToArray();
double[][] xData = vectorSource.GetVectors(data, NormalizationType.None);
Array[] randomized = GlobalSettings.Random.Shuffle(yData, xData).ToArray();
//standardizer = Standardizer.GetNumericStandardizer(data);
GridSearch <SupportVectorMachine <Linear>, double[], int> gridsearch = new GridSearch <SupportVectorMachine <Linear>, double[], int>
{
ParameterRanges =
new GridSearchRangeCollection
{
new GridSearchRange("complexity", new[] { 0.001, 0.01, 0.1, 1, 10 }),
},
Learner = p => new LinearDualCoordinateDescent {
Complexity = p["complexity"], Loss = Loss.L2
},
Loss = (actual, expected, m) => new ZeroOneLoss(expected).Loss(actual)
};
gridsearch.Token = token;
GridSearchResult <SupportVectorMachine <Linear>, double[], int> result = await Task.Run(() => gridsearch.Learn(randomized[1].Cast <double[]>().ToArray(), randomized[0].Cast <int>().ToArray()), token).ConfigureAwait(false);
Model = result.BestModel;
}
protected void searchButton_Click(object sender, EventArgs e)
{
//Tar bort listor vid sökning
HeaderGridFinished.Visible = false;
HeaderGridOngoing.Visible = false;
gridOngoing.Visible = false;
gridFinished.Visible = false;
GridNotStarted.Visible = false;
HeaderNotStarted.Visible = false;
//Skicka användarens sökord till webb service
string SearchWord = searchText.Text;
GridSearchResult.DataSource = client.GetCasesFromSearchWord(SearchWord).Select(o => new
{
ID = o.Id,
Firstname = o.ApplicantsFirstName,
Lastname = o.ApplicantsSurname,
RefNumber = o.RefNumber,
Country = o.CountryOfApplicants,
Adress = o.ApplicantsAddress,
Description = o.Description
}).ToList();
GridSearchResult.DataBind();
//Om det fanns några sökresultat eller inte för det sökta ordet
if (GridSearchResult.Rows.Count == 0)
{
SearchMessage.InnerText = "Inga sökresultat, försök igen";
SearchMessage.Visible = true;
HeaderGridFinished.Visible = true;
HeaderGridOngoing.Visible = true;
gridOngoing.Visible = true;
gridFinished.Visible = true;
GridNotStarted.Visible = true;
HeaderNotStarted.Visible = true;
}
else
{
HeaderGridSearchResultButton.Visible = true;
SearchMessage.Visible = false;
HeaderGridSearchResult.Visible = true;
GridSearchResult.Visible = true;
}
}
