private void CancelRentButton_Click(object sender, RoutedEventArgs e)
{
if (DataGridBox.SelectedItem != null)
{
Warning warn = new Warning("Are you sure?");
warn.ShowDialog();
if (isRemoved)
{
WSCalendar temp = (WSCalendar)DataGridBox.SelectedItem;
calendarService.deleteRent(temp.id);
}
}
DataGridBox.ClearValue(ItemsControl.ItemsSourceProperty);
DataGridBox.Items.Clear();
if (RenterToggle.IsChecked == true)
{
RenterCombobox.IsEnabled = true;
loadLandlordColumns();
}
else
{
RenterCombobox.IsEnabled = false;
loadTenantColumns();
}
}
private static void sparseMachine(Sparse <double>[] inputs, double[] doubleOutputs)
{
// The dataset has output labels as 4 and 2. We have to convert them
// into negative and positive labels so they can be properly processed.
//
bool[] outputs = doubleOutputs.Apply(x => x == 2.0 ? false : true);
// Create a learning algorithm for Sparse data. The first generic argument
// of the learning algorithm below is the chosen kernel function, and the
// second is the type of inputs the machine should accept. Note that, using
// those interfaces, it is possible to define custom kernel functions that
// operate directly on double[], string[], graphs, trees or any object:
var teacher = new LinearDualCoordinateDescent <Linear, Sparse <double> >()
{
Loss = Loss.L2,
Complexity = 1000, // Create a hard-margin SVM
Tolerance = 1e-5
};
// Use the learning algorithm to Learn
var svm = teacher.Learn(inputs, outputs);
// Compute the machine's answers
bool[] answers = svm.Decide(inputs);
// Create a confusion matrix to show the machine's performance
var m = new ConfusionMatrix(predicted: answers, expected: outputs);
// Show it onscreen
DataGridBox.Show(new ConfusionMatrixView(m)).Hold();
}
private void ItemHome_Selected(object sender, RoutedEventArgs e)
{
DataGridBox.ClearValue(ItemsControl.ItemsSourceProperty);
DataGridBox.Items.Clear();
StartDateDP.SelectedDate = null;
EndDateDP.SelectedDate = null;
KitchenCB.IsChecked = false;
AnimalsCB.IsChecked = false;
ParkingCB.IsChecked = false;
CityTB.Text = "";
BedCountTB.Text = "";
PriceTB.Text = "";
}
private void RenterToggle_Click(object sender, RoutedEventArgs e)
{
DataGridBox.ClearValue(ItemsControl.ItemsSourceProperty);
DataGridBox.Items.Clear();
if (RenterToggle.IsChecked == true)
{
RenterCombobox.IsEnabled = true;
loadLandlordColumns();
}
else
{
RenterCombobox.IsEnabled = false;
loadTenantColumns();
}
}
private static void sparseMachineProbabilistic(Sparse <double>[] inputs, double[] doubleOutputs)
{
// The dataset has output labels as 4 and 2. We have to convert them
// into negative and positive labels so they can be properly processed.
//
bool[] outputs = doubleOutputs.Apply(x => x == 2.0 ? false : true);
// Create a learning algorithm for Sparse data. The first generic argument
// of the learning algorithm below is the chosen kernel function, and the
// second is the type of inputs the machine should accept. Note that, using
// those interfaces, it is possible to define custom kernel functions that
// operate directly on double[], string[], graphs, trees or any object:
var teacher = new LinearDualCoordinateDescent <Linear, Sparse <double> >()
{
Loss = Loss.L2,
Complexity = 1000, // Create a hard-margin SVM
Tolerance = 1e-5
};
// Use the learning algorithm to Learn
var svm = teacher.Learn(inputs, outputs);
// Create a probabilistic calibration algorithm based on Platt's method:
var calibration = new ProbabilisticOutputCalibration <Linear, Sparse <double> >()
{
Model = svm
};
// Let's say that instead of having our data as bool[], we would
// have received it as double[] containing the actual probabilities
// associated with each sample:
doubleOutputs.Apply(x => x == 2.0 ? 0.05 : 0.87, result: doubleOutputs);
// Calibrate the SVM using Platt's method
svm = calibration.Learn(inputs, doubleOutputs);
// Compute the machine's answers
bool[] answers = svm.Decide(inputs);
// Compute the machine's probabilities
double[] prob = svm.Probability(inputs);
// Create a confusion matrix to show the machine's performance
var m = new ConfusionMatrix(predicted: answers, expected: outputs);
// Show it onscreen
DataGridBox.Show(new ConfusionMatrixView(m)).Hold();
}
private void SearchButton_Click(object sender, RoutedEventArgs e)
{
DataGridBox.ClearValue(ItemsControl.ItemsSourceProperty);
DataGridBox.Items.Clear();
DataLayer.Classes.FlatSearchCriteria criteria = new DataLayer.Classes.FlatSearchCriteria();
criteria.startDate = StartDateDP.SelectedDate.GetValueOrDefault();
criteria.endDate = EndDateDP.SelectedDate.GetValueOrDefault();
if (!BedCountTB.Text.Equals(""))
{
int parsed;
if (int.TryParse(BedCountTB.Text, out parsed))
{
criteria.bedCount = parsed;
}
}
if (!PriceTB.Text.Equals(""))
{
int parsed;
if (int.TryParse(PriceTB.Text, out parsed))
{
criteria.price = parsed;
if (PriceCB.SelectedIndex == 0)
{
criteria.over = true;
}
else
{
criteria.over = false;
}
}
}
criteria.city = CityTB.Text;
criteria.animals = AnimalsCB.IsChecked.GetValueOrDefault();
criteria.kitchen = KitchenCB.IsChecked.GetValueOrDefault();
criteria.parking = ParkingCB.IsChecked.GetValueOrDefault();
rents = flatsService.getAvailableRooms(criteria);
LoadGrid(rents);
}
private static void cancer()
{
// Create a new LibSVM sparse format data reader
// to read the Wisconsin's Breast Cancer dataset
//
var reader = new SparseReader("examples-sparse.txt");
int[] outputs; // Read the classification problem into dense memory
double[][] inputs = reader.ReadToEnd(sparse: false, labels: out outputs);
// The dataset has output labels as 4 and 2. We have to convert them
// into negative and positive labels so they can be properly processed.
//
outputs = outputs.Apply(x => x == 2 ? -1 : +1);
// Create a new linear-SVM for the problem dimensions
var svm = new SupportVectorMachine(inputs: reader.Dimensions);
// Create a learning algorithm for the problem's dimensions
var teacher = new LinearDualCoordinateDescent(svm, inputs, outputs)
{
Loss = Loss.L2,
Complexity = 1000,
Tolerance = 1e-5
};
// Learn the classification
double error = teacher.Run();
// Compute the machine's answers for the learned inputs
int[] answers = inputs.Apply(x => Math.Sign(svm.Compute(x)));
// Create a confusion matrix to show the machine's performance
var m = new ConfusionMatrix(predicted: answers, expected: outputs);
// Show it onscreen
DataGridBox.Show(new ConfusionMatrixView(m));
}
public void ComputeTest1()
{
double[,] data = Matrix.Identity(5);
DataGridBox.Show(data).Hold();
}
