private async void ButtonSearch_Click(object sender, EventArgs e)
{
bool found = false;
foreach (var item in DataGetter.GetData())
{
if (textBoxSearchCountry.Text == item.Name)
{
textBoxCountryNameResult.Text = item.Name;
textBoxCapital.Text = item.Capital;
textBoxAlpha3Code.Text = item.Alpha3Code;
textBoxArea.Text = item.Area.ToString();
textBoxPopulation.Text = item.Population.ToString();
textBoxRegion.Text = item.Region;
found = true;
break;
}
}
if (found == false)
{
MessageBox.Show("Страна не была найдена");
}
else
if (MessageBox.Show("Сохранить в базу данных информацию?", "Страна найдена", MessageBoxButtons.YesNo) == DialogResult.Yes)
{
await highLvlSQL.SaveDataInDB(textBoxCountryNameResult.Text, textBoxAlpha3Code.Text, textBoxCapital.Text,
textBoxArea.Text, textBoxPopulation.Text, textBoxRegion.Text); //сохранение найденных полей в базу данных
dataGridView1.Rows.Clear();
dataGridView1.Refresh();
FillDataGrid();
}
}
public ScatterExample()
{
InitializeComponent();
DataGetter dg = new DataGetter();
List <double[]> data = (List <double[]>)dg.GetData("group_B.csv", ',');
List <double[]> data1 = (List <double[]>)dg.GetData("group_A.csv", ',');
Values = new ChartValues <ObservablePoint>();
Values1 = new ChartValues <ObservablePoint>();
Perceptron.learn();
for (var i = 0; i < 500; i++)
{
Values.Add(new ObservablePoint(data[i][0], data[i][1]));
Values1.Add(new ObservablePoint(data1[i][0], data1[i][1]));
}
DataContext = this;
}
public int GetAnswer()
{
_slope = DataGetter.GetData();
var currentRow = 0;
var currentCol = 0;
var treesHit = 0;
while (currentRow < _slope.Rows.Count)
{
if (IsATree(currentRow, currentCol))
{
treesHit++;
}
currentCol += 3;
currentRow += 1;
}
return(treesHit);
}
public void DataGettingTest()
{
DataGetter dg = new DataGetter();
var data = dg.GetData("approximation1.txt", ' ');
var one = dg.GetTrainingDataWithOneOutput("approximation1.txt", 1);
var test = dg.GetTrainingDataWithOneOutput("approximation_test.txt", 1);
var two = dg.GetTrainingDataWithChosenInputs("classification.txt", new bool[] { true, true, true, true });
{ Console.WriteLine("test"); }
var distCal = new EuclideanDistance();
RBFNetwork network = new RBFNetwork(distCal,
new GaussianRadialBasis(),
new KNNWidthCalculator(distCal, 2, 1),
new RandomNeuronPositioner(),
2, one[0].DesiredOutput.Count, one[0].Input.Count);
network.Train(new BackpropagationTrainingParameters(0.5, 20, 0, -1, 1, one), test);
var output = network.ProcessInput(test[0].Input);
}