private void Classify_button_Click(object sender, EventArgs e)
{
dataGridView1.Rows.Clear();
dataGridView1.Refresh();
int l = Math.Min(Convert.ToInt32(imageLeftIndex.Text), Convert.ToInt32(imageRightIndex.Text));
int r = Math.Max(Convert.ToInt32(imageLeftIndex.Text), Convert.ToInt32(imageRightIndex.Text));
l--;
r--;
if (l >= 0 && r < 10000 && Convert.ToInt32(K_text.Text) <= 60000)
{
double accuracy = KNN.Classify(l, r, ReadingInput.testImages, ReadingInput.trainImages, Convert.ToInt32(K_text.Text));
int index = 0;
for (int i = 0; i <= r - l; i++)
{
DataGridViewRow row = (DataGridViewRow)dataGridView1.Rows[0].Clone();
row.Cells[0].Value = KNN.testResults[index];
row.Cells[1].Value = KNN.testResults[index + 1];
row.Cells[2].Value = KNN.testResults[index + 2];
index = index + 3;
dataGridView1.Rows.Add(row);
}
accuracy_text.Text = accuracy.ToString() + "%";
ConfusionMatrix CM = new ConfusionMatrix();
CM.Show();
}
}
private void Classify_button_Click(object sender, EventArgs e)
{
int index = Convert.ToInt32(imageIndex.Text);
index--;
if (index >= 0 && index < 10000 && Convert.ToInt32(K_text.Text) <= 60000)
{
byte pred = KNN.Classify(ReadingInput.testImages[index], ReadingInput.trainImages, Convert.ToInt32(K_text.Text));
predicion_text.Text = pred.ToString();
}
}
private void Classify_button_Click(object sender, EventArgs e)
{
Bitmap drawnImage = new Bitmap(pictureBox1.Image);
Bitmap resized = new Bitmap(drawnImage, new Size(28, 28));
pictureBox1.Image = resized;
byte[,] arr = new byte[28, 28];
for (int i = 0; i < 28; i++)
{
for (int j = 0; j < 28; j++)
{
arr[i, j] = resized.GetPixel(j, i).R;
}
}
DigitImage DI = new DigitImage(arr, 0);
byte pred = KNN.Classify(DI, ReadingInput.trainImages, Convert.ToInt32(K_text.Text));
predicion_text.Text = pred.ToString();
}
