public static async Task RunBenchmark(int deltaN, int max)
{
int currentSize = deltaN;
var encoder = new ConvolutionalEncoder();
var decoder = new ConvolutionalDecoder();
var noisyChannel = new NoisyChannel(0.01);
while (currentSize <= max)
{
var bmp = new Bitmap(currentSize, currentSize);
var bytes = BitmapHelper.GetColorBytes(bmp);
var watch = System.Diagnostics.Stopwatch.StartNew();
var resultWithEncoding = await MessageController.SendBytes(
bytes,
noisyChannel,
encoder,
decoder);
watch.Stop();
Console.WriteLine($"{currentSize},{watch.Elapsed}");
currentSize += deltaN;
}
}
private async void VectorSubmit_Click(object sender, EventArgs e)
{
var vectorText = VectorInput.Text;
string channelNoiseText = ChannelNoiseInput.Text;
if (!double.TryParse(channelNoiseText.Replace(',', '.'), out var noise) || noise < 0 || noise > 1)
{
MessageBox.Show("Noise must be a number between 0 and 1.");
return;
}
var channel = new NoisyChannel(errorChance: noise);
var encodedResult = await MessageController.EncodeAndSendVector(vectorText, new ConvolutionalEncoder(), channel);
DisplayEncodedVector(encodedResult);
}
private async void uploadImageButton_Click(object sender, EventArgs e)
{
encodingResultPanel.Controls.Clear();
encodingResultPanel.Controls.Add(UIHelper.CreateLabelWithText("Loading images... Please wait."));
var dialog = new OpenFileDialog
{
ShowHelp = true,
Filter = "Image Files(*.jpeg;*.bmp;*.png;*.jpg)|*.jpeg;*.bmp;*.png;*.jpg"
};
var Response = dialog.ShowDialog();
if (Response != DialogResult.OK)
{
return;
}
var channelNoiseText = ChannelNoiseInput.Text;
if (!double.TryParse(channelNoiseText.Replace(',', '.'), out var noise) || noise < 0 || noise > 1)
{
MessageBox.Show("Noise must be a number between 0 and 1.");
return;
}
var channel = new NoisyChannel(noise);
var originalImage = new Bitmap(dialog.FileName);
var scaledImage = BitmapHelper.ScaleBitmap(originalImage, desiredWidth: 200);
var imageBytes = BitmapHelper.GetColorBytes(scaledImage);
var unencodedResult = await MessageController.SendBytes(imageBytes, channel);
var encodedResult = await MessageController.SendBytes(
imageBytes,
channel,
new ConvolutionalEncoder(),
new ConvolutionalDecoder());
DisplayImageResult(scaledImage, unencodedResult, encodedResult);
}
public static async Task RunBenchmark(double errorDelta, double expectedErrorCorrectionRate)
{
var bmp = new Bitmap(100, 100);
var bytes = BitmapHelper.GetColorBytes(bmp);
double averageErrorCorrectionPercentage = 100;
double currentErrorChance = 0;
var encoder = new ConvolutionalEncoder();
var decoder = new ConvolutionalDecoder();
do
{
var noisyChannel = new NoisyChannel(currentErrorChance);
double sum = 0;
for (int i = 0; i < 10; i++)
{
var resultWithoutEncoding = await MessageController.SendBytes(
bytes, noisyChannel
);
var resultWithEncoding = await MessageController.SendBytes(
bytes,
new NoisyChannel(currentErrorChance),
encoder,
decoder);
var Percentage =
MathHelper.CalculatePercentageWithPrecision(
resultWithEncoding.Errors,
resultWithoutEncoding.Errors,
2);
sum += Percentage;
}
averageErrorCorrectionPercentage = sum / 10;
Console.WriteLine($"{currentErrorChance},{averageErrorCorrectionPercentage:F2}");
currentErrorChance += errorDelta;
} while(averageErrorCorrectionPercentage >= expectedErrorCorrectionRate);
}
private async void TextSubmit_Click(object sender, EventArgs e)
{
var initialText = TextInput.Text;
string channelNoiseText = ChannelNoiseInput.Text;
if (!double.TryParse(channelNoiseText.Replace(',', '.'), out var noise) || noise < 0 || noise > 1)
{
MessageBox.Show("Noise must be a number between 0 and 1.");
return;
}
var channel = new NoisyChannel(errorChance: noise);
var unencodedResult = await MessageController.SendText(initialText, channel);
var encodedResult = await MessageController.SendText(
initialText,
channel,
new ConvolutionalEncoder(),
new ConvolutionalDecoder());
DisplayTextResult(initialText, unencodedResult, encodedResult);
}
