public void Reading_Data_Training()
{
TrainImages = new List <DigitImage>();
FileStream ifsLabels = new FileStream(@"train-labels.idx1-ubyte", FileMode.Open); // test labels
FileStream ifsImages = new FileStream(@"train-images.idx3-ubyte", FileMode.Open); // test images
BinaryReader brLabels = new BinaryReader(ifsLabels);
BinaryReader brImages = new BinaryReader(ifsImages);
int magic1 = brImages.ReadInt32(); // discard
int numImages = brImages.ReadInt32();
int numRows = brImages.ReadInt32();
int numCols = brImages.ReadInt32();
int magic2 = brLabels.ReadInt32();
int numLabels = brLabels.ReadInt32();
byte[,] pixels = new byte[28, 28];
//for (int i = 0; i < pixels.Length; ++i)
// pixels[i] = new byte[28];
// each test image
for (int di = 0; di < 60000; ++di)
{
for (int i = 0; i < 28; ++i)
{
for (int j = 0; j < 28; ++j)
{
byte b = brImages.ReadByte();
pixels[i, j] = b;
}
}
byte lbl = brLabels.ReadByte();
DigitImage dImage = new DigitImage(pixels, lbl);
TrainImages.Add(dImage);
}
ifsImages.Close();
brImages.Close();
ifsLabels.Close();
brLabels.Close();
}
public static byte Classify(DigitImage digitImage, List <DigitImage> trainingSet, int k)
{
int[] count = new int[10];
List <KeyValuePair <double, byte> > sortedDiff = new List <KeyValuePair <double, byte> >();
foreach (DigitImage trainingImage in trainingSet)
{
double diff = 0.0f;
for (int i = 0; i < 28; i++)
{
for (int j = 0; j < 28; j++)
{
diff += (digitImage.pixels[i, j] - trainingImage.pixels[i, j]) * (digitImage.pixels[i, j] - trainingImage.pixels[i, j]);
}
}
diff = Math.Sqrt(diff);
KeyValuePair <double, byte> p = new KeyValuePair <double, byte>(diff, trainingImage.label);
sortedDiff.Add(p);
}
int index = 0;
sortedDiff.Sort(Compare);
foreach (KeyValuePair <double, byte> diff in sortedDiff)
{
count[diff.Value]++;
index++;
if (index == k)
{
break;
}
}
int max = 0;
byte Class = 0;
for (byte i = 0; i < 10; i++)
{
if (count[i] > max)
{
Class = i;
max = count[i];
}
}
return(Class);
}
