AdvancedOCR.DataSetItem.LoadLeCunSet C# (CSharp) Method

        private static DataSetItem[] LoadLeCunSet(string imagePath, string labelPath)
        {
            FileStream ImageStream = new FileStream(imagePath, FileMode.Open);
            FileStream LabelStream = new FileStream(labelPath, FileMode.Open);
            BinaryReader brImage = new BinaryReader(ImageStream);
            BinaryReader brLabel = new BinaryReader(LabelStream);

            if (ReadBigEndianInteger(brImage) != 2051)
                throw new InvalidDataException("Invalid magic in specified image file.");
            if (ReadBigEndianInteger(brLabel) != 2049)
                throw new InvalidDataException("Invalid magic in specified label file.");

            int ItemCount = ReadBigEndianInteger(brImage);
            if (ReadBigEndianInteger(brLabel) != ItemCount)
                throw new InvalidDataException("Number of images and labels do not match.");

            int rows = ReadBigEndianInteger(brImage);
            int columns = ReadBigEndianInteger(brImage);

            DataSetItem[] items = new DataSetItem[ItemCount];
            for (int i = 0; i < ItemCount; i++)
            {
                char character = (char)(brLabel.ReadByte().ToString()[0]);

                // Read image with border of 2 on all sides.
                double[] inputs = new double[(rows + 4) * (columns + 4)];
                for (int x = 0; x < (rows + 4); x++)
                    for (int y = 0; y < (columns + 4); y++)
                    {
                        inputs[x + y * (rows + 4)] = -0.1;
                    }

                int inputIndex = 2;
                for (int y = 2; y < (columns + 2); y++)
                {
                    for (int x = 2; x < (rows + 2); x++)
                    {

                        // Background pixel is -0.1 (black) and Foreground is 1.175.
                        // Refer to page 7 of LeCun's document on Gradient-based learning applied to document recognition.
                        inputs[inputIndex] = (((double)brImage.ReadByte()) / 255.0) * 1.275 - 0.1;

                        inputIndex += 1;
                    }
                    inputIndex += 4;
                }
                items[i] = new DataSetItem() { Inputs = inputs, Character = character };
            }
            return items;
        }

 private static IList <DataSetItem> LoadGeneralisationDataset()
 {
     if (!File.Exists(GeneralisationDatasetCache))
     {
         DataSetItem[] set = DataSetItem.LoadLeCunSet(GeneralisationDatasetImages, GeneralisationDatasetLabels);
         DataSetItem.SaveNativeSet(GeneralisationDatasetCache, set);
         return(set);
     }
     else
     {
         return(DataSetItem.LoadNativeSet(GeneralisationDatasetCache));
     }
 }