private void loadTrain(string path, string lpath, ClassifiedSample <double[][, ]> sample)
{
sample.Clear();
using (var lfile = File.Open(lpath, FileMode.Open, FileAccess.Read))
using (var reader = new StreamReader(lfile))
{
reader.ReadLine(); // read label file header
var dir = new DirectoryInfo(path);
foreach (var file in dir.EnumerateFiles())
{
var data = loadFile(file.FullName);
var clsName = reader.ReadLine().Split(',')[1];
var cls = m_Classes.First(c => c.Value.Name.Equals(clsName)).Value;
sample.Add(data, cls);
}
}
Console.WriteLine("Loaded files from: {0}", path);
}
private void loadSample(string ipath, ClassifiedSample <double[][, ]> sample)
{
sample.Clear();
using (var ifile = File.Open(ipath, FileMode.Open, FileAccess.Read))
using (var reader = new StreamReader(ifile))
{
var header = reader.ReadLine();
while (true)
{
var str = reader.ReadLine();
if (string.IsNullOrWhiteSpace(str))
{
break;
}
var raw = str.Split(',')
.Select(d => int.Parse(d))
.ToArray();
var label = raw[0];
var data = new double[1][, ] {
new double[IMG_SIZE, IMG_SIZE]
};
for (int i = 1; i <= IMG_SIZE * IMG_SIZE; i++)
{
var shade = raw[i]; // do not invert 255-* because we want to keep logical format: 0=white, 255=black - not image color format!
var x = (i - 1) % IMG_SIZE;
var y = (i - 1) / IMG_SIZE;
data[0][y, x] = shade / 255.0D;
}
sample.Add(data, m_Classes[label]);
}
Console.WriteLine("Loaded: {0}", ipath);
}
}