private void InitHopfieldModelFromTextData(string initDir)
{
var di = new DirectoryInfo(initDir);
var files = di.GetFiles("*.txt");
size = files.Length;
xs = new List <int[]>(size);
classes = new List <string>(size);
for (var i = 0; i < size; ++i)
{
var numbers = new List <int>();
var rows = File.ReadAllLines(files[i].FullName);
foreach (var line in rows)
{
var splitted = line.Split(' ');
foreach (var word in splitted)
{
if (word != "")
{
numbers.Add(int.Parse(word));
}
}
}
xs.Add(numbers.ToArray());
var className = Path.GetFileNameWithoutExtension(files[i].Name);
classes.Add(className);
}
assotiatedMemory = new HopfieldModel(xs);
}
private int getSampleIndex(int[] y)
{
var ind = 0;
var minHammingDist = HopfieldModel.hammingDistance(xs[0], y);
for (var i = 1; i < xs.Count; ++i)
{
var hd = HopfieldModel.hammingDistance(xs[i], y);
if (hd < minHammingDist)
{
ind = i;
minHammingDist = hd;
}
}
return(ind);
}
private void InitHopfieldModel(string initDir)
{
var di = new DirectoryInfo(initDir);
var files = di.GetFiles("*.jpg");
size = files.Length;
xs = new List <int[]>(size);
classes = new List <string>(size);
for (var i = 0; i < size; ++i)
{
var b = new Bitmap(Image.FromFile(files[i].FullName));
var bipolarized = SignalProcessor.bipolarize(b, Color.FromArgb(0, 0, 0), 50);
xs.Add(bipolarized);
var className = Path.GetFileNameWithoutExtension(files[i].Name);
classes.Add(className);
}
assotiatedMemory = new HopfieldModel(xs);
}
