internal void train(UIElementCollection children)
{
bool[] input = new bool[16 * 16];
foreach (Rectangle rect in children)
{
int top = Convert.ToInt32(rect.GetValue(Canvas.TopProperty));
int left = Convert.ToInt32(rect.GetValue(Canvas.LeftProperty));
input[top * 16 + left] = true;
}
bool[] output = (from e in oneHotEncoding where e.Value == trainChar select e.Key).First();
TrainingData trainingData = new TrainingData()
{
input = input, output = output
};
training.Add(trainingData);
singleLayerPerceptron.train(input, output);
}
public MainWindowViewModel()
{
foreach (char c in alphabet)
{
bool[] code = new bool[alphabet.Length];
code[alphabet.IndexOf(c)] = true;
oneHotEncoding.Add(code, c);
}
try
{
using (FileStream fs = File.Open(slpFilePath, FileMode.Open))
{
BinaryFormatter binaryFormatter = new BinaryFormatter();
singleLayerPerceptron = (SingleLayerPerceptron)binaryFormatter.Deserialize(fs);
}
}
catch (Exception e)
{
singleLayerPerceptron = new SingleLayerPerceptron(16 * 16, alphabet.Length);
try
{
#region Load Trainingdata and Train Single Layer Perceptron
List <string> lines = new List <string>();
using (StreamReader streamReader = new StreamReader(File.Open(trainingFilePath, FileMode.OpenOrCreate)))
{
while (!streamReader.EndOfStream)
{
lines.Add(streamReader.ReadLine());
}
}
foreach (string line in lines)
{
TrainingData trainingData = new TrainingData();
string[] splittedLine = line.Split(' ');
string inputString = splittedLine[0];
string outputString = splittedLine[1];
bool[] input = new bool[inputString.Length];
for (int i = 0; i < inputString.Length - 1; i++)
{
if (inputString[i] == '0')
{
input[i] = false;
}
else if (inputString[i] == '1')
{
input[i] = true;
}
}
trainingData.input = input;
//Doublecoding :(
bool[] output = new bool[splittedLine[1].Length];
for (int i = 0; i < outputString.Length - 1; i++)
{
if (outputString[i] == '0')
{
output[i] = false;
}
else if (outputString[i] == '1')
{
output[i] = true;
}
}
trainingData.output = output;
training.Add(trainingData);
}
foreach (var item in training)
{
singleLayerPerceptron.train(item.input, item.output);
}
#endregion
}
catch (Exception)
{
//Nothing special happens here since we initialized training as new list already.
}
}
}