public override int Recognize(string filename)
{
List <double> x = AlgsWithFiles.binarImage(filename, baseColor);
for (int i = 0; i < K; ++i)
{
double s = 0;
for (int j = 0; j < M; ++j)
{
s += weightMatrix[i, j] * x[j];
}
s += M / 2.0;
// здесь s - состояние нейрона первого слоя
outputs[i] = s;
}
Array.Copy(outputs, tmpoutputs, outputs.Length);
double hammingDist;
int indx = -1;
while (true)
{
outputs = Matrix.MultiplyOnVector(feedbackSynapse, tmpoutputs);
hammingDist = 0;
for (int i = 0; i < K; ++i)
{
outputs[i] = TresholdFun(outputs[i]);
hammingDist += Math.Abs(outputs[i] - tmpoutputs[i]);
}
if (hammingDist <= emax)
{
double max = outputs[0];
indx = 0;
for (int i = 1; i < K; ++i)
{
if (outputs[i] > max)
{
max = outputs[i];
indx = i;
}
}
break;
}
Array.Copy(outputs, tmpoutputs, outputs.Length);
}
return(indx);
}
public override int Recognize(string filename)
{
List <double> x = AlgsWithFiles.bipolImage(filename, baseColor);
double[] fstSignals = Matrix.getDoubleArr(x);
double[] sndSignals = fstSignals;
double eps = 0.001;
for (int i = 0; i < 10000; ++i)
{
double err = 0.0;
sndSignals = Matrix.MultiplyOnVector(weightMatrix, fstSignals);
for (int j = 0; j < sndSignals.Length; ++j)
{
sndSignals[j] = TresholdFunc(sndSignals[j]);
}
for (int j = 0; j < sndSignals.Length; ++j)
{
err += fstSignals[j] * sndSignals[j];
}
err = fstSignals.Length - err;
fstSignals = sndSignals;
if (err < eps)
{
break;
}
}
// find result
double minDist = double.MaxValue;
int index = 0;
for (int k = 0; k < samples.Count; ++k)
{
double dist = 0.0;
for (int j = 0; j < sndSignals.Length; ++j)
{
dist += samples[k][j] * sndSignals[j];
}
dist = fstSignals.Length - dist;
if (dist < minDist)
{
minDist = dist;
index = k;
}
}
return(index);
}
// создание сети по названию папки с эталонными изображениями, обучение сети
public HammingNN(String dirname, Color baseColor)
{
List <List <double> > input = AlgsWithFiles.processingFilesInDirectory(dirname, AlgsWithFiles.binarImage, baseColor);
K = input.Count;
M = input[0].Count;
this.baseColor = baseColor;
outputs = new double[K];
tmpoutputs = new double[K];
weightMatrix = new double[K, M];
feedbackSynapse = new double[K][];
double eps = 1 / (1.2 * K);
// инициализация матриц весовой и синапсов обр связи
for (int i = 0; i < K; ++i)
{
for (int j = 0; j < M; ++j)
{
weightMatrix[i, j] = input[i][j] / 2.0;
}
////
feedbackSynapse[i] = new double[K];
for (int k = 0; k < K; ++k)
{
if (i == k)
{
feedbackSynapse[i][k] = 1;
}
else
{
feedbackSynapse[i][k] = -eps;
}
}
}
}
public HopfieldNN(String dirname, Color baseColor)
{
samples = AlgsWithFiles.processingFilesInDirectory(dirname, AlgsWithFiles.bipolImage, baseColor);
this.baseColor = baseColor;
int n = samples[0].Count;
weightMatrix = new double[n][];
for (int i = 0; i < n; ++i)
{
weightMatrix[i] = new double[n];
for (int j = 0; j < n; ++j)
{
double sum = 0;
for (int k = 0; k < samples.Count; ++k)
{
sum += samples[k][i] * samples[k][j];
}
weightMatrix[i][j] = sum / n;
}
}
}
