public static int[,] guessAll(double[][]weights, BackgroundWorker bw, int optionsCount, int vectorLength, getVector gv)
{
int progress, maxProgress;
progress = 0;
int[] count = new int[optionsCount];
Bitmap bmp;
List<double> arr;
int ID;
int[,] result = new int[optionsCount, 2];
string[] picturePaths = Directory.GetFiles(pathDigitsAndLetters);
maxProgress = picturePaths.Length;
//foreach (string picturePath in picturePaths)
for(int j=0;j<picturePaths.Length;j++)
{
string picturePath = picturePaths[j];
progress++;
bw.ReportProgress((int)((float)progress / maxProgress * 100));
bmp = new Bitmap(picturePath);
int correctAnswer = getCorrectAnswer(picturePath);
bmp = BmpProcesser.normalizeBitmap(bmp, 100, 100);
arr = guess(gv(bmp), optionsCount, weights);
ID = arr.IndexOf(arr.Min());
if (ID == correctAnswer)
result[correctAnswer, 0]++;
else
result[ID, 1]++;
}
return result;
}
public static double[][] learnAll(int learningCount, BackgroundWorker bw, bool linearDelta, double deltaAtTheEnd, int optionsCount, int vectorLength,getVector gv)
{
double delta = 1;
double[][] weights;
weights = new double[optionsCount][];
for (int n = 0; n < optionsCount; n++)
{
weights[n] = new double[vectorLength];
for (int i = 0; i < vectorLength; i++)
weights[n][i] = 0;
}
int progress, maxProgress;
Bitmap bmp;
string[] picturePaths = Directory.GetFiles(pathDigitsAndLetters);
Random rand = new Random();
progress = 0;
maxProgress = learningCount;
for (int n = 0; n < learningCount; n++)
{
string picturePath = picturePaths[rand.Next(picturePaths.Length)];
progress++;
bw.ReportProgress((int)((float)progress / maxProgress * 100));
bmp = new Bitmap(picturePath);
bmp = BmpProcesser.normalizeBitmap(bmp, 100, 100);
learnKohonen(gv(bmp), getCorrectAnswer(picturePath), weights, optionsCount, delta);
if (deltaAtTheEnd >= 1)
deltaAtTheEnd = 0.99;
if (linearDelta)
{
delta = -(double)progress / ((1 / (1 - deltaAtTheEnd)) * maxProgress) + 1;
}
else
{
if (deltaAtTheEnd <= 0)
deltaAtTheEnd = 0.01;
double a = deltaAtTheEnd / (1 - deltaAtTheEnd);
delta = a * learningCount / ((double)progress + a * learningCount);
}
}
return weights;
}
public static double[][] learnAllAverage(int learningCount, BackgroundWorker bw, int optionsCount, int vectorLength, getVector gv)
{
double[][] weights = new double[optionsCount][];
for (int n = 0; n < optionsCount; n++)
{
weights[n] = new double[vectorLength];
for (int i = 0; i < vectorLength; i++)
weights[n][i] = 0;
}
int progress, maxProgress;
double[] vector1 = new double[vectorLength];
int[] count = new int[optionsCount];
Bitmap bmp;
string[] picturePaths = Directory.GetFiles(pathDigitsAndLetters);
Random rand = new Random();
progress = 0;
maxProgress = learningCount;
for (int n = 0; n < learningCount; n++)
{
string picturePath = picturePaths[rand.Next(picturePaths.Length)];
int k = getCorrectAnswer(picturePath);
progress++;
bw.ReportProgress((int)((float)progress / maxProgress * 100));
bmp = new Bitmap(100, 100);
bmp = new Bitmap(picturePath);
bmp = BmpProcesser.normalizeBitmap(bmp, 100, 100);
vector1 = gv(bmp);
for (int i = 0; i < vectorLength; i++)
weights[k][i] += vector1[i];
count[k]++;
}
for (int k = 0; k < optionsCount; k++)
for (int i = 0; i < vectorLength; i++)
weights[k][i] = weights[k][i] / count[k];
return weights;
}
