public static int Calculate(int age, string sex, Bitmap brainScan)
{
double[] input;
var pixelBrightness = SupportAI.CalculateScan(brainScan);
if (sex.ToLower() == Properties.Resource.Female)
{
input = new double[] { age, 1.0, pixelBrightness[0], pixelBrightness[1] }
}
;
else
{
input = new double[] { age, .0, pixelBrightness[0], pixelBrightness[1] }
};
input[0] = SupportAI.Normalize(input[0], double.Parse(Properties.Resource.NormalizeAgeMax), double.Parse(Properties.Resource.NormalizeAgeMin));
input[2] = SupportAI.Normalize(input[2], double.Parse(Properties.Resource.NormalizeBrightnessMax), double.Parse(Properties.Resource.NormalizeBrightnessMin));
input[3] = SupportAI.Normalize(input[3], double.Parse(Properties.Resource.NormalizeDarknessMax), double.Parse(Properties.Resource.NormalizeDarknessMin));
Network network = new Network(new int[] { 4, 7, 7, 1 });
network.ImportBiases("...\\...\\...\\NeuralNetwork\\NeuralNetwork\\Biases.txt");
network.ImportWeights("...\\...\\...\\NeuralNetwork\\NeuralNetwork\\Weights.txt");
return(network.FeedForward(input));
}
}
static void Main(string[] args)
{
double[][] dataLearning = SupportAI.LoadData(Properties.Resource.TrainingData, 0, true);
double[][] inputLearning = dataLearning.Select(line => line.Take(4).ToArray()).ToArray();
double[][] outputLearning = dataLearning.Select(line => line.Skip(4).ToArray()).ToArray();
double[][] dataTest = SupportAI.LoadData(Properties.Resource.TestData, dataLearning.Last().Length, false);
double[][] inputTest = dataTest.Select(line => line.Take(4).ToArray()).ToArray();
double[][] outputTest = dataTest.Select(line => line.Skip(4).ToArray()).ToArray();
Network network = new Network(new int[] { 4, 7, 7, 1 });
network.ImportBiases("...\\...\\Biases.txt");
network.ImportWeights("...\\...\\Weights.txt");
network.ShowNetwork();
int gen = 500;
double procentage = 0.0;
int tmp = 1;
while (procentage < 65)
{
// v uczenie v
for (int j = 0; j < gen; j++)
{
for (int i = 0; i < inputLearning.Length; i++)
{
network.Learning(inputLearning[i], outputLearning[i]);
if (j > gen - 2 && procentage > 60)
{
network.ShowOutput(outputLearning[i]);
}
}
}
//v testowanie v
int pass = 0;
for (int i = 0; i < inputTest.Length; i++)
{
if (network.FeedForward(inputTest[i], outputTest[i]))
{
pass++;
}
// network.ShowOutput(outputTest[i]);
}
procentage = 100.0 * pass / inputTest.Length;
Console.WriteLine($"Gen: {(tmp++)*gen} \nAll: {inputTest.Length} Correct: {pass} Procentage: {procentage}%");
}
network.ExportBiases("...\\...\\Biases.txt");
network.ExportWeights("...\\...\\Weights.txt");
Console.ReadKey();
}
