private static void HopfieldNeuralNetwork_Test4()
{
double[] letter1 = new double[9] {
-1, 1, -1, -1, 1, -1, -1, 1, -1
};
double[] letter2 = new double[9] {
1, 1, 1, 1, -1, 1, 1, -1, 1
};
double[] letter3 = new double[9] {
1, -1, 1, 1, 1, 1, 1, -1, 1
};
double[] letter4 = new double[9] {
1, 1, 1, 1, -1, -1, 1, -1, -1
};
double[] letter5 = new double[9] {
-1, -1, -1, -1, 1, -1, -1, -1, -1
};
double[] letterForTest = new double[9] {
-1, -1, 1, 1, 1, 1, 1, -1, 1
};
IList <double[]> standartData = new List <double[]>();
standartData.Add(letter1);
standartData.Add(letter2);
standartData.Add(letter3);
standartData.Add(letter4);
standartData.Add(letter5);
HopfieldNeuralNetwork hn = new HopfieldNeuralNetwork();
hn.Initialize();
hn.Train(standartData);
double[] answer = hn.Run(letterForTest);
Vector answerVector = new Vector(answer);
int simillarCount = -1;
for (int i = 0; i < standartData.Count; i++)
{
Vector vector = new Vector(standartData[i]);
Console.WriteLine($"Standart vector [{i}]: {vector}");
if (answerVector == vector)
{
simillarCount = i;
}
}
Console.WriteLine($"\nCurrentt vector [{simillarCount}]: {new Vector(answer)}");
}
public static void HopefieldNeuralNetworkTest()
{
HopfieldNeuralNetwork network = new HopfieldNeuralNetwork(4);
double[] inputToTrain = new double[] { 1, 1, 0, 0 };
network.Train(inputToTrain);
double[] firstInput = new double[] { 1, 1, 0, 0 };
double[] firstOutput = network.Run(firstInput);
double[] secondInput = new double[] { 1, 0, 0, 0 };
double[] secondOutput = network.Run(secondInput);
Console.WriteLine($"Trained network for {ConvertToTrueAndFalsePresentation(inputToTrain)}");
Console.WriteLine($"Output for {ConvertToTrueAndFalsePresentation(firstInput)} is {ConvertToTrueAndFalsePresentation(firstOutput)}");
Console.WriteLine($"Output for {ConvertToTrueAndFalsePresentation(secondInput)} is {ConvertToTrueAndFalsePresentation(secondOutput)}");
Console.ReadKey();
}
