Реализовать когнитрон

[Marfein]

public class Cognitron { private int numInputs; private int numOutputs; private double[][] weights; private double[] output; private double[] threshold;

// Конструктор
public Cognitron(int numInputs, int numOutputs) {
    this.numInputs = numInputs;
    this.numOutputs = numOutputs;
    this.weights = new double[numOutputs][numInputs];
    this.output = new double[numOutputs];
    this.threshold = new double[numOutputs];
}

// Метод для обучения когнитрона
public void train(double[][] input, double[][] target, double learningRate, int numIterations) {
    // Реализация алгоритма обучения когнитрона
}

// Метод для распознавания образов
public double[] recognize(double[] input) {
    // Реализация алгоритма распознавания образов
    return output;
}

}

[Marfein]

public void train(double[][] input, double[][] target, double learningRate, int numIterations) { int numExamples = input.length; for (int iteration = 0; iteration < numIterations; iteration++) { for (int example = 0; example < numExamples; example++) { double[] inputVector = input[example]; double[] targetVector = target[example]; double[] activation = new double[numOutputs]; for (int j = 0; j < numOutputs; j++) { double sum = 0; for (int i = 0; i < numInputs; i++) { sum += weights[j][i] inputVector[i]; } activation[j] = sum - threshold[j]; output[j] = activationFunction(activation[j]); } for (int j = 0; j < numOutputs; j++) { double delta = (targetVector[j] - output[j]) derivativeFunction(activation[j]); for (int i = 0; i < numInputs; i++) { weights[j][i] += learningRate delta inputVector[i]; } threshold[j] -= learningRate * delta; } } } }

[Marfein]

public double[] recognize(double[] input) { double[] activation = new double[numOutputs]; for (int j = 0; j < numOutputs; j++) { double sum = 0; for (int i = 0; i < numInputs; i++) { sum += weights[j][i] * input[i]; } activation[j] = sum - threshold[j]; output[j] = activationFunction(activation[j]); } return output; }

[Marfein]

private double activationFunction(double x) { return 1 / (1 + Math.exp(-x)); }

private double derivativeFunction(double x) { double fx = activationFunction(x); return fx * (1 - fx); }

[Marfein]

public static void main(String[] args) { // Загрузка данных для обучения double[][] trainingData = {{0,0,1,1,0,0,0,0,0,0}, {0,1,1,1,1,0,0,0,0,0}, {1,1,0,1,1,1,0,0,0,0}, {1,1,1,0,1,1,1,0,0,0}, {0,1,1,1,1,0,0,0,0,0}, {0,0,1,1,0,0,0,0,0,0}, {0,0,0,0,0,0,0,0,0,1}, {0,0,0,0,0,0,0,0,1,1}, {0,0,0,0,0,0,0,1,1,1}, {0,0,0,0,0,0,1,1,1,0}, {0,0,0,0,0,1,1,1,0,0}, {0,0,0,0,1,1,1,0,0,0}, {0,0,0,1,1,1,0,0,0,0}, {0,0,1,1,1,0,0,0,0,0}, {0,1,1,1,0,0,0,0,0,0}, {1,1,1,0,0,0,0,0,0,0}, {1,1,0,0,0,0,0,0,0,0}, {1,0,0,0,0,0,0,0,0,0}, {1,0,0,0,0,0,0,0,0,1}, {1,0,0,0,0,0,0,0,1,1}, {1,0,0,0,0,0,0,1,1,1}, {1,0,0,0,0,0,1,1,1,0}, {1,0,0,0,0,1,1,1,0,0}, {1,0,0,0,1,1,1,0,0,0}, {1,0,0,1,1,1,0,0,0,0}, {1,0,1,1,1,0,0,0,0,0}, {1,1,1,1,0,0,0,0,0,0}, {0,1,1,0,0,0,0,0,0,0}, {0,0,0,0,0,0,0,0,0,0}}; double[][] testData = {{0,0,1,1,0,0,0,0,0,0}, {0,1,1,1,1,0,0,0,0,0}, {1,1,1,1,1,1,0,0,0,0}, {1,1,1,1,0,0,0,0,0,0}, {0,1,1,0,0,0,0,0,0,0}, {0,0,0,0,0,0,0,0,0,0}};

// Создание когнитрона с 10 входами и 7 скрытыми нейронами
Cognitron cognitron = new Cognitron(10, 7);

// Обучение когнитрона на данных для обучения
cognitron.train(trainingData);

// Распознавание образов в тестовых данных
for (int i = 0; i < testData.length; i++) {
    double[] input = testData[i];
    double[] output = cognitron.recognize(input);
    int recognizedDigit = getMaxIndex(output);

    System.out.print("Test " + (i + 1) + ": ");
    printArray(input);
    System.out.println("Recognized digit: " + recognizedDigit);
}

}

private static int getMaxIndex(double[] array) { int maxIndex = 0; double maxValue = array[0];

for (int i = 1; i < array.length; i++) {
    if (array[i] > maxValue) {
        maxIndex = i;
        maxValue = array[i];
    }
}

return maxIndex;

}

private static void printArray(double[] array) { for (double d : array) { System.out.print((int) d + " "); } System.out.println(); }