感知器Perceptron

柔情只为你懂 2022-09-27 15:30 228阅读 0赞

//perceptron.h
    #ifndef PERCEPTRON_H__
    #define PERCEPTRON_H__
    
    #include <stdio.h>
    #include <stdlib.h>
    #include <string.h>
    #include <string>
    
    class perceptron { 
    public:
        float *weights;   //权重
        float bias;      //偏差
        int input_num;   //输入参数个数
    
    public:
        perceptron(int inputNum);
        int activator(float x);    //激活函数
        int predict(int *input);
        void train(int *input, int label, float learning_rate);
        void update(int *input, int output, int label, float learning_rate);    //label是期望的输出，output是实际的输出
    };
    #endif

//perceptron.cpp
    #include "perceptron.h"
    
    perceptron::perceptron(int inputNum)
    {
        input_num = inputNum;
        weights = (float*) malloc(sizeof(float) * inputNum);
        bzero(weights, sizeof(float) * inputNum);
        weights[0] = 0; weights[1] = 0.00125;
        bias = 0.13;
    }
    
    int perceptron::activator(float x)
    {
        if (x > 0) {
            return 1;
        } else {
            return 0;
        }
    }
    
    int perceptron::predict(int *input)
    {
        float y = bias;
        for (int i = 0; i < input_num; ++i) {
            y += weights[i] * input[i];
        }
        return activator(y);
    }
    
    void perceptron::train(int *input, int label, float learning_rate)
    {
        int y = predict(input);
        update(input, y, label, learning_rate);
    }
    
    void perceptron::update(int *input, int output, int label, float learning_rate)
    {
        float delta = label - output;
        for (int i = 0; i < input_num; ++i) {
            weights[i] += learning_rate * delta * input[i];
        }
        bias += delta * learning_rate;
    }

//add.cpp
    #include "perceptron.h"
    
    #define epochs 10
    #define input_num 2
    #define learning_rate 0.1
    void train_and_perceptron(perceptron &P)
    {
        for (int i = 0; i < epochs; ++i) {
            int *input = (int *) malloc(sizeof(int) * input_num);
            int output;
            input[0] = 1, input[1] = 1, output = 1;    //数据一
            P.train(input, output, learning_rate);
    
            input[0] = 1, input[1] = 0, output = 0;    //数据二
            P.train(input, output, learning_rate);
    
            input[0] = 0, input[1] = 1, output = 0;    //数据三
            P.train(input, output, learning_rate);
    
            input[0] = 0, input[1] = 0, output = 0;    //数据四
            P.train(input, output, learning_rate);
    
            free(input);
        }
    }
    
    int main()
    {
        perceptron P = perceptron(input_num);
        train_and_perceptron(P);
    
        printf("bias: %f weights[0]: %f weights[1]: %f\n", P.bias, P.weights[0], P.weights[1]);
        int *input = (int *) malloc(sizeof(int) * input_num);
        input[0] = 1, input[1] = 1;    //数据一
        printf("1 and 1 = %d\n", P.predict(input));
        input[0] = 1, input[1] = 0;    //数据二
        printf("1 and 1 = %d\n", P.predict(input));
        input[0] = 0, input[1] = 1;    //数据三
        printf("0 and 1 = %d\n", P.predict(input));
        input[0] = 0, input[1] = 0;    //数据四
        printf("0 and 0 = %d\n", P.predict(input));
        free(input);
    
        return 0;
    }

//or.cpp
    #include "perceptron.h"
    
    #define epochs 10
    #define input_num 2
    #define learning_rate 0.1
    
    void train_or_perceptron(perceptron &P)
    {
        for (int i = 0; i < epochs; ++i) {
            int *input = (int *) malloc(sizeof(int) * input_num);
            int output;
            input[0] = 1, input[1] = 1, output = 1;    //数据一
            P.train(input, output, learning_rate);
    
            input[0] = 1, input[1] = 0, output = 1;    //数据二
            P.train(input, output, learning_rate);
    
            input[0] = 0, input[1] = 1, output = 1;    //数据三
            P.train(input, output, learning_rate);
    
            input[0] = 0, input[1] = 0, output = 0;    //数据四
            P.train(input, output, learning_rate);
    
            free(input);
        }
    }
    
    int main()
    {
        perceptron P = perceptron(input_num);
        train_or_perceptron(P);
    
        printf("bias: %f weights[0]: %f weights[1]: %f\n", P.bias, P.weights[0], P.weights[1]);
        int *input = (int *) malloc(sizeof(int) * input_num);
        input[0] = 1, input[1] = 1;    //数据一
        printf("1 or 1 = %d\n", P.predict(input));
        input[0] = 1, input[1] = 0;    //数据二
        printf("1 or 1 = %d\n", P.predict(input));
        input[0] = 0, input[1] = 1;    //数据三
        printf("0 or 1 = %d\n", P.predict(input));
        input[0] = 0, input[1] = 0;    //数据四
        printf("0 or 0 = %d\n", P.predict(input));
        free(input);
    
        return 0;
    }