感知器Perceptron

//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; }