relief算法优化版c语言

以下是基于Relief算法的优化版C语言代码实现：

#include<stdio.h>
#include<stdlib.h>
#include<math.h>

#define MAX_FEATURES 100 //最大特征数
#define MAX_SAMPLES 1000 //最大样本数
#define MAX_ITERATIONS 100 //最大迭代次数
#define K_NEIGHBORS 5 //K近邻数量
#define ALPHA 0.5 //衰减因子
#define THRESHOLD 0.0 //特征权重阈值

double data[MAX_SAMPLES][MAX_FEATURES]; //样本特征数据
int labels[MAX_SAMPLES]; //样本标签数据
double weights[MAX_FEATURES]; //特征权重

int num_features; //特征数
int num_samples; //样本数
int num_iterations; //迭代次数

//计算两个样本的欧几里得距离
double euclidean_distance(int sample1, int sample2) {
    double distance = 0.0;
    for (int i = 0; i < num_features; i++) {
        distance += pow(data[sample1][i] - data[sample2][i], 2);
    }
    return sqrt(distance);
}

//计算样本与其K个最近邻之间的权重更新值
void compute_weights(int sample_index) {
    //计算K个最近邻
    int neighbors[K_NEIGHBORS];
    double distances[K_NEIGHBORS];
    for (int i = 0; i < K_NEIGHBORS; i++) {
        neighbors[i] = -1;
        distances[i] = INFINITY;
    }
    for (int i = 0; i < num_samples; i++) {
        if (i == sample_index) continue;
        double distance = euclidean_distance(sample_index, i);
        for (int j = 0; j < K_NEIGHBORS; j++) {
            if (distance < distances[j]) {
                for (int k = K_NEIGHBORS - 1; k > j; k--) {
                    neighbors[k] = neighbors[k - 1];
                    distances[k] = distances[k - 1];
                }
                neighbors[j] = i;
                distances[j] = distance;
                break;
            }
        }
    }
    //更新特征权重
    for (int i = 0; i < num_features; i++) {
        double sum = 0.0;
        for (int j = 0; j < K_NEIGHBORS; j++) {
            double diff = data[sample_index][i] - data[neighbors[j]][i];
            sum += pow(diff, 2);
        }
        weights[i] -= (sum / K_NEIGHBORS) * ALPHA;
    }
}

//Relief算法主函数
void relief() {
    //初始化特征权重
    for (int i = 0; i < num_features; i++) {
        weights[i] = 0.0;
    }
    //迭代更新特征权重
    for (int i = 0; i < num_iterations; i++) {
        int sample_index = rand() % num_samples;
        compute_weights(sample_index);
    }
}

//读取样本数据函数
void read_data(char* filename) {
    FILE* fp = fopen(filename, "r");
    if (fp == NULL) {
        printf("Error: cannot open file.\n");
        exit(1);
    }
    //读取特征数和样本数
    fscanf(fp, "%d %d", &num_features, &num_samples);
    //读取样本特征和标签数据
    for (int i = 0; i < num_samples; i++) {
        for (int j = 0; j < num_features; j++) {
            fscanf(fp, "%lf", &data[i][j]);
        }
        fscanf(fp, "%d", &labels[i]);
    }
    fclose(fp);
}

//输出特征权重函数
void print_weights() {
    for (int i = 0; i < num_features; i++) {
        if (weights[i] > THRESHOLD || weights[i] < -THRESHOLD) {
            printf("Feature %d: %lf\n", i + 1, weights[i]);
        }
    }
}

int main(int argc, char* argv[]) {
    if (argc != 4) {
        printf("Usage: ./relief <data_file> <num_iterations> <threshold>\n");
        exit(1);
    }
    //读取命令行参数
    num_iterations = atoi(argv[2]);
    THRESHOLD = atof(argv[3]);
    //读取样本数据
    read_data(argv[1]);
    //运行Relief算法
    relief();
    //输出特征权重
    print_weights();
    return 0;
}

其中，`data`数组存储样本特征数据，`labels`数组存储样本标签数据，`weights`数组存储特征权重。`euclidean_distance`函数计算两个样本的欧几里得距离，`compute_weights`函数计算样本与其K个最近邻之间的权重更新值，`relief`函数是Relief算法的主函数。使用`read_data`函数读取样本数据，使用`print_weights`函数输出特征权重。在`main`函数中，读取命令行参数，运行Relief算法，输出特征权重。