relief算法c语言
时间: 2024-02-04 22:02:11 浏览: 30
RELIEF算法是一种特征选择算法,用于从数据集中选择最重要的特征。下面是一个简单的C语言实现:
```c
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#define MAX_FEATURES 100
#define MAX_SAMPLES 1000
int main(int argc, char *argv[]) {
// 读入数据
double features[MAX_SAMPLES][MAX_FEATURES];
int labels[MAX_SAMPLES];
int num_samples, num_features;
scanf("%d%d", &num_samples, &num_features);
for (int i = 0; i < num_samples; i++) {
for (int j = 0; j < num_features; j++) {
scanf("%lf", &features[i][j]);
}
scanf("%d", &labels[i]);
}
// 初始化权重
double weights[num_features];
for (int i = 0; i < num_features; i++) {
weights[i] = 0.0;
}
// 计算权重
double nearest_hit[num_features], nearest_miss[num_features];
int nearest_hit_idx, nearest_miss_idx;
double hit_distance, miss_distance;
int num_hits, num_misses;
for (int i = 0; i < num_samples; i++) {
// 找到最近的同类样本和异类样本
num_hits = 0;
num_misses = 0;
for (int j = 0; j < num_samples; j++) {
if (i == j) {
continue;
}
if (labels[j] == labels[i]) {
hit_distance = 0.0;
for (int k = 0; k < num_features; k++) {
hit_distance += pow(features[i][k] - features[j][k], 2.0);
}
hit_distance = sqrt(hit_distance);
if (num_hits == 0 || hit_distance < nearest_hit[0]) {
nearest_hit[0] = hit_distance;
nearest_hit_idx = j;
num_hits = 1;
}
} else {
miss_distance = 0.0;
for (int k = 0; k < num_features; k++) {
miss_distance += pow(features[i][k] - features[j][k], 2.0);
}
miss_distance = sqrt(miss_distance);
if (num_misses == 0 || miss_distance < nearest_miss[0]) {
nearest_miss[0] = miss_distance;
nearest_miss_idx = j;
num_misses = 1;
}
}
}
// 更新权重
for (int j = 0; j < num_features; j++) {
weights[j] -= pow(features[i][j] - features[nearest_hit_idx][j], 2.0) / num_hits;
weights[j] += pow(features[i][j] - features[nearest_miss_idx][j], 2.0) / num_misses;
}
}
// 输出权重
for (int i = 0; i < num_features; i++) {
printf("%lf ", weights[i]);
}
printf("\n");
return 0;
}
```
该代码首先读入特征数据和标签数据,然后初始化权重为0。接着对于每个样本,找到最近的同类样本和异类样本,计算权重的增量,并更新权重。最后输出所有特征的权重。