relief算法改进版c语言

在C语言中实现改进版的relief算法，可以按照以下步骤进行：

1. 定义数据结构：首先需要定义一个数据结构来表示样本数据，该数据结构可以包括特征值和类别标签。

typedef struct {
    double *features;   // 特征值
    int label;          // 类别标签
} Sample;

2. 加载数据：从文件中读取样本数据，并将其存储到一个数组中。

Sample *load_samples(char *filename, int num_samples, int num_features)
{
    FILE *fp;
    Sample *samples;
    int i, j;

    fp = fopen(filename, "r");
    if (fp == NULL) {
        fprintf(stderr, "Error: cannot open file %s\n", filename);
        exit(EXIT_FAILURE);
    }

    samples = (Sample *)malloc(num_samples * sizeof(Sample));
    if (samples == NULL) {
        fprintf(stderr, "Error: cannot allocate memory for samples\n");
        exit(EXIT_FAILURE);
    }

    for (i = 0; i < num_samples; i++) {
        samples[i].features = (double *)malloc(num_features * sizeof(double));
        if (samples[i].features == NULL) {
            fprintf(stderr, "Error: cannot allocate memory for features\n");
            exit(EXIT_FAILURE);
        }

        for (j = 0; j < num_features; j++) {
            fscanf(fp, "%lf", &samples[i].features[j]);
        }

        fscanf(fp, "%d", &samples[i].label);
    }

    fclose(fp);

    return samples;
}

3. 计算距离：定义一个函数来计算两个样本之间的欧几里得距离。

double distance(double *a, double *b, int num_features)
{
    int i;
    double sum = 0.0;

    for (i = 0; i < num_features; i++) {
        sum += (a[i] - b[i]) * (a[i] - b[i]);
    }

    return sqrt(sum);
}

4. 计算邻域：定义一个函数来计算每个样本的近邻样本，并将其存储到一个邻域矩阵中。

void compute_neighbors(Sample *samples, int num_samples, int num_features, int **neighbors, int k)
{
    int i, j, l;
    double dist;
    int *indices;
    double *dists;

    indices = (int *)malloc(num_samples * sizeof(int));
    if (indices == NULL) {
        fprintf(stderr, "Error: cannot allocate memory for indices\n");
        exit(EXIT_FAILURE);
    }

    dists = (double *)malloc(num_samples * sizeof(double));
    if (dists == NULL) {
        fprintf(stderr, "Error: cannot allocate memory for dists\n");
        exit(EXIT_FAILURE);
    }

    for (i = 0; i < num_samples; i++) {
        for (j = 0; j < k; j++) {
            indices[j] = j;
            dists[j] = distance(samples[i].features, samples[j].features, num_features);
        }

        for (j = k; j < num_samples; j++) {
            dist = distance(samples[i].features, samples[j].features, num_features);

            for (l = 0; l < k; l++) {
                if (dist < dists[l]) {
                    memmove(&indices[l+1], &indices[l], (k-l-1) * sizeof(int));
                    memmove(&dists[l+1], &dists[l], (k-l-1) * sizeof(double));
                    indices[l] = j;
                    dists[l] = dist;
                    break;
                }
            }
        }

        for (j = 0; j < k; j++) {
            neighbors[i][j] = indices[j];
        }
    }

    free(indices);
    free(dists);
}

5. 计算权重：定义一个函数来计算每个特征的权重，并将其存储到一个权重向量中。

void compute_weights(Sample *samples, int num_samples, int num_features, int **neighbors, int k, double *weights)
{
    int i, j, l;
    double diff;

    for (i = 0; i < num_features; i++) {
        weights[i] = 0.0;

        for (j = 0; j < num_samples; j++) {
            for (l = 0; l < k; l++) {
                if (samples[neighbors[j][l]].label != samples[j].label) {
                    diff = samples[j].features[i] - samples[neighbors[j][l]].features[i];
                    weights[i] += fabs(diff);
                    break;
                }
            }
        }

        weights[i] /= (double)(num_samples * k);
    }
}

6. 计算偏移：定义一个函数来计算每个特征的偏移量，并将其存储到一个偏移向量中。

void compute_offsets(Sample *samples, int num_samples, int num_features, int **neighbors, int k, double *weights, double *offsets)
{
    int i, j, l;
    double diff;

    for (i = 0; i < num_features; i++) {
        offsets[i] = 0.0;

        for (j = 0; j < num_samples; j++) {
            for (l = 0; l < k; l++) {
                if (samples[neighbors[j][l]].label != samples[j].label) {
                    diff = samples[j].features[i] - samples[neighbors[j][l]].features[i];
                    offsets[i] += weights[i] * diff;
                    break;
                }
            }
        }

        offsets[i] /= (double)(num_samples * k);
    }
}

7. 计算新特征：定义一个函数来计算每个样本的新特征，并将其存储到一个新特征矩阵中。

void compute_new_features(Sample *samples, int num_samples, int num_features, double *weights, double *offsets, double **new_features)
{
    int i, j;

    for (i = 0; i < num_samples; i++) {
        new_features[i] = (double *)malloc(num_features * sizeof(double));
        if (new_features[i] == NULL) {
            fprintf(stderr, "Error: cannot allocate memory for new features\n");
            exit(EXIT_FAILURE);
        }

        for (j = 0; j < num_features; j++) {
            new_features[i][j] = samples[i].features[j] - weights[j] * samples[i].label - offsets[j];
        }
    }
}

8. 应用算法：将以上步骤组合在一起，定义一个函数来应用改进版的relief算法。

void relief_improved(Sample *samples, int num_samples, int num_features, int k, double *weights, double *offsets, double **new_features)
{
    int **neighbors;
    int i;

    neighbors = (int **)malloc(num_samples * sizeof(int *));
    if (neighbors == NULL) {
        fprintf(stderr, "Error: cannot allocate memory for neighbors\n");
        exit(EXIT_FAILURE);
    }

    for (i = 0; i < num_samples; i++) {
        neighbors[i] = (int *)malloc(k * sizeof(int));
        if (neighbors[i] == NULL) {
            fprintf(stderr, "Error: cannot allocate memory for neighbors\n");
            exit(EXIT_FAILURE);
        }
    }

    compute_neighbors(samples, num_samples, num_features, neighbors, k);
    compute_weights(samples, num_samples, num_features, neighbors, k, weights);
    compute_offsets(samples, num_samples, num_features, neighbors, k, weights, offsets);
    compute_new_features(samples, num_samples, num_features, weights, offsets, new_features);

    for (i = 0; i < num_samples; i++) {
        free(neighbors[i]);
    }

    free(neighbors);
}

这样，我们就可以使用以上函数来实现改进版的relief算法了。