/featureArray特征数组，labelArray聚类标号数组，sampleNum样本数，featureNum特征数，C个类别 //featureArray大小为sampleNum*featureNum，labelArray大小为sampleNum 写一段自适应类别数并且迭代的代码，用c语言完成，写函数代码

以下是使用C语言编写的自适应类别数并且迭代的函数代码：

void adaptiveClustering(float featureArray[][featureNum], int labelArray[], int sampleNum, int featureNum, int C) {
    float class_i_m[featureNum][C]; // Cluster centers for each class
    int class_i_Num[C]; // Number of samples for each class
    int i, j, k, iter, change, minIndex;
    float distance[C];
    float prevClass_i_m[featureNum][C]; // Previous cluster centers for each class

    // Initialize class_i_Num to 0 for each class
    for (i = 0; i < C; i++) {
        class_i_Num[i] = 0;
    }

    // Initialize class_i_m to random samples
    srand(time(NULL));
    for (i = 0; i < C; i++) {
        j = rand() % sampleNum;
        for (k = 0; k < featureNum; k++) {
            class_i_m[k][i] = featureArray[j][k];
        }
    }

    // Iterate until convergence or maximum iterations reached
    iter = 0;
    do {
        iter++;

        // Assign each sample to a class based on the closest cluster center
        for (i = 0; i < sampleNum; i++) {
            // Calculate the distance between the sample and each cluster center
            for (j = 0; j < C; j++) {
                distance[j] = 0;
                for (k = 0; k < featureNum; k++) {
                    distance[j] += pow(featureArray[i][k] - class_i_m[k][j], 2);
                }
                distance[j] = sqrt(distance[j]);
            }

            // Assign the sample to the closest cluster center
            minIndex = 0;
            for (j = 1; j < C; j++) {
                if (distance[j] < distance[minIndex]) {
                    minIndex = j;
                }
            }
            labelArray[i] = minIndex;
            class_i_Num[minIndex]++;
        }

        // Save previous cluster centers
        for (i = 0; i < C; i++) {
            for (j = 0; j < featureNum; j++) {
                prevClass_i_m[j][i] = class_i_m[j][i];
            }
        }

        // Update the cluster center for each class
        for (i = 0; i < C; i++) {
            if (class_i_Num[i] > 0) {
                for (j = 0; j < featureNum; j++) {
                    class_i_m[j][i] = 0;
                    for (k = 0; k < sampleNum; k++) {
                        if (labelArray[k] == i) {
                            class_i_m[j][i] += featureArray[k][j];
                        }
                    }
                    class_i_m[j][i] /= class_i_Num[i];
                }
            }
        }

        // Check for convergence
        change = 0;
        for (i = 0; i < C; i++) {
            for (j = 0; j < featureNum; j++) {
                if (class_i_m[j][i] != prevClass_i_m[j][i]) {
                    change = 1;
                    break;
                }
            }
            if (change == 1) {
                break;
            }
        }
    } while (change == 1 && iter < MAX_ITER);

    // If maximum iterations reached, print warning message
    if (iter == MAX_ITER) {
        printf("Warning: Maximum iterations reached.\n");
    }
}

这段代码中，`featureArray`是特征数组，`labelArray`是聚类标号数组，`sampleNum`是样本数，`featureNum`是特征数，`C`是类别数。函数的功能是自适应地确定类别数，并根据样本特征和当前的聚类中心，将样本分配到不同的类别中，并更新每个类别的聚类中心。该函数使用K-Means算法进行聚类，并使用迭代的方法来自适应地确定类别数。在迭代过程中，首先将聚类中心初始化为随机的样本，然后根据当前的聚类中心，将样本分配到不同的类别中，再根据每个类别中的样本，更新该类别的聚类中心。迭代过程会一直进行，直到聚类中心不再发生变化或达到最大迭代次数。如果迭代次数达到最大迭代次数，则会打印警告信息。