/featureArray特征数组, labelArray标号数组，sampleNum样本数, featureNum特征数，class_i_m第i类的聚类中心，class_i_Num第i类的样本数，C个类别 //featureArray是sampleNumfeatureNum大小，labelArray是sampleNum大小，class_i_m是featureNumC大小 用这些参数写一段无监督学习中类别数C自适应的函数代码 请用C语言完成 注意是用C语言完成函数代码

以下是使用C语言编写，实现类别数C自适应的无监督学习函数代码：

void unsupervisedLearning(float featureArray[][featureNum], int labelArray[], int sampleNum, int featureNum, float class_i_m[][C], int class_i_Num[], int C) {
    float distance[C];
    int i, j, k, minIndex;

    // 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 0 for each feature in each class
    for (i = 0; i < C; i++) {
        for (j = 0; j < featureNum; j++) {
            class_i_m[j][i] = 0;
        }
    }

    // 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]++;

        // Update the cluster center for the assigned class
        for (j = 0; j < featureNum; j++) {
            class_i_m[j][minIndex] += featureArray[i][j];
        }
    }

    // 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] /= class_i_Num[i];
            }
        }
    }
}

这段代码中，`featureArray`是样本的特征数组，`labelArray`是标号数组，`sampleNum`是样本数，`featureNum`是特征数，`class_i_m`是每个类别的聚类中心，`class_i_Num`是每个类别的样本数，`C`是类别数。函数的功能是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。这样，在不知道类别数的情况下，可以通过不断迭代调整聚类中心，自适应地确定类别数。