用c语言实现以下功能：分类使用的数据集为iris数据集，数据集描述信息和数据集划分信息如下： iris数据集包含3类别的数据，每类有50个样本，即整个数据集包含150个样本。训练集：从iris数据集随机选取50%作为训练集，即75个训练样本；测试集：iris数据集中剩余的50%作为测试集，即75个测试样本。 要求：编写决策树程序，使用决策树方法在上述数据进行训练测试，并给出测试结果。 注1：需要给出评价指标的测试结果：整体精度OA和类别平均精度AA。 Overall Accuracy = 各类被预测对了的样本数量的累加/预测样本总数； Average Accuracy = 各类预测的精度相加/类别数。

以下是C语言实现决策树的示例代码：

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

#define MAX_FEATURES 4
#define MAX_CLASSES 3
#define MAX_SAMPLES 150
#define MAX_NODES 100

typedef struct sample {
    double features[MAX_FEATURES];
    int label;
} sample;

typedef struct node {
    int feature;
    double threshold;
    int left_child;
    int right_child;
    int label;
} node;

sample training_set[MAX_SAMPLES];
sample testing_set[MAX_SAMPLES];
node decision_tree[MAX_NODES];
int num_nodes = 0;

int num_training_samples = 0;
int num_testing_samples = 0;

double compute_gini_index(sample subset[], int size) {
    int num_classes[MAX_CLASSES] = {0};
    for (int i = 0; i < size; i++) {
        num_classes[subset[i].label]++;
    }
    double gini_index = 1;
    for (int i = 0; i < MAX_CLASSES; i++) {
        double p = (double)num_classes[i] / size;
        gini_index -= p * p;
    }
    return gini_index;
}

void split_data(sample subset[], int size, int feature, double threshold, sample left[], int *num_left, sample right[], int *num_right) {
    *num_left = 0;
    *num_right = 0;
    for (int i = 0; i < size; i++) {
        if (subset[i].features[feature] < threshold) {
            left[*num_left] = subset[i];
            (*num_left)++;
        } else {
            right[*num_right] = subset[i];
            (*num_right)++;
        }
    }
}

void train_decision_tree(int node_index, sample subset[], int size) {
    double gini_index = compute_gini_index(subset, size);
    if (gini_index == 0) {
        decision_tree[node_index].label = subset[0].label;
        return;
    }
    int best_feature = -1;
    double best_threshold;
    double best_gini_index = 1;
    for (int i = 0; i < MAX_FEATURES; i++) {
        for (double j = 0; j < 1; j += 0.01) {
            sample left[MAX_SAMPLES];
            int num_left;
            sample right[MAX_SAMPLES];
            int num_right;
            split_data(subset, size, i, j, left, &num_left, right, &num_right);
            if (num_left > 0 && num_right > 0) {
                double gini_left = compute_gini_index(left, num_left);
                double gini_right = compute_gini_index(right, num_right);
                double gini_index = (double)num_left / size * gini_left + (double)num_right / size * gini_right;
                if (gini_index < best_gini_index) {
                    best_gini_index = gini_index;
                    best_feature = i;
                    best_threshold = j;
                }
            }
        }
    }
    if (best_feature == -1) {
        decision_tree[node_index].label = subset[0].label;
        return;
    }
    decision_tree[node_index].feature = best_feature;
    decision_tree[node_index].threshold = best_threshold;
    decision_tree[node_index].left_child = num_nodes + 1;
    num_nodes++;
    decision_tree[node_index].right_child = num_nodes + 1;
    num_nodes++;
    sample left[MAX_SAMPLES];
    int num_left;
    sample right[MAX_SAMPLES];
    int num_right;
    split_data(subset, size, best_feature, best_threshold, left, &num_left, right, &num_right);
    train_decision_tree(decision_tree[node_index].left_child, left, num_left);
    train_decision_tree(decision_tree[node_index].right_child, right, num_right);
}

int classify_sample(sample s, int node_index) {
    if (decision_tree[node_index].label != -1) {
        return decision_tree[node_index].label;
    }
    if (s.features[decision_tree[node_index].feature] < decision_tree[node_index].threshold) {
        return classify_sample(s, decision_tree[node_index].left_child);
    } else {
        return classify_sample(s, decision_tree[node_index].right_child);
    }
}

void test_decision_tree() {
    int num_correct = 0;
    int confusion_matrix[MAX_CLASSES][MAX_CLASSES] = {0};
    for (int i = 0; i < num_testing_samples; i++) {
        int predicted_label = classify_sample(testing_set[i], 0);
        confusion_matrix[testing_set[i].label][predicted_label]++;
        if (predicted_label == testing_set[i].label) {
            num_correct++;
        }
    }
    double overall_accuracy = (double)num_correct / num_testing_samples;
    printf("Overall Accuracy: %.2f\n", overall_accuracy);
    double average_accuracy = 0;
    for (int i = 0; i < MAX_CLASSES; i++) {
        int num_samples_in_class = 0;
        int num_correct_in_class = 0;
        for (int j = 0; j < num_testing_samples; j++) {
            if (testing_set[j].label == i) {
                num_samples_in_class++;
                if (classify_sample(testing_set[j], 0) == i) {
                    num_correct_in_class++;
                }
            }
        }
        double class_accuracy = (double)num_correct_in_class / num_samples_in_class;
        average_accuracy += class_accuracy;
        printf("Class %d Accuracy: %.2f\n", i, class_accuracy);
    }
    average_accuracy /= MAX_CLASSES;
    printf("Average Accuracy: %.2f\n", average_accuracy);
}

int main() {
    FILE *fp = fopen("iris.data", "r");
    char line[1024];
    int num_samples = 0;
    while (fgets(line, 1024, fp)) {
        char *token = strtok(line, ",");
        for (int i = 0; i < MAX_FEATURES; i++) {
            training_set[num_samples].features[i] = atof(token);
            token = strtok(NULL, ",");
        }
        if (strcmp(token, "Iris-setosa\n") == 0) {
            training_set[num_samples].label = 0;
        } else if (strcmp(token, "Iris-versicolor\n") == 0) {
            training_set[num_samples].label = 1;
        } else {
            training_set[num_samples].label = 2;
        }
        num_samples++;
    }
    fclose(fp);
    num_training_samples = num_samples / 2;
    num_testing_samples = num_samples - num_training_samples;
    memcpy(testing_set, &training_set[num_training_samples], num_testing_samples * sizeof(sample));
    train_decision_tree(0, training_set, num_training_samples);
    test_decision_tree();
    return 0;
}

该代码使用的数据集为iris数据集，其中训练集和测试集的划分比例为1:1。程序首先读取数据集，然后训练决策树，并使用测试集评估决策树的性能。程序输出的评价指标包括整体精度OA和类别平均精度AA。