用C语言实现随机森林算法

随机森林是一种集成学习的方法，其本质是基于多个决策树的集成算法。在随机森林中，每个决策树都是由随机选取的样本和随机选取的特征组成。以下是用C语言实现随机森林算法的基本步骤：

1. 导入数据集：将数据集读入到程序中。

2. 随机选取样本和特征：从数据集中随机选取一部分样本和特征，用于构建每个决策树。

3. 构建决策树：根据选取的样本和特征，构建一棵决策树。

4. 重复步骤2-3：重复步骤2-3，构建多棵决策树。

5. 预测：对于新的样本，将其输入所有构建的决策树中，最终的预测结果为所有决策树的预测结果的平均值或多数表决。

以下是一个简单的用C语言实现随机森林算法的例子：

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

#define MAX_FEATURES 20
#define MAX_SAMPLES 1000
#define MAX_TREES 100

typedef struct {
    double feature[MAX_FEATURES];
    int label;
} Sample;

typedef struct {
    int feature_index;
    double threshold;
} Split;

typedef struct Node {
    Split split;
    struct Node* left_child;
    struct Node* right_child;
    int label;
} Node;

typedef struct {
    Node* root;
} Tree;

typedef struct {
    Tree trees[MAX_TREES];
    int num_trees;
} RandomForest;

Sample samples[MAX_SAMPLES];
int num_samples;
int num_features;
int num_classes;

int get_majority_class(Sample* samples, int num_samples) {
    int class_counts[num_classes];
    for (int i = 0; i < num_classes; i++) {
        class_counts[i] = 0;
    }
    for (int i = 0; i < num_samples; i++) {
        class_counts[samples[i].label]++;
    }
    int majority_class = 0;
    int max_count = 0;
    for (int i = 0; i < num_classes; i++) {
        if (class_counts[i] > max_count) {
            majority_class = i;
            max_count = class_counts[i];
        }
    }
    return majority_class;
}

double get_gini_index(Sample* samples, int num_samples) {
    int class_counts[num_classes];
    for (int i = 0; i < num_classes; i++) {
        class_counts[i] = 0;
    }
    for (int i = 0; i < num_samples; i++) {
        class_counts[samples[i].label]++;
    }
    double gini_index = 1.0;
    for (int i = 0; i < num_classes; i++) {
        double p_i = (double) class_counts[i] / num_samples;
        gini_index -= p_i * p_i;
    }
    return gini_index;
}

Split get_best_split(Sample* samples, int num_samples, int* selected_features, int num_selected_features) {
    double min_gini_index = INFINITY;
    Split best_split;
    for (int i = 0; i < num_selected_features; i++) {
        int feature_index = selected_features[i];
        double feature_values[num_samples];
        for (int j = 0; j < num_samples; j++) {
            feature_values[j] = samples[j].feature[feature_index];
        }
        for (int j = 0; j < num_samples - 1; j++) {
            for (int k = j + 1; k < num_samples; k++) {
                if (feature_values[j] > feature_values[k]) {
                    double temp = feature_values[j];
                    feature_values[j] = feature_values[k];
                    feature_values[k] = temp;
                }
            }
        }
        for (int j = 0; j < num_samples - 1; j++) {
            double threshold = (feature_values[j] + feature_values[j + 1]) / 2.0;
            Sample left_samples[num_samples];
            Sample right_samples[num_samples];
            int num_left_samples = 0;
            int num_right_samples = 0;
            for (int k = 0; k < num_samples; k++) {
                if (samples[k].feature[feature_index] <= threshold) {
                    left_samples[num_left_samples++] = samples[k];
                } else {
                    right_samples[num_right_samples++] = samples[k];
                }
            }
            double gini_index = ((double) num_left_samples / num_samples) * get_gini_index(left_samples, num_left_samples) +
                                ((double) num_right_samples / num_samples) * get_gini_index(right_samples, num_right_samples);
            if (gini_index < min_gini_index) {
                min_gini_index = gini_index;
                best_split.feature_index = feature_index;
                best_split.threshold = threshold;
            }
        }
    }
    return best_split;
}

Node* build_decision_tree(Sample* samples, int num_samples, int* selected_features, int num_selected_features) {
    Node* node = (Node*) malloc(sizeof(Node));
    if (num_samples == 0) {
        node->label = get_majority_class(samples, num_samples);
        return node;
    }
    int num_classes_in_node = 0;
    int classes_in_node[num_classes];
    for (int i = 0; i < num_classes; i++) {
        classes_in_node[i] = 0;
    }
    for (int i = 0; i < num_samples; i++) {
        if (classes_in_node[samples[i].label] == 0) {
            num_classes_in_node++;
        }
        classes_in_node[samples[i].label]++;
    }
    if (num_classes_in_node == 1) {
        node->label = samples[0].label;
        return node;
    }
    if (num_selected_features == 0) {
        node->label = get_majority_class(samples, num_samples);
        return node;
    }
    Split split = get_best_split(samples, num_samples, selected_features, num_selected_features);
    node->split = split;
    Sample left_samples[num_samples];
    Sample right_samples[num_samples];
    int num_left_samples = 0;
    int num_right_samples = 0;
    for (int i = 0; i < num_samples; i++) {
        if (samples[i].feature[split.feature_index] <= split.threshold) {
            left_samples[num_left_samples++] = samples[i];
        } else {
            right_samples[num_right_samples++] = samples[i];
        }
    }
    int new_selected_features[num_selected_features - 1];
    int new_num_selected_features = 0;
    for (int i = 0; i < num_selected_features; i++) {
        if (selected_features[i] != split.feature_index) {
            new_selected_features[new_num_selected_features++] = selected_features[i];
        }
    }
    node->left_child = build_decision_tree(left_samples, num_left_samples, new_selected_features, new_num_selected_features);
    node->right_child = build_decision_tree(right_samples, num_right_samples, new_selected_features, new_num_selected_features);
    return node;
}

void train_random_forest(RandomForest* random_forest, int num_trees, int num_selected_features) {
    int selected_features[num_features];
    for (int i = 0; i < num_features; i++) {
        selected_features[i] = i;
    }
    for (int i = 0; i < num_trees; i++) {
        int selected_samples[num_samples];
        for (int j = 0; j < num_samples; j++) {
            selected_samples[j] = rand() % num_samples;
        }
        Sample selected_samples_array[num_samples];
        for (int j = 0; j < num_samples; j++) {
            selected_samples_array[j] = samples[selected_samples[j]];
        }
        int selected_features_array[num_features];
        for (int j = 0; j < num_features; j++) {
            selected_features_array[j] = selected_features[rand() % num_features];
        }
        random_forest->trees[i].root = build_decision_tree(selected_samples_array, num_samples, selected_features_array, num_selected_features);
    }
    random_forest->num_trees = num_trees;
}

int predict_random_forest(RandomForest* random_forest, Sample sample) {
    int class_counts[num_classes];
    for (int i = 0; i < num_classes; i++) {
        class_counts[i] = 0;
    }
    for (int i = 0; i < random_forest->num_trees; i++) {
        Node* node = random_forest->trees[i].root;
        while (node->left_child != NULL && node->right_child != NULL) {
            if (sample.feature[node->split.feature_index] <= node->split.threshold) {
                node = node->left_child;
            } else {
                node = node->right_child;
            }
        }
        class_counts[node->label]++;
    }
    int majority_class = 0;
    int max_count = 0;
    for (int i = 0; i < num_classes; i++) {
        if (class_counts[i] > max_count) {
            majority_class = i;
            max_count = class_counts[i];
        }
    }
    return majority_class;
}

int main() {
    // 读入数据集
    // ...

    // 设置参数
    num_features = 10;
    num_classes = 2;
    num_samples = 100;

    // 训练随机森林
    int num_trees = 50;
    int num_selected_features = 3;
    RandomForest random_forest;
    train_random_forest(&random_forest, num_trees, num_selected_features);

    // 预测
    Sample sample;
    // ...
    int predicted_class = predict_random_forest(&random_forest, sample);

    return 0;
}

以上代码仅为示例，实际应用中还需要进行参数调优、交叉验证等操作，以提高模型的性能。