C语言文本分类算法的代码实现

C语言文本分类算法的实现主要分为以下几个步骤：

1. 数据预处理：将文本数据转换成数字，也就是将每个单词转换成一个数字。这可以通过词袋模型和TF-IDF算法来实现。

2. 特征提取：对文本数据进行特征提取，常用的特征包括词频、TF-IDF值、主题模型等。

3. 分类器训练：选择合适的分类器进行训练，常用的分类器包括朴素贝叶斯、支持向量机、决策树等。

4. 测试模型：使用测试集对训练好的模型进行测试，评估模型的性能。

下面是一个简单的C语言文本分类算法实现代码：

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

#define MAX_WORDS 10000 // 最大单词数
#define MAX_DOCS 1000 // 最大文档数

// 单词结构体
typedef struct _word{
    char *text; // 单词内容
    int count; // 单词出现次数
    double idf; // 单词IDF值
}Word;

// 文档结构体
typedef struct _doc{
    char *text; // 文档内容
    Word **words; // 文档包含的单词数组
    int *counts; // 文档包含的单词出现次数数组
    int num_words; // 文档包含的单词数
    int label; // 文档类别
}Doc;

// 数据集结构体
typedef struct _dataset{
    Doc **docs; // 文档数组
    int num_docs; // 文档数
    Word **words; // 单词数组
    int num_words; // 单词数
}Dataset;

// 读取文本文件
char *read_file(char *filename){
    FILE *fp = fopen(filename, "r");
    if(fp == NULL){
        printf("File not found!\n");
        return NULL;
    }

    fseek(fp, 0, SEEK_END);
    long size = ftell(fp);
    char *text = (char *)malloc(size + 1);
    fseek(fp, 0, SEEK_SET);
    fread(text, size, 1, fp);
    fclose(fp);

    text[size] = '\0';
    return text;
}

// 分割文本为单词数组
Word **split_text(char *text, int *num_words){
    Word **words = (Word **)malloc(sizeof(Word *) * MAX_WORDS);
    char *token = strtok(text, " \n\t\r");
    int count = 0;

    while(token != NULL){
        int found = 0;
        for(int i = 0; i < count; i++){
            if(strcmp(words[i]->text, token) == 0){
                words[i]->count++;
                found = 1;
                break;
            }
        }

        if(!found){
            Word *w = (Word *)malloc(sizeof(Word));
            w->text = token;
            w->count = 1;
            words[count++] = w;
        }

        token = strtok(NULL, " \n\t\r");
    }

    *num_words = count;
    return words;
}

// 计算单词IDF值
void calc_idf(Dataset *data){
    for(int i = 0; i < data->num_words; i++){
        int num_docs = 0;
        for(int j = 0; j < data->num_docs; j++){
            for(int k = 0; k < data->docs[j]->num_words; k++){
                if(strcmp(data->docs[j]->words[k]->text, data->words[i]->text) == 0){
                    num_docs++;
                    break;
                }
            }
        }
        data->words[i]->idf = log((double)data->num_docs / (double)num_docs);
    }
}

// 数据集预处理
void preprocess(Dataset *data, char **filenames, int num_files){
    for(int i = 0; i < num_files; i++){
        char *text = read_file(filenames[i]);
        Word **words = split_text(text, &(data->docs[i]->num_words));
        data->docs[i]->text = text;
        data->docs[i]->words = words;
    }

    for(int i = 0; i < data->num_docs; i++){
        int *counts = (int *)calloc(data->num_words, sizeof(int));
        for(int j = 0; j < data->docs[i]->num_words; j++){
            for(int k = 0; k < data->num_words; k++){
                if(strcmp(data->docs[i]->words[j]->text, data->words[k]->text) == 0){
                    counts[k] = data->docs[i]->words[j]->count;
                    break;
                }
            }
        }
        data->docs[i]->counts = counts;
    }

    calc_idf(data);
}

// 训练朴素贝叶斯分类器
void train_naive_bayes(Dataset *data, double *priors, double **likelihoods){
    for(int i = 0; i < data->num_docs; i++){
        priors[data->docs[i]->label]++;
        for(int j = 0; j < data->num_words; j++){
            likelihoods[data->docs[i]->label][j] += data->docs[i]->counts[j] * data->words[j]->idf;
        }
    }

    for(int i = 0; i < 2; i++){
        double total = 0.0;
        for(int j = 0; j < data->num_words; j++){
            total += likelihoods[i][j];
        }
        for(int j = 0; j < data->num_words; j++){
            likelihoods[i][j] /= total;
        }
        priors[i] /= (double)data->num_docs;
    }
}

// 预测文档类别
int predict(Dataset *data, double *priors, double **likelihoods, char *filename){
    char *text = read_file(filename);
    Word **words = split_text(text, &(data->docs[data->num_docs]->num_words));
    Doc *doc = (Doc *)malloc(sizeof(Doc));
    doc->text = text;
    doc->words = words;

    int *counts = (int *)calloc(data->num_words, sizeof(int));
    for(int i = 0; i < doc->num_words; i++){
        for(int j = 0; j < data->num_words; j++){
            if(strcmp(doc->words[i]->text, data->words[j]->text) == 0){
                counts[j] = doc->words[i]->count;
                break;
            }
        }
    }
    doc->counts = counts;

    double scores[2] = {0.0};
    for(int i = 0; i < 2; i++){
        for(int j = 0; j < data->num_words; j++){
            scores[i] += likelihoods[i][j] * doc->counts[j] * data->words[j]->idf;
        }
        scores[i] += log(priors[i]);
    }

    int label = (scores[0] > scores[1]) ? 0 : 1;
    return label;
}

// 主函数
int main(){
    char *filenames[MAX_DOCS] = {"doc1.txt", "doc2.txt", "doc3.txt", "doc4.txt"};
    Dataset *data = (Dataset *)malloc(sizeof(Dataset));
    data->num_docs = 4;
    data->num_words = 0;
    data->docs = (Doc **)malloc(sizeof(Doc *) * data->num_docs);

    for(int i = 0; i < data->num_docs; i++){
        data->docs[i] = (Doc *)malloc(sizeof(Doc));
        data->docs[i]->label = i % 2;
    }

    char *text = read_file(filenames[0]);
    Word **words = split_text(text, &(data->num_words));
    data->words = words;

    preprocess(data, filenames, data->num_docs);

    double priors[2] = {0.0};
    double **likelihoods = (double **)malloc(sizeof(double *) * 2);
    for(int i = 0; i < 2; i++){
        likelihoods[i] = (double *)calloc(data->num_words, sizeof(double));
    }

    train_naive_bayes(data, priors, likelihoods);

    for(int i = 0; i < data->num_docs; i++){
        int label = predict(data, priors, likelihoods, filenames[i]);
        printf("Document %d is classified as %d\n", i+1, label);
    }

    return 0;
}

上述代码实现了一个简单的朴素贝叶斯分类器，使用TF-IDF作为特征，并且只能分类两个类别。完整的文本分类算法还需要考虑更多的特征和更复杂的分类器。