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基于汉明距离进行聚类

李傲

发布于：2021年8月24日
次浏览

vector版本

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#include <cmath>
#include <ctime>
#include <iostream>
#include <stdlib.h>
#include <string.h>
#include <string>
#include <vector>
using namespace std;
//建立簇类
class Cluster
{
public:
    vector<string> center;
    vector<string> samples;
};
//用映射的方式方便取均值
char map1(char c)
{
    if (c == 'A')
    {
        c = 1;
    }
    if (c == 'C')
    {
        c = 2;
    }
    if (c == 'G')
    {
        c = 3;
    }
    if (c == 'T')
    {
        c = 4;
    }
    if (c == 'N')
    {
        c = 5;
    }
    return c;
}
char map2(char c)
{
    if (c == 1)
    {
        c = 'A';
    }
    if (c == 2)
    {
        c = 'C';
    }
    if (c == 3)
    {
        c = 'G';
    }
    if (c == 4)
    {
        c = 'T';
    }
    if (c == 5)
    {
        c = 'N';
    }
    return c;
}
//返回汉明距离
int ham_distance(char *a, char *b)
{
    int dist = 0;
    while (*a && *b)
    {
        dist += (*a != *b) ? 1 : 0; //异或
        *a++;
        *b++;
    }
    return dist;
}
//聚类函数
vector<Cluster> k_means(vector<string> base, int k, int maxepoches)
{
    int row_num = base.size();
    int col_num = base.front().length();
    vector<Cluster> clusters(k);
    //初始化聚类中心
    int seed = (int)time(NULL);
    for (int i = 0; i < k; i++)
    {
        srand(seed);
        int c = rand() % row_num;
        clusters[i].center.push_back(base[c]);
        seed = rand();
    }
    //开始迭代
    for (int epoch = 0; epoch < maxepoches; epoch++)
    {
        //初始化
        for (int i = 0; i < k; i++)
        {
            clusters[i].samples.clear();
        }
        for (int j = 0; j < row_num; j++)
        {
            //转换成*char类型
            int c = 0;
            const char *t1 = base[j].c_str();
            const char *t2 = clusters[c].center[0].c_str();
            char *temp1 = new char[col_num];
            strcpy(temp1, t1);
            char *temp2 = new char[col_num];
            strcpy(temp2, t2);
            int min_distance = ham_distance(temp1, temp2);
            //更新簇的sample
            for (int i = 1; i < k; i++)
            {
                const char *t1 = base[j].c_str();
                const char *t2 = clusters[i].center[0].c_str();
                char *temp1 = new char[col_num + 1];
                strcpy(temp1, t1);
                char *temp2 = new char[col_num + 1];
                strcpy(temp2, t2);
                int distance = ham_distance(temp1, temp2);
                if (distance < min_distance)
                {
                    min_distance = distance;
                    c = i;
                }
            }
            clusters[c].samples.push_back(base[j]);
        }
        //更新簇的center
        for (int i = 0; i < k; i++)
        {
            vector<double> val(col_num);
            char *center = new char[col_num+1];
            for (int j = 0; j < clusters[i].samples.size(); j++)
            {
                const char *sample = clusters[i].samples[j].c_str();
                char *temp = new char[col_num + 1];
                strcpy(temp, sample);
                for (int d = 0; d < col_num; d++)
                {
                    val[d] += map1(temp[d]);
                    if (j == clusters[i].samples.size() - 1)
                    {
                        center[d] = map2(val[d]/clusters[i].samples.size());
                    }
                }
            }
            clusters[i].center[0] = center;
            clusters[i].center[0].erase(col_num);
        }
    }
    return clusters;
}
int main()
{
    vector<string> base;
    base.push_back("ANNTGTAATAGNCNGAGCTGGCTATTCAGTACCANNAGTGTTGCCGNAGTGACTCCANCTTCAATGTCGTNGCGTATAGTGAGTTTATCCTATAACGGAC");
    base.push_back("TTGCNATACCANGGCTTTGTGATGCAGTAATAAGCAGNGCGTCATGANGTGCTTNCCTCGTCTNGCGTGCAAATTNCGTACTTTAGANTTGCGCATTACG");
    base.push_back("CTACCNTGGCTTGTCCNCTGGNGAAACGNGTAGCTNCGTACCGACTTCAGTATGTATGAATCTAACACTGCNATGCAATCAACCATGGNTCGTNAGNAAA");
    base.push_back("AATAGGTGGTCTCNTCTTCAGTGACCTCATATNCGAANCTACGGTCTCATGANGTGCTTNCCTCGTCTNGCGTGCAAATTNCGTACTTTAGANTTGCGCA");
    base.push_back("TTACGCTACCNTGGCTTGTCCNCTGGNGAAACGNGTAGCTNCGTACCGACTTCAGTATGTATGAATCTAACACTGCNATGCAATCAACCATGGNTCGTNA");
    base.push_back("GTATGCTTCNATACAGCAGNCAAATAGGTGGTCTCNTCTTCAGTGACCTCATATNCGAANCTACGGTCTCATGANGTGCTTNCCTCGTCTNGCGTGCAAA");
    base.push_back("GTATGCTTCNATACAGCAGNCAAATAGGTGGTCTCNTCTTCAGTGACCTCATATNCGAANCTACGGTCTCATGANGTGCTTNCCTCGTCTNGCGTGCAAA");
    base.push_back("GTATGCTTCNATACAGCAGNCAAATAGGTGGTCTCNTCTTCAGTGACCTCATATNCGAANCTACGGTCTCATGANGTGCTTNCCTCGTCTNGCGTGCAAA");
    base.push_back("ACCNTGGCTTGTCCNCTGGNGAAACGNGTAGCTNCGTACCGACTTCAGTATGTATGAATCTAACACTGCNATGCAATCAACCATGGNTCGTNAGNAAAGC");
    base.push_back("CGGTCTCATGANGTGCTTNCCTCGTCTNGCGTGCAAATTNCGTACTTTAGANTTGCGCATTACGCTACCNTGGCTTGTCCNCTGGNGAAACGNGTAGCTN");
    //调用主函数
    vector<Cluster> clusters_out = k_means(base, 4, 100);

    //输出分类结果
    for (int i = 0; i < clusters_out.size(); i++)
    {
        cout << "Cluster " << i << " :" << endl;

        //簇中心
        cout << "\t"
             << "center: "
             << "\n\t\t[ ";
        for (int j = 0; j < clusters_out[i].center.size(); j++)
        {
            cout << clusters_out[i].center[j] << " ";
        }
        cout << "]" << endl;

        //簇样本点
        cout << "\t"
             << "Samples:\n";
        for (int k = 0; k < clusters_out[i].samples.size(); k++)
        {
            cout << "\t\t[ ";
            cout << clusters_out[i].samples[k] << " ";
            cout << "]\n";
        }
    }
    return 0;
}

list版本

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#include <cmath>
#include <ctime>
#include <iostream>
#include <stdlib.h>
#include <string.h>
#include <string>
#include <list>
using namespace std;
//建立簇类
class Cluster
{
    public:
        list<string> center;
        list<string> samples;
};
//用映射的方式方便取均值
char map1(char c)
{
    if (c == 'A')
    {
        c = 1;
    }
    if (c == 'C')
    {
        c = 2;
    }
    if (c == 'G')
    {
        c = 3;
    }
    if (c == 'T')
    {
        c = 4;
    }
    if (c == 'N')
    {
        c = 5;
    }
    return c;
}
char map2(char c)
{
    if (c == 1)
    {
        c = 'A';
    }
    if (c == 2)
    {
        c = 'C';
    }
    if (c == 3)
    {
        c = 'G';
    }
    if (c == 4)
    {
        c = 'T';
    }
    if (c == 5)
    {
        c = 'N';
    }
    return c;
}
//返回汉明距离
int ham_distance(char *a, char *b)
{
    int dist = 0;
    while (*a && *b)
    {
        dist += (*a != *b) ? 1 : 0; //异或
        *a++;
        *b++;
    }
    return dist;
}
//聚类函数
list<Cluster> k_means(list<string> base, int k, int maxepoches)
{
    int row_num = base.size();
    int col_num = base.front().length();
    list<Cluster> clusters(k);
    //初始化聚类中心
    int seed = (int)time(NULL);
    for (list<Cluster>::iterator it = clusters.begin(); it!=clusters.end(); it++)
    {
        srand(seed);
        int c = rand() % row_num;
        list<string>::iterator base_it = base.begin();
        advance(base_it,c);
        (*it).center.push_back((*base_it));
        seed = rand();
    }
    //开始迭代
    for (int epoch = 0; epoch < maxepoches; epoch++)
    {
        //初始化
        for (list<Cluster>::iterator it = clusters.begin(); it!=clusters.end(); it++)
        {
            (*it).samples.clear();
        }
        for (list<string>::iterator base_it = base.begin(); base_it!=base.end(); base_it++)
        {
            //转换成*char类型
            list<Cluster>::iterator it = clusters.begin();
            const char *t1 = (*base_it).c_str();
            const char *t2 = (*it).center.front().c_str();
            char *temp1 = new char[col_num+1];
            strcpy(temp1, t1);
            char *temp2 = new char[col_num+1];
            strcpy(temp2, t2);
            int min_distance = ham_distance(temp1, temp2);
            delete[] temp1;
            delete[] temp2;
            //更新簇的sample
            int c = 0, i = 0;
            for (;it != clusters.end(),i<4;it++,i++)
            {
                const char *t1 = (*base_it).c_str();
                const char *t2 =  (*it).center.front().c_str();
                char *temp1 = new char[col_num + 1];
                strcpy(temp1, t1);
                char *temp2 = new char[col_num + 1];
                strcpy(temp2, t2);
                int distance = ham_distance(temp1, temp2);
                delete[] temp1;
                delete[] temp2;
                if (distance < min_distance)
                {
                    min_distance = distance;
                    c = i;
                }
            }
            it = clusters.begin();
            advance(it, c);
            (*it).samples.push_back(*base_it);
        }
        //更新簇的center
        for (list<Cluster>::iterator it = clusters.begin(); it!=clusters.end(); it++)
        {
            list<double> val(col_num);
            char *center = new char[col_num+1];
            for (list<string>::iterator sam_it = (*it).samples.begin(); sam_it!=(*it).samples.end(); sam_it++)
            {
                const char *sample = (*sam_it).c_str();
                char *temp = new char[col_num + 1];
                strcpy(temp, sample);
                int d = 0;
                for (list<double>::iterator val_it = val.begin(); val_it != val.end();val_it++)
                {
                    (*val_it) += map1(temp[d]);
                    if ((*sam_it)==(*it).samples.back())
                    {
                        center[d] = map2((*val_it)/(*it).samples.size());
                    }
                    d++;
                }
                delete[] temp;
            }
            (*it).center.front() = center;
            (*it).center.front().erase(col_num);
            delete[] center;
        }
    }
    return clusters;
}
int main()
{
    list<string> base;
   base.push_back("ANNTGTAATAGNCNGAGCTGGCTATTCAGTACCANNAGTGTTGCCGNAGTGACTCCANCTTCAATGTCGTNGCGTATAGTGAGTTTATCCTATAACGGAC");
    base.push_back("TTGCNATACCANGGCTTTGTGATGCAGTAATAAGCAGNGCGTCATGANGTGCTTNCCTCGTCTNGCGTGCAAATTNCGTACTTTAGANTTGCGCATTACG");
    base.push_back("CTACCNTGGCTTGTCCNCTGGNGAAACGNGTAGCTNCGTACCGACTTCAGTATGTATGAATCTAACACTGCNATGCAATCAACCATGGNTCGTNAGNAAA");
    base.push_back("AATAGGTGGTCTCNTCTTCAGTGACCTCATATNCGAANCTACGGTCTCATGANGTGCTTNCCTCGTCTNGCGTGCAAATTNCGTACTTTAGANTTGCGCA");
    base.push_back("TTACGCTACCNTGGCTTGTCCNCTGGNGAAACGNGTAGCTNCGTACCGACTTCAGTATGTATGAATCTAACACTGCNATGCAATCAACCATGGNTCGTNA");
    base.push_back("GTATGCTTCNATACAGCAGNCAAATAGGTGGTCTCNTCTTCAGTGACCTCATATNCGAANCTACGGTCTCATGANGTGCTTNCCTCGTCTNGCGTGCAAA");
    base.push_back("GTATGCTTCNATACAGCAGNCAAATAGGTGGTCTCNTCTTCAGTGACCTCATATNCGAANCTACGGTCTCATGANGTGCTTNCCTCGTCTNGCGTGCAAA");
    base.push_back("GTATGCTTCNATACAGCAGNCAAATAGGTGGTCTCNTCTTCAGTGACCTCATATNCGAANCTACGGTCTCATGANGTGCTTNCCTCGTCTNGCGTGCAAA");
    base.push_back("ACCNTGGCTTGTCCNCTGGNGAAACGNGTAGCTNCGTACCGACTTCAGTATGTATGAATCTAACACTGCNATGCAATCAACCATGGNTCGTNAGNAAAGC");
    base.push_back("CGGTCTCATGANGTGCTTNCCTCGTCTNGCGTGCAAATTNCGTACTTTAGANTTGCGCATTACGCTACCNTGGCTTGTCCNCTGGNGAAACGNGTAGCTN");
    //调用主函数
    list<Cluster> clusters_out = k_means(base, 4, 10);
    //输出分类结果
    int i = 0;
    for (list<Cluster>::iterator it = clusters_out.begin(); it != clusters_out.end();it++)
    {
        cout << "Cluster " << i << " :" << endl;

        //簇中心
        cout << "\t"
             << "center: "
             << "\n\t\t[ ";
        cout << (*it).center.front() << " ";
        cout << "]" << endl;

        //簇样本点
        cout << "\t"
             << "Samples:\n";
        for (list<string>::iterator sam_it = (*it).samples.begin(); sam_it != (*it).samples.end();sam_it++)
        {
            cout << "\t\t[ ";
            cout << *sam_it << " ";
            cout << "]\n";
        }
        i++;
    }
    return 0; 
}

博客内容遵循 署名-非商业性使用-相同方式共享 4.0 国际 (CC BY-NC-SA 4.0) 协议

更新于：2021年8月26日

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