[机器学习]三角不等式加速K均值聚类及C++实现
本博客涉及代码可在GitHub下载:传送门
K均值聚类
K均值聚类是常用的欧式距离聚类算法，即认为两个目标向量的差的模长越小，两个目标越可能是一类的。
通俗理解：牧师-村民模型
有四个牧师去郊区布道，一开始牧师们随意选了几个布道点，并且把这几个布道点的情况公告给了郊区所有的村民，于是每个村民到离自己家最近的布道点去听课。听课之后，大家觉得距离太远了，于是每个牧师统计了一下自己的课上所有的村民的地址，搬到了所有地址的中心地带，并且在海报上更新了自己的布道点的位置。牧师每一次移动不可能离所有人都更近，有的人发现A牧师移动以后自己还不如去B牧师处听课更近，于是每个村民又去了离自己最近的布道点……就这样，牧师每个礼拜更新自己的位置，村民根据自己的情况选择布道点，最终稳定了下来。
可以发现该牧师的目的是为了让每个村民到其最近中心点的距离和最小。
数学模型
收敛性证明
三角不等式加速
定理1及优化
定理1：x是一个数据点，b和c是中点，如果 \(|bc|\geq 2|xb|\) 则有 \(|xc|\geq |xb|\)
使用三角不等式（两边之和大于第三边）即可证明。有了这条性质，我们如此考虑，若数据点x此时锚定了中点b，且我们知道 \(|xb|\) 或 \(|xb|\) 的一个上界，而 \(|bc|\geq 2|xb|\)，那么我们不用考虑x点会从b点转移到c点去，节约了计算 \(|xc|\) 的时间。
定理2及优化
定理2：x是一个数据点，b和c是中点，有 \(|xc|\geq max\{0,|xb|-|bc|\}\)。
使用三角不等式（两边之和大于第三边）即可证明。
对于锚定了中点b的数据点x，考虑是否转移到另一个中点c，若我们知道 \(|xb|\) 的上界和 \(|xc|\) 的下界，且上界小于这个下界，那一定不考虑。在算法开始的时候上界和下界可以直接赋值为距离，在后续的迭代中，需要保证上下界的合法。
我们不维护每一对点的距离的上界，只维护一个数据点到它的锚定点的距离的上界u(x)。一开始数据点到锚定点的距离是确定的，上界也确定，若该点的锚定点发生了位移，根据定理1则 u(x)+=dis(m(c(x)),c(x))，m(c)表示c位移后的位置（代码中为mean），c(x)表示x数据点点锚定的中点。同时当我们计算x到它锚定点的距离的时候，我们顺手更新一下这个上界为x到它当前的锚定点的距离，让它不会一直增大以至于算法后期失去约束能力。同时，我们可以记录一下这个点它的上界是否仍然是c到x的距离，如果是的话，我们又能省去一次计算距离。
我们维护每一个数据点x到中点c的距离的下界l(x,c)，一开始赋值为距离，迭代的时候，根据定理2，l(x,c)=max{l(x,c)-dis(c,m(c)),0}。
伪代码
C++实现思路
KMean
KMeans(vector<vector<double>> dataset, vector<int> label, int cluster_number)
构造函数接口涉及为二维动态数组传数据点，一维数据传标签，最后一个int传聚类数量（k的值）。vector<int> result;算法结果int iterator_times;迭代次数class Point;class Data_point;class Center;double cal_dis(Point &a, Point &b)void cal_disc(){Calculate distance between centers and s(c)void init()Initializationbool repeat()Repeat stepsvoid getResult()Get result and save toresultvoid mainThread()
Point
某种意义上是个抽象类
vector<double> pos;点的坐标void build(vector<double> arr)把数据填进pos
Data_point: 继承Point
int center;锚定的中点vector<double> lowerBound;到所有中点的下界double upperBound;到锚定点的上界bool uOutofDate, transed;上界是否是距离，本轮迭代是否转移
Center: 继承Point
vector<double> mean;均值向量int cnt;有几个附属点vector<double> disc;到其它c点的距离double s到其他c点的距离的下界的 \(\frac{1}{2}\)void trans()转移到均值点void add_mean(Data_point &data)把一个数据点加进mean里void cal_mean()所有数据点加进后求均值
代码
头文件
#include <cmath> #include <cstdio> #include <ctime> #include <iostream> #include <vector> using namespace std; KMeans
class KMeans{ class Point; class Data_point; class Center; public: vector<int> result; int iterator_times; KMeans(vector<vector<double>> dataset, vector<int> label, int cluster_number){ data_size = dataset.size(); dimension = dataset[0].size(); cluster = cluster_number; for (auto it = dataset.begin(); it != dataset.end();it++){ vector<double> pos; for (int i = 0; i < dimension;i++) pos.push_back((*it)[i]); point.push_back(Data_point(pos)); } for (auto it = label.begin(); it != label.end();it++) this->label.push_back(*it); //output(); iterator_times = 0; mainThread(); } private: int data_size, dimension, cluster; vector<Data_point> point; vector<Center> center; vector<int> label; point
class Point{ public: vector<double> pos; void build(vector<double> arr){ pos.clear(); for (auto it = arr.begin(); it != arr.end();it++) pos.push_back(*it); } void build(Point &rhs){ pos.clear(); for (auto it = rhs.pos.begin(); it != rhs.pos.end();it++) pos.push_back(*it); } void output(){ cout << point data:; for (auto it = pos.begin(); it != pos.end();it++) cout << (*it) << ; cout << endl; } private: void clear(){ pos.clear(); } }; Data_point
class Data_point : public Point{ public: int center; vector<double> lowerBound; double upperBound; bool uOutofDate, transed; Data_point(vector<double> arr){ this->build(arr); center = 0; uOutofDate = transed = false; } }; Center
class Center : public Point{ public: vector<double> mean; int cnt; vector<double> disc; double s;//s(c)=1/2 min(c,c')(c!=c') Center(Data_point &rhs){ this->build(rhs); cnt = 0; } void trans(){ int len = mean.size(); for (int i = 0; i < len;i++){ pos[i]=mean[i]; mean[i] = 0; } cnt = 0; } void add_mean(Data_point &data){ int len = mean.size(); for (int i = 0; i < len;i++) mean[i] += data.pos[i]; cnt++; } void cal_mean(){ int len = mean.size(); for (int i = 0; i < len;i++) mean[i] /= cnt; } void output(){ int len = pos.size(); cout << center position:; for (int i = 0; i < pos.size();i++) cout << pos[i] << ; cout << endl; } }; KMeans内成员函数
double cal_dis(Point &a, Point &b){ double dis = 0; for (int i = 1; i <= this->dimension; i++){ dis += (a.pos[i] - b.pos[i]) * (a.pos[i] - b.pos[i]); } return sqrt(dis); } double cal_dis(Point &a, vector<double> &b){ double dis = 0; for (int i = 1; i <= this->dimension; i++){ dis += (a.pos[i] - b[i]) * (a.pos[i] - b[i]); } return sqrt(dis); } void output(){ cout << Data size: << data_size << endl; cout << Dimension: << dimension << endl; cout << Data: << endl; // for (auto it = point.begin(); it != point.end();it++) // it->output(); } //Calculate distance between centers and s(c) void cal_disc(){ for (int c1 = 0; c1 < cluster;c1++) for (int c2 = c1+1; c2 < cluster;c2++){ double dis = cal_dis(center[c1], center[c2]); center[c1].disc[c2] = dis; center[c2].disc[c1] = dis; } for (int c1 = 0; c1 < cluster;c1++){ double s = center[c1].disc[(c1 + 1) % cluster]; for (int c2 = 0; c2 < cluster;c2++){ if(c1==c2) continue; double s2 = center[c1].disc[c2]; if(s2<s) s = s2; } center[c1].s = s / 2; } } void init(){ //get initial centers vector<int> num; for (int i = 0; i < data_size;i++) num.push_back(i); srand(time(NULL)); for (int i = 0; i < data_size; i++) swap(num[i],num[rand() % data_size]); for (int i = 0; i < cluster; i++) center.push_back(Center(point[num[i]])); num.clear(); //Initial the center for (auto c = center.begin(); c != center.end();c++){ for (int i = 0; i < cluster;i++) c->disc.push_back(0); for (int i = 0; i < dimension;i++) c->mean.push_back(0); } cal_disc(); //Calculate initial c(x) and l(x,c) and u(x) for (auto x = point.begin(); x != point.end();x++){ for (int i = 0; i < cluster;i++) x->lowerBound.push_back(0); double dis = cal_dis(*x, center[0]); x->center = 0; int len = center.size(); for (int c = 1; c < len;c++){ if(dis<=center[x->center].disc[c]) continue; double dis2 = cal_dis(*x, center[c]); x->lowerBound[c] = dis2; if(dis2<dis){ x->center = c; dis = dis2; } } x->upperBound = dis; center[x->center].add_mean(*x); } for (auto c = center.begin(); c != center.end();c++) c->cal_mean(); //Move the centers // cout << first time center: << endl; // for (auto c = center.begin(); c != center.end();c++) // c->output(); for (auto c = center.begin(); c != center.end();c++) c->trans(); // cout << second time center: << endl; // for (auto c = center.begin(); c != center.end();c++) // c->output(); } //Repeat steps bool repeat(){ bool flag = false; cal_disc(); for (auto x = point.begin(); x != point.end();x++){ x->transed = false; double disc; //Ignore points which will not trans if(x->upperBound<=center[x->center].s) continue; //Update the upperBound if(x->uOutofDate){ x->uOutofDate=false; disc = cal_dis(*x, center[x->center]); x->upperBound = disc; }else disc = x->upperBound; //trans points to closer centers for (int c = 0; c < cluster;c++){ if(disc>x->lowerBound[x->center]||disc>0.5*center[x->center].disc[c]){ double disc2 = cal_dis(*x, center[c]); //cout << iter note:<<disc<< <<disc2 << endl; if(disc2<disc){ //cout << yes << endl; //cout << trans note: << disc2 << << disc << endl; x->center = c; x->transed = true; flag = true; } } } //Add to the center center[x->center].add_mean(*x); } //Calculate the mean of centers for (auto c = center.begin(); c!=center.end();c++) c->cal_mean(); //Update the lowerBound and upperBound for (auto x = point.begin(); x != point.end();x++){ if(!x->transed) continue; for (int c = 0; c < cluster;c++) x->lowerBound[c] = max(x->lowerBound[c] - cal_dis(center[c], center[c].mean), 0.0); x->upperBound += cal_dis(center[x->center], center[x->center].mean); x->uOutofDate = true; } //Move the centers for (auto c = center.begin(); c != center.end();c++) c->trans(); iterator_times++; return flag; } //Get result and save to `result` void getResult(){ for (auto x = point.begin(); x != point.end();x++) result.push_back((*x).center); } void mainThread(){ init(); while(true){ if(!repeat()) break; } getResult(); } }; 测试代码
int main(){ // Get the data freopen(seeds_dataset.txt, r, stdin); vector<vector<double>> dataset; vector<int> label; double tmp; int tmp2; for (int i = 1; i <= 210; i++){ vector<double> point; for (int j = 1; j <= 7; j++){ cin >> tmp; point.push_back(tmp); } cin >> tmp2; label.push_back(tmp2); dataset.push_back(point); } //KMeans(vector<vector<double>> dataset, vector<int> label, int number of cluster) KMeans model(dataset, label, 3); cout << result: << endl; for (auto it = model.result.begin(); it != model.result.end();it++) cout << (*it) << ; cout << endl; cout << iteration times: << model.iterator_times << endl; }