对于我的任务，我必须对600个数据点应用K-means算法，这些数据点都有60个维度（或属性，如果你愿意的话）。我将600个数据点存储到6个集群中（所以K = 6），这就是我到目前为止所拥有的：
#include <iostream> //std::cout
#include <fstream> //std::ifstream
#include <string> //std::string
#include <sstream> //std::istringstream
#include <vector> //std::vector
#include <cmath> //std::cmath
#include <array> //std::array
#define K 6
#define MAX_ITERATIONS 10000000
#define NUM_ATTRIBUTES 60
struct Point{
std::array<double, NUM_ATTRIBUTES> point;
std::string classType;
};
struct Cluster{
std::vector<Point> points;
Point centroid;
};
int randNumGenerator(int max){
int num = (rand() % max);
return num;
}
void setData(Point p, std::string line, std::vector<Point> &data, int index){
std::stringstream s(line);
std::string classes[] = {"Normal", "Cyclic", "Increasing trend",
"Decreasing trend", "Upward shift", "Downward shift"};
for(int i = 0; i < NUM_ATTRIBUTES; i++){
double num;
if(s >> num){
p.point[i] = num;
}
}
if(index > 0 && index <= 100){
p.classType = classes[0];
}
else if(index > 100 && index <= 200){
p.classType = classes[1];
}
else if(index > 200 && index <= 300){
p.classType = classes[2];
}
else if(index > 300 && index <= 400){
p.classType = classes[3];
}
else if(index > 400 && index <= 500){
p.classType = classes[4];
}
else if(index > 500 && index <= 600){
p.classType = classes[5];
}
data.push_back(p);
}
void initializeCentroids(std::vector<Point> &points, int num_clusters,
std::vector<Point> ¢roids){
Point p;
std::vector<bool> numsUsedAlready(points.size());
for(int i = 0; i < num_clusters; i++){
int randNum = randNumGenerator(points.size());
while(numsUsedAlready[randNum]){
randNum = randNumGenerator(points.size());
}
numsUsedAlready[randNum] = true;
p = points[randNum];
centroids.push_back(p);
}
}
double calculateDistance(Point p, Point centroid){
double ret = 0;
for(int i = 0; i < p.point.size(); i++){
double distance = p.point[i] - centroid.point[i];
ret += distance * distance;
}
return sqrt(ret);
}
void setCentroids(std::vector<Cluster> &clusters, std::vector<Point> centroids){
for(int i = 0; i < centroids.size(); i++){
Cluster c;
c.points.push_back(centroids[i]);
c.centroid = centroids[i];
clusters.push_back(c);
}
}
void setClusters(std::vector<Cluster> &clusters, std::vector<Point> points){
int sendToCluster = 99999999;
for(int index = 0; index < points.size(); index++){
double minDist = 99999999999;
Point p = points[index];
for(int clusterNum = 0; clusterNum < clusters.size(); clusterNum++){
double tempDist = calculateDistance(p, clusters[clusterNum].centroid);
//std::cout << "dist: " << tempDist << " clusterNum: " << clusterNum << std::endl;
if(tempDist < minDist){
minDist = tempDist;
sendToCluster = clusterNum;
}
}
//std::cout << "Pushing to clusterNUm " << sendToCluster << std::endl;
clusters[sendToCluster].points.push_back(p);
}
}
void updateCentroid(std::vector<Cluster> &clusters){
for(int i = 0; i < clusters.size(); i++){
Cluster c = clusters[i];
for(int j = 0; j < NUM_ATTRIBUTES; j++){
double avg = 0;
for(int h = 0; h < c.points.size(); h++){
Point p = c.points[h];
avg += p.point[j];
}
double oldCentroidValue = c.centroid.point[j];
c.centroid.point[j] = avg / c.points.size();
std::cout << "old: " << oldCentroidValue << " new: " << c.centroid.point[j] << std::endl;
}
}
}
void k_clustering(std::vector<Point> &points, int num_clusters){
std::vector<Point> centroids;
initializeCentroids(points, num_clusters, centroids);
std::vector<Cluster> clusters;
setCentroids(clusters, centroids);
/*for(int i = 0; i < clusters.size(); i++){
for(int j = 0; j < NUM_ATTRIBUTES; j++){
std::cout << clusters[i].centroid.point[j] << " ";
}
std::cout << std::endl;
}
*/
setClusters(clusters, points);
updateCentroid(clusters);
for(int i = 0; i < clusters.size(); i++){
std::cout << i << " " << clusters[i].points.size() << std::endl;
}
}
void readInputFile(std::ifstream &file, std::vector<Point> &data){
std::string line;
int counter = 0;
while(getline(file,line)){
counter++;
Point p;
setData(p, line, data, counter);
}
}
void usageString(){
std::cout << "Usage: output <input_file>" << std::endl;
}
char* checkFile(int argc, char *argv[]){
if(argc < 2){
usageString();
exit(0);
}
else{
char *inputfile = argv[1];
return inputfile;
}
}
int main(int argc, char *argv[]){
char *inputfile;
inputfile = checkFile(argc, argv);
std::ifstream input(inputfile);
if(!input.is_open()){
std::cerr << "Error: Data file doesn't exist" << std::endl;
return EXIT_FAILURE;
}
srand(time(NULL));
std::vector<Point> data;
readInputFile(input, data);
k_clustering(data, K);
//printData(data);
//Attribute closest cluster to each data point
//For each point, calculate distance to each centroid
//Assign point to cluster (and update centroid value, by finding mean values of all points)
//Repeat until nothing changed or after a certain number of iterations
return 1;
}
然而，我注意到我的代码只能在一次迭代中正常工作。但是在第一次迭代之后，在我的setClusters＆＃39;功能，我推进了一个点。我必须将Point转移到另一个群集（如果需要），但是如果不采取从该群集中删除该Point然后将其推送到另一个群集的漫长路径，我会对如何做到这一点感到困惑。我确信有一种更有效的方法可以将Point换成另一个群集。
答案 0 :(得分：0)
我没有时间浏览整个代码，但如果您对优化setClusters()感兴趣，那么也许您可以尝试C ++的移动语义和{ {1}} vector。
emplace_back()另一个想法是void setClusters(std::vector<Cluster> &clusters, std::vector<Point> points){
int sendToCluster = 99999999;
for(int index = 0; index < points.size(); index++){
double minDist = 99999999999;
Point& p = points[index]; // use a reference to avoid copying, unless you don't want to alter the original "points" vector
for(int clusterNum = 0; clusterNum < clusters.size(); clusterNum++){
double tempDist = calculateDistance(p, clusters[clusterNum].centroid);
//std::cout << "dist: " << tempDist << " clusterNum: " << clusterNum << std::endl;
if(tempDist < minDist){
minDist = tempDist;
sendToCluster = clusterNum;
}
}
//std::cout << "Pushing to clusterNUm " << sendToCluster << std::endl;
clusters[sendToCluster].points
.emplace_back( // construct the new item in place
std::move(p) // by "moving" from p
);
}
points.clear(); // clear the input vector, unless you don't want to alter it. cf. the comment on using references
}
s持有＆＃34;指数＆＃34;而不是实际的＆＃34;点＆＃34;。在这种情况下，您可以将结构声明为：
Cluster然后，在struct Cluster{
std::vector<int> pointIndices;
Point centroid;
};
中，您可以推送索引：
setClusters()我希望它有所帮助。