#include <cstdio>
#include <algorithm>
#include <cmath>
#include <vector>
#include <cassert>
using namespace std;
template<typename coord_type>
struct point_data_t{
coord_type x,y;
point_data_t(){
x = y = 0;
}
point_data_t(coord_type x,coord_type y){
this->x = x;
this->y = y;
}
double dist(const point_data_t &other)const{
return sqrt((double)(x - other.x) * (x - other.x) + (double)(y - other.y) * (y - other.y));
}
bool operator < (const point_data_t &other)const{
if(x != other.x){
return x < other.x;
}
return y < other.y;
}
bool operator == (const point_data_t &other)const{
return x == other.x && y == other.y;
}
bool operator != (const point_data_t &other)const{
return x != other.x || y != other.y;
}
};
class KDTree{
private:
KDTree *leftson,*rightson;
point_data_t<int> P;
public:
KDTree(){
leftson = rightson = NULL;
P = point_data_t<int>(0,0);
}
~KDTree(){
if(leftson != NULL){
delete leftson;
}
if(rightson != NULL){
delete rightson;
}
}
KDTree(KDTree *leftson,KDTree *rightson,point_data_t<int> P){
this->leftson = leftson;
this->rightson = rightson;
this->P = P;
}
void build(vector<point_data_t<int>> &points,int left,int right);
///best_dist_so_far contains the actual distance squared;leaves P uneffected,returns the closeste point
point_data_t<int> find_nearest_neighbour(point_data_t<int> &P,double &best_dist_so_far);
KDTree(vector<point_data_t<int>> points);
};
void KDTree::build(vector<point_data_t<int>> &points,int left,int right){///[left,right);swaps x and y of points to prevent two compare functions;changes the order of parameter points
int mid = (left + right) / 2;
nth_element(points.begin() + left,points.begin() + mid,points.begin() + right);
this->P = points[mid];
for(int i = left;i < right;i++){
swap(points[i].x,points[i].y);
}
if(left < mid){
this->leftson = new KDTree;
this->leftson->build(points,left,mid);
}
if(mid + 1 < right){
this->rightson = new KDTree;
this->rightson->build(points,mid + 1,right);
}
for(int i = left;i < right;i++){
swap(points[i].x,points[i].y);
}
}
point_data_t<int> KDTree::find_nearest_neighbour(point_data_t<int> &P,double &best_dist_so_far){///P has the coordonates in order for this depth,returns the answer with coordinates swaped depending on the depth
point_data_t<int> best_subtree_point = this->P;
if(P != this->P && best_dist_so_far > P.dist(this->P)){
best_dist_so_far = P.dist(this->P);
best_subtree_point = this->P;
}
if(leftson == NULL && rightson == NULL){
return best_subtree_point;
}
if(leftson == NULL){
swap(P.x,P.y);
point_data_t<int> rightson_ans = this->rightson->find_nearest_neighbour(P,best_dist_so_far);
swap(P.x,P.y);
swap(rightson_ans.x,rightson_ans.y);
if(P.dist(best_subtree_point) > P.dist(rightson_ans)){
best_subtree_point = rightson_ans;
}
return best_subtree_point;
}
if(rightson == NULL){
swap(P.x,P.y);
point_data_t<int> leftson_ans = this->leftson->find_nearest_neighbour(P,best_dist_so_far);
swap(P.x,P.y);
swap(leftson_ans.x,leftson_ans.y);
if(P.dist(best_subtree_point) > P.dist(leftson_ans)){
best_subtree_point = leftson_ans;
}
return best_subtree_point;
}
if(P < this->P){
swap(P.x,P.y);
point_data_t<int> leftson_ans = this->leftson->find_nearest_neighbour(P,best_dist_so_far);
swap(P.x,P.y);
swap(leftson_ans.x,leftson_ans.y);
if(P.dist(best_subtree_point) > P.dist(leftson_ans)){
best_subtree_point = leftson_ans;
}
if(P.x + best_dist_so_far >= this->P.x){
swap(P.x,P.y);
point_data_t<int> rightson_ans = this->rightson->find_nearest_neighbour(P,best_dist_so_far);
swap(P.x,P.y);
swap(rightson_ans.x,rightson_ans.y);
if(P.dist(best_subtree_point) > P.dist(rightson_ans)){
best_subtree_point = rightson_ans;
}
}
return best_subtree_point;
}
else{
swap(P.x,P.y);
point_data_t<int> rightson_ans = this->rightson->find_nearest_neighbour(P,best_dist_so_far);
swap(P.x,P.y);
swap(rightson_ans.x,rightson_ans.y);
if(P.dist(best_subtree_point) > P.dist(best_subtree_point)){
best_subtree_point = rightson_ans;
}
if(P.x - best_dist_so_far <= this->P.x){
swap(P.x,P.y);
point_data_t<int> leftson_ans = this->leftson->find_nearest_neighbour(P,best_dist_so_far);
swap(P.x,P.y);
swap(leftson_ans.x,leftson_ans.y);
if(P.dist(best_subtree_point) > P.dist(leftson_ans)){
best_subtree_point = leftson_ans;
}
}
return best_subtree_point;
}
}
KDTree::KDTree(vector<point_data_t<int>> points){
this->build(points,0,(int)points.size());
}
int N;
vector<point_data_t<int>> V;
FILE *f = fopen("cmap.in","r");
FILE *g = fopen("cmap.out","w");
int main(){
fscanf(f,"%d",&N);
V.resize(N);
for(int i = 0;i < N;i++){
fscanf(f,"%d %d",&V[i].x,&V[i].y);
}
KDTree *root = new KDTree(V);
double ans = 5e18;
for(auto it:V){
double tmp = 5e18;
root->find_nearest_neighbour(it,tmp);
ans = min(tmp,ans);
}
fprintf(g,"%.7f",ans);
fclose(f);
fclose(g);
return 0;
}