blogle

#define _GNU_SOURCE                                                                                                                                                                                          
#include <sched.h>                                                                                                                                                                                           

#include <stdio.h>                                                                                                                                                                                           
#include <stdlib.h>                                                                                                                                                                                          
#include <signal.h>                                                                                                                                                                                    

blogle

import numpy as np
from scipy.spatial import KDTree

def minkowski_distance_p(x, y, p=2):
    """
    Compute the p-th power of the L**p distance between two arrays.

    For efficiency, this function computes the L**p distance but does
    not extract the pth root. If `p` is 1 or infinity, this is equal to
    the actual L**p distance.

blogle

import numpy as np
import networkx as nx
import heapq
from collections import defaultdict

def distance(u, v):
    return np.sum((u - v)**2)

class KDTree(object):

blogle

def choose(n,k):
    "efficient calculation of nCr using sets"
    
    if n < k:
        raise ValueError('n < k')
        
    n_set, k_set = set(range(1,n+1)), set(range(1,k+1))
    n_min_k_set = set(range(1,(n-k)+1))
    
    if len(n_min_k_set) < len(k_set):