import numpy as np
X = np.random.random((1000, 3))

def pairwise_numpy(X):
    return np.sqrt(((X[:, None, :] - X) ** 2).sum(-1))
%timeit pairwise_numpy(X)

def pairwise_python(X):
    M = X.shape[0]
    N = X.shape[1]
    D = np.empty((M, M), dtype=np.float)
    for i in range(M):
        for j in range(M):
            d = 0.0
            for k in range(N):
                tmp = X[i, k] - X[j, k]
                d += tmp * tmp
            D[i, j] = np.sqrt(d)
    return D
%timeit pairwise_python(X)

from numba import double
from numba.decorators import jit, autojit

pairwise_numba = autojit(pairwise_python)

%timeit pairwise_numba(X)

%load_ext cythonmagic

%%cython

import numpy as np
cimport cython
from libc.math cimport sqrt

@cython.boundscheck(False)
@cython.wraparound(False)
def pairwise_cython(double[:, ::1] X):
    cdef int M = X.shape[0]
    cdef int N = X.shape[1]
    cdef double tmp, d
    cdef double[:, ::1] D = np.empty((M, M), dtype=np.float64)
    for i in range(M):
        for j in range(M):
            d = 0.0
            for k in range(N):
                tmp = X[i, k] - X[j, k]
                d += tmp * tmp
            D[i, j] = sqrt(d)
    return np.asarray(D)

%timeit pairwise_cython(X)

%%file pairwise_fort.f

      subroutine pairwise_fort(X,D,m,n)
          integer :: n,m
          double precision, intent(in) :: X(m,n)
          double precision, intent(out) :: D(m,m) 
          integer :: i,j,k
          double precision :: r 
          do i = 1,m 
              do j = 1,m 
                  r = 0
                  do k = 1,n 
                      r = r + (X(i,k) - X(j,k)) * (X(i,k) - X(j,k)) 
                  end do 
                  D(i,j) = sqrt(r) 
              end do 
          end do 
      end subroutine pairwise_fort

# Compile the Fortran with f2py.
# We'll direct the output into /dev/null so it doesn't fill the screen
!f2py -c pairwise_fort.f -m pairwise_fort > /dev/null

from pairwise_fort import pairwise_fort
XF = np.asarray(X, order='F')
%timeit pairwise_fort(XF)

from scipy.spatial.distance import cdist
%timeit cdist(X, X)

from sklearn.metrics import euclidean_distances
%timeit euclidean_distances(X, X)

%pylab inline

labels = ['python\nloop', 'numpy\nbroadc.', 'sklearn', 'fortran/\nf2py', 'scipy', 'cython', 'numba']
timings = [13.4, 0.111, 0.0356, 0.0167, 0.0129, 0.00987, 0.00912]
x = np.arange(len(labels))

ax = plt.axes(xticks=x, yscale='log')
ax.bar(x - 0.3, timings, width=0.6, alpha=0.4, bottom=1E-6)
ax.grid()
ax.set_xlim(-0.5, len(labels) - 0.5)
ax.set_ylim(1E-3, 1E2)
ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda i, loc: labels[int(i)]))
ax.set_ylabel('time (s)')
ax.set_title("Pairwise Distance Timings")