Issue
I have the following function, which takes two 1D numpy arrays q_i and q_j, does some calculation (including taking the norm of their difference) and returns a numpy array:
import numpy as np
import numpy.linalg as lin
def coulomb(q_i, q_j, c1=0.8, c2=0.2, k=0.5):
"""
Parameters
----------
q_i : numpy.ndarray
q_j : numpy.ndarray
c1 : float
c2 : float
k : float
Returns
-------
numpy.ndarray
"""
q_ij = q_i - q_j
q_ij_norm = lin.norm(q_i - q_j)
f_q_i = (k * c1 * c2 / q_ij_norm ** 3) * q_ij
return f_q_i
Now I have a bunch of these q arrays stored in another numpy array t = [q1, q2, q3, …, qn], and I want to evaluate the function coulomb for all unique pairs of q_i and q_j inside t (i.e. for (q1, q2), (q1, q3), …, (q1, qn), (q2, q3), …, (q2, qn), … (q_n-1, qn)).
Is there a way to vectorize this calculation (and I mean really vectorize it to boost performance, because np.vectorize is only a for-loop under the hood)?
My current solution is a nested for-loop, which is far from optimal performance-wise:
for i, _ in enumerate(t):
for j, _ in enumerate(t[i+1:]):
f = coulomb(t[i], t[j])
...
Solution
here 3 posible solutions, the last one, is a little caothic but uses a vectorization to calculate n q vs one. Also is the fastests
from itertools import combinations
import numpy as np
import numpy.linalg as lin
def coulomb(q_i, q_j, c1=0.8, c2=0.2, k=0.5):
"""
Parameters
----------
q_i : numpy.ndarray
q_j : numpy.ndarray
c1 : float
c2 : float
k : float
Returns
-------
numpy.ndarray
"""
q_ij = q_i - q_j
q_ij_norm = lin.norm(q_ij)
f_q_i = (k * c1 * c2 / q_ij_norm ** 3) * q_ij
return f_q_i
def coulomb2(q_i, q_j, c1=0.8, c2=0.2, k=0.5):
"""
Parameters
----------
q_i : numpy.ndarray
q_j : numpy.ndarray
c1 : float
c2 : float
k : float
Returns
-------
numpy.ndarray
"""
q_ij = q_i - q_j
q_ij_norm = lin.norm(q_ij,axis=1).reshape(-1,1)
f_q_i = (k * c1 * c2 / q_ij_norm ** 3) * q_ij
return f_q_i
q = np.random.randn(500,10)
from itertools import combinations
from time import time
t1= time()
v = []
for i in range(q.shape[0]):
for j in range(i+1,q.shape[0]):
v.append([coulomb(q[i], q[j])])
t2= time()
combs = combinations(range(len(q)), 2)
vv =[]
for i,j in combs:
vv.append([coulomb(q[i], q[j])])
t3 = time()
vvv = []
for i in range(q.shape[0]):
vvv += list(coulomb2(q[i], q[i+1:]))
t4 = time()
print(t2-t1)
print(t3-t2)
print(t4-t3)
#0.9133327007293701
#1.0843684673309326
#0.04461050033569336
``
Answered By – Ulises Bussi
Answer Checked By – Terry (BugsFixing Volunteer)