why 'np.random.uniform' in python is same in population size? [duplicate]

I wanna define a structure with python3.x. this structure is the initial population, but I don't know, why in this loop, random numbers are the same?

import numpy as np
from numpy.lib.shape_base import _dstack_dispatcher
from numpy.random import default_rng
Xmin=-30;Xmax=30;n=2;MaxIter=200;nP=50
Fc=0.7
nC=2*round(Fc*nP/2)#;print(nC)
Fm=0.1
nM=round(Fm*nP)#;print(nM)
Pi_M=0.02
empty_ind={'X':[],'Cost':[],'rank':[],'CD':[]}
pop=np.tile(empty_ind,(nP,1))
for i in range(nP): pop[i,0]['X']=np.random.uniform(Xmin,Xmax,[1,n]) def MOP5(a): #n=np.size(x) x=a[0,0] y=a[0,1] f1=0.5*(x**2+y**2)+np.sin(x**2+y**2) f2=(3*x-2*y+4)**2/8 + (x-y+1)**2/27 +15 f3=1/(x**2+y**2+1)- 1.1*np.exp(-x**2-y**2) aa=[f1,f2,f3] z=np.reshape(aa,(3,1)) return z pop[i,0]['Cost']=MOP5(pop[i,0]['X'])
print(pop)

and this is my result :

[{'X': array([[ 11.65978708, -17.46716958]]), 'Cost': array([[2.21468705e+02], [7.31520444e+02], [2.26217399e-03]]), 'rank': [], 'CD': []}] [{'X': array([[ 11.65978708, -17.46716958]]), 'Cost': array([[2.21468705e+02], [7.31520444e+02], [2.26217399e-03]]), 'rank': [], 'CD': []}] 

please let me know, why the results are the same?

1 Answer

I suspect that np.tile actually tiles the same dict object (in this case empty_ind). So when you assign something to pop[i, 0]['X'], you modify the all the dicts at the same time (because those dict are indeed the same object).

Try something like:

pop = [{'X':[],'Cost':[],'rank':[],'CD':[]} for _ in range(nP)] # make a new object {...} at each for iteration
for i in range(nP): pop[i]['X']=np.random.uniform(Xmin,Xmax,[1,n]) def MOP5(a): #n=np.size(x) x=a[0,0] y=a[0,1] f1=0.5*(x**2+y**2)+np.sin(x**2+y**2) f2=(3*x-2*y+4)**2/8 + (x-y+1)**2/27 +15 f3=1/(x**2+y**2+1)- 1.1*np.exp(-x**2-y**2) aa=[f1,f2,f3] z=np.reshape(aa,(3,1)) return z pop[i]['Cost']=MOP5(pop[i]['X'])
print(pop)

Here I chose to use list instead of np.array for pop; having a numpy array of dicts does not make much sense to me anyways.