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@axsk
Created May 29, 2020 13:40
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import numpy as np
t_adopt = .5
p_adopt = .9
p_expl = .1
def timestep(state, influence):
influenced = (np.abs(np.dot(influence,state)) > t_adopt)
r = np.random.rand(np.size(state))
# vectorized: 415us
#state = logic(state, influenced, r)
# loop: 737us
#for i in range(state.size):
# state[i] = logic1(state[i], influenced[i], r[i])
#map: 489 us
state = list(map(logic1, state, influenced, r))
#numba 180 us
#state = logicnumba(state, influenced, r)
return state
def init(n):
s = np.random.rand(n) > .5
I = np.random.rand(n,n)
return s, I
def run(n = 1000, iter=1000):
s, I = init(n)
for i in range(iter):
s = timestep(s,I)
return s
import numba
@numba.vectorize
def logic(state, influenced, rand):
if influenced:
if p_adopt > rand:
state = not(state)
else:
if p_expl > rand:
state = not(state)
return state
def logic1(state, influenced, rand):
if influenced:
if p_adopt > rand:
state = not(state)
else:
if p_expl > rand:
state = not(state)
return state
logic = np.vectorize(logic1)
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