def algoAnalysis(fn, nMin, nMax, mode):
"""
Run timer and plot time complexity
"""
n = []
time_result = []
y_result = []
y_err = []
for i in [j*32 for j in range(nMin,nMax+1)]:
with timer() as t:
temp_result, temp_err = fn(i, mode)
time_result.append(t.msecs)
y_result.append(temp_result)
y_err.append(temp_err)
n.append(i)
return n, time_result, y_result, y_err
import random
from itertools import izip
from itertools import islice
from pprint import pprint
from bisect import bisect
from optparse import OptionParser
import my_timer
# equal fitness function
def eq_fit():
return 1
full_t = my_timer.timer()
tt = my_timer.timer()
def record_stop(pop):
tt.stop()
pop.dvars().perform.append(tt.elapsed_long())
return True
def record_start(pop):
global tt
tt.start()
return True
import simuPOP as sim
import random
from itertools import izip
from itertools import islice
from pprint import pprint
from bisect import bisect
from optparse import OptionParser
import my_timer
# equal fitness function
def eq_fit():
return 1
tt = my_timer.timer()
def record_start(pop):
tt.start()
return True
def record_stop(pop):
tt.stop()
pop.dvars().perform.append( tt.elapsed_long() )
return True
# modified version of model provided in User Guide (pg. 86-87)
def infSitesMutate(pop, param):
(startPos, endPos, rate) = param
# determine the number of mutation events occurring in this generation
nEvents = sim.getRNG().randPoisson( rate )
