def mutate (pop, mjumps, mprob, genmprob):
##returns a mutated population, where
#pop is the population [number of subjects, size of subject]
#mjumps is the number of mutation trials
[pn,sn] = pop.shape
for j in np.arange(pn):
if (rand.random() <= mprob):
r = rand.random (sn)
ng = rand.random_integers(0,1, np.sum(r <= genmprob))
pop[j][r <= genmprob] = ng
return pop
def cross (pop, cjumps=None, cprob=None):
##returns a new population from crossing cjumps pairs in pop with
##probability cprob
## if not given cjumps, then cjumps = popsize
## if not given cprob, then cprob = 1
#random new pop size
[pn,sn] = pop.shape
if not cjumps:
cjumps = pn
if cprob > 0 and cprob <= 1:
#if only a percentage of selected parents will produce siblings
npn = np.sum(rand.random([cjumps,1]) > cprob)
else:
#if all selected parents will produce siblings
npn = cjumps
nupop = np.zeros([npn,sn], dtype=int);
#Uniform Crossover
for j in np.arange(npn):
pi1 = rand.randint(0,pn)
pi2 = rand.randint(0,pn)
while pi1 == pi2:
pi2 = rand.randint(0,pn)
p1 = pop[pi1,:]
p2 = pop[pi2,:]
c = np.array([p1,p2])
sel = rand.random_integers(0,1, [1,sn]);
nupop[j,:] = np.choose(sel,c)
def Rand(*shape):
r = np.empty(np.prod(shape))
for i in range(r.size):
r[i] = random.random()
# #change
# global asdf
# for i in range(r.size):
# asdf += 1
# r[i] = np.sin(asdf)
return r.reshape(shape)
