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 get_batch(X, patchshape, batch_size=200):
ix = randint(0, X.shape[1] - patchshape[0])
iy = randint(0, X.shape[2] - patchshape[1])
iz = randint(0, X.shape[3] -
patchshape[2]) if patchshape[2] != X.shape[3] else 0
B = X[randint(0, X.shape[0], size=batch_size), ix:ix + patchshape[0],
iy:iy + patchshape[1], iz:iz + patchshape[2]].reshape(
batch_size, patchshape[0] * patchshape[1] * patchshape[2])
B = B.T
B = B - np.mean(B, axis=0)
U, S, V = svd(mm(B, B.T))
ZCAMatrix = mm(U, mm(np.diag(1.0 / np.sqrt(S + 1e-5)), U.T))
B = mm(B, ZCAMatrix)
return B
def Randint(low, high=None, size = None):
assert high is None, 'unimplemented yet'
if size is None:
#change
# global asdf
# asdf += 1
# return asdf % low
return random.randint(0, low-1)
a = np.empty(np.prod(size) if not np.isscalar(size) else size, dtype=int)
for i in range(a.size):
a[i] = random.randint(0, low-1)
#change
# global asdf
# asdf += 1
# a[i] = asdf % low
return a.reshape(size)
# ---------------------------------------------------------------- # # Fourier Transforms # ---------------------------------------------------------------- # np.fft.fft(a) # complex fourier transform of a f = np.fft.fftfreq(len(a)) # fft frequencies np.fft.fftshift(f) # shifts zero frequency to the middle np.fft.rfft(a) # real fourier transform of a np.fft.rfftfreq(len(a)) # real fft frequencies # ---------------------------------------------------------------- # # Rounding # ---------------------------------------------------------------- # np.ceil(a) # rounds to nearest upper int np.floor(a) # rounds to nearest lower int np.round(a) # rounds to neares int # ---------------------------------------------------------------- # # Random Variables # ---------------------------------------------------------------- # from np.random import normal, seed, rand, uniform, randint normal(loc=0, scale=2, size=100) # 100 normal distributed seed(23032) # resets the seed value rand(200) # 200 random numbers in [0, 1) uniform(1, 30, 200) # 200 random numbers in [1, 30) randint(1, 16, 300) # 300 random integers in [1, 16)
