def __crand(self):
"""
conditional randomization
for observation i with ni neighbors, the candidate set cannot include
i (we don't want i being a neighbor of i). we have to sample without
replacement from a set of ids that doesn't include i. numpy doesn't
directly support sampling wo replacement and it is expensive to
implement this. instead we omit i from the original ids, permutate the
ids and take the first ni elements of the permuted ids as the
neighbors to i in each randomization.
"""
z = np.array(self.z)
lisas = np.zeros((self.n, self.permutations))
n_1 = self.n - 1
k = self.w.max_neighbors + 1
nn = self.n - 1
rids = np.tile(np.arange(nn), (self.permutations, 1))
shuffle1d.shuffle2(rids, int(time.time() * 10e6))
ids = np.arange(self.w.n)
ido = self.w.id_order
w = [self.w.weights[ido[i]] for i in ids]
wc = np.array([self.w.cardinalities[ido[i]] for i in ids])
lisas = shuffle1d.for_i_in_wn(z, ids, rids[:, -k:], wc, lisas, w)
self.rlisas = (n_1 / self.den) * lisas
def __crand(self):
"""
conditional randomization
for observation i with ni neighbors, the candidate set cannot include
i (we don't want i being a neighbor of i). we have to sample without
replacement from a set of ids that doesn't include i. numpy doesn't
directly support sampling wo replacement and it is expensive to
implement this. instead we omit i from the original ids, permutate the
ids and take the first ni elements of the permuted ids as the
neighbors to i in each randomization.
"""
z = np.array(self.z)
lisas = np.zeros((self.n, self.permutations))
n_1 = self.n - 1
k = self.w.max_neighbors + 1
nn = self.n - 1
rids = np.tile(np.arange(nn), (self.permutations, 1))
shuffle1d.shuffle2(rids, int(time.time() * 10e6))
ids = np.arange(self.w.n)
ido = self.w.id_order
w = [self.w.weights[ido[i]] for i in ids]
wc = np.array([self.w.cardinalities[ido[i]] for i in ids])
lisas = shuffle1d.for_i_in_wn(z, ids, rids[:,-k:], wc, lisas, w)
self.rlisas = (n_1 / self.den) * lisas
def mpcrand(lm, conditional=True, cores=None):
z = np.array(lm.z)
#Has to be allocated as a C style array, not a numpy wrapped C style array.
lisas_c = mp.RawArray(ctypes.c_double, lm.n * lm.permutations)
lisas = np.frombuffer(lisas_c,
dtype=np.float).reshape(lm.n, lm.permutations)
n_1 = lm.n - 1
k = lm.w.max_neighbors + 1
nn = lm.n - 1
rids = np.tile(np.arange(nn), (lm.permutations, 1))
shuffle1d.shuffle2(rids, int(time.time() * 10e6))
ids = np.arange(lm.w.n)
ido = lm.w.id_order
wc = np.array([lm.w.cardinalities[ido[i]] for i in ids])
#Segment the jobs over available cores
if cores is None:
ncores = mp.cpu_count()
else:
ncores = cores
pool = mp.Pool(ncores)
#Compute the offsets - this is a decomposition by areal unit.
step = lm.n / ncores
starts = range(0, lm.n, step)
stops = starts[1:]
stops[-1] = stops[-1] + 1
offsets = zip(starts[:-1], stops)
#Split the weights into ncores lists
w = [lm.w.weights[ido[i]] for i in ids]
if conditional == True:
jobs = [
mp.Process(target=shuffle1d.mpfori,
args=(lisas, z, ids, rids, wc, w, offsets[i][0],
offsets[i][1], time.time() * 10e6))
for i in range(ncores)
]
else:
jobs = [
mp.Process(target=shuffle1d.mpfori_nonconditional,
args=(lisas, z, ids, rids, wc, w, offsets[i][0],
offsets[i][1], time.time() * 10e6))
for i in range(ncores)
]
for j in jobs:
j.start()
for j in jobs:
j.join()
return (n_1 / lm.den) * lisas
def mpcrand(lm, conditional=True, cores=None):
z = np.array(lm.z)
#Has to be allocated as a C style array, not a numpy wrapped C style array.
lisas_c = mp.RawArray(ctypes.c_double, lm.n * lm.permutations)
lisas = np.frombuffer(lisas_c, dtype = np.float).reshape(lm.n, lm.permutations)
n_1 = lm.n - 1
k = lm.w.max_neighbors + 1
nn = lm.n - 1
rids = np.tile(np.arange(nn), (lm.permutations, 1))
shuffle1d.shuffle2(rids, int(time.time() * 10e6))
ids = np.arange(lm.w.n)
ido = lm.w.id_order
wc = np.array([lm.w.cardinalities[ido[i]] for i in ids])
#Segment the jobs over available cores
if cores is None:
ncores = mp.cpu_count()
else:
ncores = cores
pool = mp.Pool(ncores)
#Compute the offsets - this is a decomposition by areal unit.
step = lm.n / ncores
starts = range(0, lm.n, step)
stops = starts[1:]
stops[-1] = stops[-1] + 1
offsets = zip(starts[:-1], stops)
#Split the weights into ncores lists
w = [lm.w.weights[ido[i]] for i in ids]
if conditional == True:
jobs =[mp.Process(target=shuffle1d.mpfori, args=(lisas, z, ids, rids, wc, w, offsets[i][0], offsets[i][1], time.time() * 10e6)) for i in range(ncores)]
else:
jobs = [mp.Process(target=shuffle1d.mpfori_nonconditional, args=(lisas, z, ids, rids, wc, w, offsets[i][0], offsets[i][1], time.time() * 10e6)) for i in range(ncores)]
for j in jobs:
j.start()
for j in jobs:
j.join()
return (n_1 / lm.den) * lisas
