def _resample_Z_gsl(self, data=[]):
"""
Resample the parents given the bias_model, weight_model, and impulse_model.
:param bias_model:
:param weight_model:
:param impulse_model:
:return:
"""
bias_model, weight_model, impulse_model = \
self.model.bias_model, self.model.weight_model, self.model.impulse_model
K, B = self.K, self.B
# Make a big matrix of size T x (K*B + 1)
for k2, (Sk, Fk, Tk, Zk) in enumerate(zip(self.Ss, self.Fs, self.Ts, self.Z)):
P = np.zeros((Tk, 1+self.K * self.B))
P[:,0] = bias_model.lambda0[k2]
Wk2 = np.repeat(weight_model.W_effective[:,k2], B)
Gk2 = impulse_model.g[:,k2,:].reshape((K*B,), order="C")
P[:,1:] = Wk2 * Gk2 * Fk.reshape((Tk, K*B))
# Normalize the rows
P = P / P.sum(1)[:,None]
# Sample parents from P with counts S[:,k2]
multinomial_par(self.pyrngs, Sk, P, Zk)
def test_parallel_multi_N_multi_p_with_out():
# Multiple N counts, multiple p arrays, out structure provided
L = 10
N = np.arange(L, dtype=np.uint32) + 10
K = 5
p = np.zeros((L, K))
p[:5] = 1./K * np.ones(K)
p[5:] = np.asarray([0.5, 0.25, 0.05, 0.1, 0.1])
# Create some RNGs
rngs = [PyRNG() for _ in xrange(get_omp_num_threads())]
n_iter = 1000000
z = np.zeros((L,K))
for _ in xrange(n_iter):
out = np.zeros((L,K), dtype=np.uint32)
multinomial_par(rngs, N, p, out)
assert out.shape == (L,K)
assert (out.sum(axis=1) == N).all()
z += out
print z/z.sum(axis=1)[:,np.newaxis]
assert (np.abs(z/z.sum(axis=1)[:,np.newaxis] - p) < 1e-2).all()
def resample_z(self):
topicprobs = self._get_topicprobs()
multinomial_par(self.pyrngs, self.data.data, topicprobs, self.z)
self._update_counts()
def resample_z(self):
topicprobs = self.get_topicprobs(self.data)
multinomial_par(self.pyrngs, self.data.data, topicprobs, self.z)
self._update_counts()
