def sample(self, size=1, sess=None):
"""z ~ q(z | lambda)"""
pi = sess.run(self.pi)
z = np.zeros((size, self.num_vars))
for i in range(self.num_factors):
z[:, (i*self.K):((i+1)*self.K)] = multinomial.rvs(1, pi[i, :],
size=size)
return z
def sample(self, size=1):
"""x ~ p(x | params)"""
pi = self.pi.eval()
x = np.zeros((size, self.num_vars))
for i in range(self.num_factors):
x[:, (i*self.K):((i+1)*self.K)] = multinomial.rvs(1, pi[i, :],
size=size)
return x
def sample(self, size=1):
"""z ~ q(z | lambda)"""
pi = self.pi.eval()
z = np.zeros((size, self.num_vars))
for i in range(self.num_factors):
z[:, (i * self.K):((i + 1) * self.K)] = multinomial.rvs(1,
pi[i, :],
size=size)
return z
def _test(n, p, size):
val_est = multinomial.rvs(n, p, size=size).shape
val_true = (size, ) + np.asarray(p).shape
assert val_est == val_true
def sample(self, size=1):
pi = self.pi.eval()
return multinomial.rvs(np.ones(self.shape[:-1]), pi, size=size)
def np_sample(p):
# get `size` from lexical scoping
return multinomial.rvs(np.ones(self.shape[:-1]), p, size=n).astype(np.float32)
def np_sample(p):
# get ``n`` from lexical scoping
return multinomial.rvs(np.ones(self.shape[:-1]), p,
size=n).astype(np.float32)
