def sample_likelihood(self, zs, n):
"""x | z ~ p(x | z)"""
out = []
for s in range(zs.shape[0]):
out += [{'x': bernoulli.rvs(zs[s, :], size=n).reshape((n, ))}]
return out
def sample_likelihood(self, zs, size):
"""x | z ~ p(x | z)"""
out = np.zeros((zs.shape[0], size))
for s in range(zs.shape[0]):
out[s,:] = bernoulli.rvs(zs[s,:], size=size).reshape((size,))
return out
def sample_likelihood(self, zs, size):
"""x | z ~ p(x | z)"""
out = []
for s in range(zs.shape[0]):
out += [{'x': bernoulli.rvs(zs[s, :], size=size).reshape((size,))}]
return out
def sample_likelihood(self, zs, size):
"""x | z ~ p(x | z)"""
out = np.zeros((zs.shape[0], size))
for s in range(zs.shape[0]):
out[s, :] = bernoulli.rvs(zs[s, :], size=size)
return out
def sample(self, size=1):
"""z ~ q(z | lambda)"""
p = self.p.eval()
z = np.zeros((size, self.num_vars))
for d in range(self.num_vars):
z[:, d] = bernoulli.rvs(p[d], size=size)
return z
def sample(self, size, sess):
"""z ~ q(z | lambda)"""
p = sess.run(self.p)
z = np.zeros(size)
for d in range(self.num_vars):
z[:, d] = bernoulli.rvs(p[d], size=size[0])
return z
def sample(self, size=1, sess=None):
"""z ~ q(z | lambda)"""
p = sess.run(self.p)
z = np.zeros((size, self.num_vars))
for d in range(self.num_vars):
z[:, d] = bernoulli.rvs(p[d], size=size)
return z
def sample(self, size=1):
"""x ~ p(x | params)"""
p = self.p.eval()
x = np.zeros((size, self.num_vars))
for d in range(self.num_vars):
x[:, d] = bernoulli.rvs(p[d], size=size)
return x
def sample(self, size=1):
p = self.p.eval()
return bernoulli.rvs(p, size=size)
def np_sample(p):
# get `size` from lexical scoping
return bernoulli.rvs(p, size=n).astype(np.float32)
def _test(p, size):
val_est = bernoulli.rvs(p, size=size).shape
val_true = (size, ) + np.asarray(p).shape
assert val_est == val_true
