def model():
loc = ed.Normal(loc=0., scale=1., name="loc")
flip = ed.Bernoulli(probs=0.5, name="flip")
if tf.equal(flip, 1):
x = ed.Normal(loc=loc, scale=0.5, sample_shape=5, name="x")
else:
x = ed.Poisson(rate=tf.nn.softplus(loc), sample_shape=3, name="x")
return x
def deep_exponential_family(data_size, feature_size, units, shape):
"""A multi-layered topic model over a documents-by-terms matrix."""
w2 = ed.Gamma(0.1, 0.3, sample_shape=[units[2], units[1]], name="w2")
w1 = ed.Gamma(0.1, 0.3, sample_shape=[units[1], units[0]], name="w1")
w0 = ed.Gamma(0.1, 0.3, sample_shape=[units[0], feature_size], name="w0")
z2 = ed.Gamma(0.1, 0.1, sample_shape=[data_size, units[2]], name="z2")
z1 = ed.Gamma(shape, shape / tf.matmul(z2, w2), name="z1")
z0 = ed.Gamma(shape, shape / tf.matmul(z1, w1), name="z0")
x = ed.Poisson(tf.matmul(z0, w0), name="x")
return x
def Zheng07SatsPoisson(halo_mvir,
n_cen,
logM0=ed.Deterministic(11.2, name='logM0'),
logM1=ed.Deterministic(12.4, name='logM1'),
alpha=ed.Deterministic(0.83, name='alpha'),
name='zheng07Sats', **kwargs):
M0 = 10.**logM0
M1 = 10.**logM1
rate = n_cen.distribution.probs * ((halo_mvir - M0)/M1)**alpha
rate = tf.where(halo_mvir < M0, 1e-4, rate)
return ed.Poisson(rate=rate, name=name)
def deep_exponential_family(data_size, feature_size, units, shape):
# units表示每层维数的大小，从unit[2]开始到unit[0]，最后输出feature_size
# data_size为batch_size的意思
w2 = ed.Gamma(0.1, 0.3, sample_shape=[units[2], units[1]],
name="w2") # 前两个位置为参数
w1 = ed.Gamma(0.1, 0.3, sample_shape=[units[1], units[0]], name="w1")
w0 = ed.Gamma(0.1, 0.3, sample_shape=[units[0], feature_size], name="w0")
# z2相当于是原始分布，用z2结合参数传到生成x
z2 = ed.Gamma(0.1, 0.1, sample_shape=[data_size, units[2]], name="z2")
z1 = ed.Gamma(shape, shape / tf.matmul(z2, w2),
name="z1") # z1的形状跟着两个参数的形状跑，此处相当于对rate建模，concentration不建模
z0 = ed.Gamma(shape, shape / tf.matmul(z1, w1), name="z0")
x = ed.Poisson(tf.matmul(z0, w0), name="x")
return x, w2, w1, w0, z2, z1, z0
def latent_poisson(shape, rate):
rate = tf.constant(rate, shape=shape)
prior = ed.Poisson(rate=rate)
return prior
