def histogram_bottom_up_ll(node, data=None, dtype=np.float64):
probs = histogram_likelihood(node, data=data, dtype=dtype)
mpe_ids = np.isnan(data[:, node.scope[0]])
mode_data = np.ones((1, data.shape[1])) * histogram_mode(node)
probs[mpe_ids] = histogram_likelihood(node, data=mode_data, dtype=dtype)
return probs
def histogram_to_tf_graph(node,
data_placeholder=None,
log_space=True,
variable_dict=None,
dtype=np.float32):
with tf.variable_scope("%s_%s" % (node.__class__.__name__, node.id)):
inps = np.arange(int(max(node.breaks))).reshape((-1, 1))
tmpscope = node.scope[0]
node.scope[0] = 0
hll = histogram_likelihood(node, inps)
node.scope[0] = tmpscope
if log_space:
hll = np.log(hll)
lls = tf.constant(hll.astype(dtype))
col = data_placeholder[:, node.scope[0]]
return tf.squeeze(tf.gather(lls, col))