def sample_n(self, n, seed=None):
if self.n != 1:
logits = logit(tf.ones(self.n, dtype=tf.float32) /
tf.cast(self.n, dtype=tf.float32))
cat = tf.contrib.distributions.Categorical(logits=logits)
indices = cat.sample_n(n, seed)
return tf.gather(self._params, indices)
else:
multiples = tf.concat(0, [tf.expand_dims(n, 0),
[1] * len(self.get_event_shape())])
return tile(self._params, multiples)
def _sample_n(self, n, seed=None):
if self.n != 1:
logits = logit(
tf.ones(self.n, dtype=tf.float32) /
tf.cast(self.n, dtype=tf.float32))
cat = tf.contrib.distributions.Categorical(logits=logits)
indices = cat._sample_n(n, seed)
return tf.gather(self._params, indices)
else:
multiples = tf.concat(
[tf.expand_dims(n, 0), [1] * len(self.get_event_shape())], 0)
return tile(self._params, multiples)
def sample_n(self, n, seed=None, name="sample_n"):
"""Sample `n` observations from the Point Mass distribution.
Args:
n: `Scalar`, type int32, the number of observations to sample.
seed: Python integer, the random seed.
name: The name to give this op.
Returns:
samples: `[n, ...]`, a `Tensor` of `n` samples for each
of the distributions determined by broadcasting the hyperparameters.
"""
with ops.name_scope(self.name):
with ops.op_scope([self._params, n], name):
multiples = tf.concat(
0,
[tf.expand_dims(n, 0), [1] * len(self.get_event_shape())])
return tile(self._params, multiples)
def _test(self, input, multiples):
if isinstance(multiples, int) or isinstance(multiples, float):
multiples_shape = [multiples]
elif isinstance(multiples, tuple):
multiples_shape = list(multiples)
else:
multiples_shape = multiples
input_shape = get_dims(input)
diff = len(input_shape) - len(multiples_shape)
if diff < 0:
input_shape = [1] * abs(diff) + input_shape
elif diff > 0:
multiples_shape = [1] * diff + multiples_shape
val_true = [x * y for x, y in zip(input_shape, multiples_shape)]
with self.test_session():
val_est = get_dims(tile(input, multiples))
assert val_est == val_true
def _sample_n(self, n, seed=None):
multiples = tf.concat(
0, [tf.expand_dims(n, 0), [1] * len(self.get_event_shape())])
return tile(self._params, multiples)
def _sample_n(self, n, seed=None):
multiples = tf.concat(0, [tf.expand_dims(n, 0),
[1] * len(self.get_event_shape())])
return tile(self._params, multiples)
