def _log_prob(self, counts):
counts = self._maybe_assert_valid_sample(counts)
concentration = tf.convert_to_tensor(self.concentration)
ordered_prob = (tf.math.lbeta(concentration + counts) -
tf.math.lbeta(concentration))
return ordered_prob + tfp_math.log_combinations(
self.total_count, counts)
def _log_prob(self, counts):
with tf.control_dependencies(self._maybe_assert_valid_sample(counts)):
log_p = (tf.math.log(self._probs) if self._logits is None else
tf.math.log_softmax(self._logits))
k = tf.convert_to_tensor(self.total_count)
return (tf.reduce_sum(counts * log_p, axis=-1) + # log_unnorm_prob
tfp_math.log_combinations(k, counts)) # -log_normalization
def _log_prob(self, counts):
log_p = (tf.math.log(self._probs) if self._logits is None else
tf.math.log_softmax(self._logits))
log_unnorm_prob = tf.reduce_sum(tf.math.multiply_no_nan(log_p, counts),
axis=-1)
neg_log_normalizer = tfp_math.log_combinations(self.total_count,
counts)
return log_unnorm_prob + neg_log_normalizer
def _log_prob(self, counts):
log_p = (
tf.math.log(self._probs)
if self._logits is None else tf.math.log_softmax(self._logits))
return (
tf.reduce_sum(counts * log_p, axis=-1) + # log_unnorm_prob
tfp_math.log_combinations(
self.total_count, counts)) # -log_normalization
