def _log_prob(self, counts):
counts = self._maybe_assert_valid_sample(counts)
ordered_prob = (
special_math_ops.lbeta(self.concentration + counts)
- special_math_ops.lbeta(self.concentration))
return ordered_prob + distribution_util.log_combinations(
self.total_count, counts)
def _log_prob(self, counts):
counts = self._maybe_assert_valid_sample(counts)
ordered_prob = (
special_math_ops.lbeta(self.concentration + counts)
- special_math_ops.lbeta(self.concentration))
return ordered_prob + distribution_util.log_combinations(
self.total_count, counts)
def testLogCombinationsShape(self):
# Shape [2, 2]
n = [[2, 5], [12, 15]]
with self.test_session():
n = np.array(n, dtype=np.float32)
# Shape [2, 2, 4]
counts = [[[1., 1, 0, 0], [2., 2, 1, 0]],
[[4., 4, 1, 3], [10, 1, 1, 4]]]
log_binom = distribution_util.log_combinations(n, counts)
self.assertEqual([2, 2], log_binom.get_shape())
def testLogCombinationsBinomial(self):
n = [2, 5, 12, 15]
k = [1, 2, 4, 11]
if not special:
return
log_combs = np.log(special.binom(n, k))
with self.test_session():
n = np.array(n, dtype=np.float32)
counts = [[1., 1], [2., 3], [4., 8], [11, 4]]
log_binom = distribution_util.log_combinations(n, counts)
self.assertEqual([4], log_binom.get_shape())
self.assertAllClose(log_combs, log_binom.eval())
def _log_normalization(self, counts):
counts = self._maybe_assert_valid_sample(counts)
return -distribution_util.log_combinations(self.total_count, counts)
def _log_normalization(self, counts): counts = self._maybe_assert_valid_sample(counts) return -distribution_util.log_combinations(self.total_count, counts)
