tensorflow_probability/python/distributions/wishart.py
def _multi_gamma_sequence(self, a, p, name="multi_gamma_sequence"):
    """Creates sequence used in multivariate (di)gamma; shape = shape(a)+[p]."""
    with self._name_scope(name):
      # Linspace only takes scalars, so we'll add in the offset afterwards.
      seq = tf.linspace(
          tf.constant(0., dtype=self.dtype), 0.5 - 0.5 * p, tf.cast(
              p, tf.int32))
      return seq + tf.expand_dims(a, [-1])
def _multi_gamma_sequence(self, a, p, name="multi_gamma_sequence"):
    """Creates sequence used in multivariate (di)gamma; shape = shape(a)+[p]."""
    with self._name_scope(name):
      # Linspace only takes scalars, so we'll add in the offset afterwards.
      seq = tf.linspace(
          tf.constant(0., dtype=self.dtype), 0.5 - 0.5 * p, tf.cast(
              p, tf.int32))
      return seq + tf.expand_dims(a, [-1])
def _multi_gamma_sequence ( self , a , p , name = "multi_gamma_sequence" ) : with self . _name_scope ( name ) : # Linspace only takes scalars, so we'll add in the offset afterwards. seq = tf . linspace ( tf . constant ( 0. , dtype = self . dtype ) , 0.5 - 0.5 * p , tf . cast ( p , tf . int32 ) ) return seq + tf . expand_dims ( a , [ - 1 ] )
Creates sequence used in multivariate (di)gamma; shape = shape(a)+[p].
Creates sequence used in multivariate ( di ) gamma ; shape = shape ( a ) + [ p ] .