def __init__(self,
loc,
scale,
seed=0,
dtype=mstype.float32,
name="Gumbel"):
"""
Constructor of Gumbel distribution.
"""
valid_dtype = mstype.float_type
Validator.check_type_name("dtype", dtype, valid_dtype, type(self).__name__)
gumbel_cdf = msb.GumbelCDF(loc, scale)
super(Gumbel, self).__init__(
distribution=msd.Uniform(0.0, 1.0, dtype=dtype),
bijector=msb.Invert(gumbel_cdf),
seed=seed, name=name)
# overwrite default_parameters and parameter_names
self._reset_parameters()
self._loc = self._add_parameter(loc, 'loc')
self._scale = self._add_parameter(scale, 'scale')
self._gumbel_bijector = gumbel_cdf
# ops needed for the class
self.cast = P.Cast()
self.const = P.ScalarToArray()
self.exp = exp_generic
self.expm1 = expm1_generic
self.fill = P.Fill()
self.lgamma = nn.LGamma()
self.log = log_generic
self.shape = P.Shape()
self.sqrt = P.Sqrt()
def test_name():
with pytest.raises(TypeError):
msd.Uniform(0., 1., name=1.0)
def test_type():
with pytest.raises(TypeError):
msd.Uniform(0., 1., dtype=dtype.int32)
def __init__(self):
super(KL, self).__init__()
self.u = msd.Uniform([0.0], [1.5], dtype=dtype.float32)
def __init__(self):
super(LogProb, self).__init__()
self.u = msd.Uniform([0.0], [[1.0], [2.0]], dtype=dtype.float32)
def __init__(self):
super(EntropyH, self).__init__()
self.u = msd.Uniform([0.0], [1.0, 2.0], dtype=dtype.float32)
def __init__(self, shape, seed=0):
super(Sampling, self).__init__()
self.u = msd.Uniform([0.0], [[1.0], [2.0]], seed=seed, dtype=dtype.float32)
self.shape = shape
def __init__(self):
super(Basics, self).__init__()
self.u = msd.Uniform([0.0], [3.0], dtype=dtype.float32)
