def __init__(self,
rate=None,
seed=None,
dtype=mstype.float32,
name="Poisson"):
"""
Constructor of Poisson.
"""
param = dict(locals())
param['param_dict'] = {'rate': rate}
valid_dtype = mstype.int_type + mstype.uint_type + mstype.float_type
Validator.check_type_name("dtype", dtype, valid_dtype, type(self).__name__)
super(Poisson, self).__init__(seed, dtype, name, param)
self._rate = self._add_parameter(rate, 'rate')
if self.rate is not None:
check_greater_zero(self.rate, 'rate')
# ops needed for the class
self.exp = exp_generic
self.log = log_generic
self.squeeze = P.Squeeze(0)
self.cast = P.Cast()
self.floor = P.Floor()
self.dtypeop = P.DType()
self.shape = P.Shape()
self.fill = P.Fill()
self.less = P.Less()
self.equal = P.Equal()
self.select = P.Select()
self.lgamma = nn.LGamma()
self.igamma = nn.IGamma()
self.poisson = C.poisson
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 __init__(self,
concentration=None,
rate=None,
seed=None,
dtype=mstype.float32,
name="Gamma"):
"""
Constructor of Gamma.
"""
param = dict(locals())
param['param_dict'] = {'concentration': concentration, 'rate': rate}
valid_dtype = mstype.float_type
Validator.check_type_name("dtype", dtype, valid_dtype,
type(self).__name__)
# As some operators can't accept scalar input, check the type here
if isinstance(concentration, (int, float)):
raise TypeError("Input concentration can't be scalar")
if isinstance(rate, (int, float)):
raise TypeError("Input rate can't be scalar")
super(Gamma, self).__init__(seed, dtype, name, param)
self._concentration = self._add_parameter(concentration,
'concentration')
self._rate = self._add_parameter(rate, 'rate')
if self._concentration is not None:
check_greater_zero(self._concentration, "concentration")
if self._rate is not None:
check_greater_zero(self._rate, "rate")
# ops needed for the class
self.log = log_generic
self.square = P.Square()
self.sqrt = P.Sqrt()
self.squeeze = P.Squeeze(0)
self.cast = P.Cast()
self.dtypeop = P.DType()
self.fill = P.Fill()
self.shape = P.Shape()
self.select = P.Select()
self.greater = P.Greater()
self.lgamma = nn.LGamma()
self.digamma = nn.DiGamma()
self.igamma = nn.IGamma()
('UnfoldGrad', {
'block': GradWrapUnfold(UnfoldNetValid()),
'desc_inputs': [Tensor(np.ones([1, 1, 3, 3], np.float32))],
'desc_bprop': [Tensor(np.ones([1, 4, 2, 2], np.float32))],
'skip': ['backward']}),
('LogSigmoid', {
'block': nn.LogSigmoid(),
'desc_inputs': [Tensor(np.array([1, 2, 3, 4]).astype(np.float32))],
'desc_bprop': [Tensor(np.array([1, 2, 3, 4]).astype(np.float32))],
'skip': ['backward']}),
('ReduceLogSumExp', {
'block': nn.ReduceLogSumExp((0,), False),
'desc_inputs': [Tensor(np.array([3, 4, 5, 6]).astype(np.float32))],
'skip': ['backward']}),
('LGamma', {
'block': nn.LGamma(),
'desc_inputs': [Tensor(np.array([3, 4, 5, 6]).astype(np.float32))],
'skip': ['backward']}),
('IGamma', {
'block': nn.IGamma(),
'desc_inputs': [Tensor(np.array([3, 4, 5, 6]).astype(np.float32)),
Tensor(np.array([3, 4, 5, 6]).astype(np.float32))],
'skip': ['backward']}),
('DiGamma', {
'block': nn.DiGamma(),
'desc_inputs': [Tensor(np.array([3, 4, 5, 6]).astype(np.float32))],
'skip': ['backward']}),
('LBeta', {
'block': nn.LBeta(),
'desc_inputs': [Tensor(np.array([3, 4, 5, 6]).astype(np.float32)),
Tensor(np.array([3, 4, 5, 6]).astype(np.float32))],
