TransformedDistribution(bijector, distribution, seed=None, name="transformed_distribution")¶
Transformed Distribution. This class contains a bijector and a distribution and transforms the original distribution to a new distribution through the operation defined by the bijector.
bijector (Bijector) – The transformation to perform.
distribution (Distribution) – The original distribution. Must has a float dtype.
seed (int) – The seed is used in sampling. The global seed is used if it is None. Default:None. If this seed is given when a TransformedDistribution object is initialized, the object’s sampling function will use this seed; elsewise, the underlying distribution’s seed will be used.
name (str) – The name of the transformed distribution. Default: ‘transformed_distribution’.
- Supported Platforms:
The arguments used to initialize the original distribution cannot be None. For example, mynormal = msd.Normal(dtype=mindspore.float32) cannot be used to initialized a TransformedDistribution since mean and sd are not specified. batch_shape is the batch_shape of the original distribution. broadcast_shape is the broadcast shape between the original distribution and bijector. is_scalar_batch is only true if both the original distribution and the bijector are scalar batches. default_parameters, parameter_names and parameter_type are set to be consistent with the original distribution. Derived class can overwrite default_parameters and parameter_names by calling reset_parameters followed by add_parameter.
>>> import mindspore >>> import mindspore.nn as nn >>> import mindspore.nn.probability.distribution as msd >>> import mindspore.nn.probability.bijector as msb >>> from mindspore import Tensor >>> class Net(nn.Cell): ... def __init__(self, shape, dtype=mindspore.float32, seed=0, name='transformed_distribution'): ... super(Net, self).__init__() ... # create TransformedDistribution distribution ... self.exp = msb.Exp() ... self.normal = msd.Normal(0.0, 1.0, dtype=dtype) ... self.lognormal = msd.TransformedDistribution(self.exp, self.normal, seed=seed, name=name) ... self.shape = shape ... ... def construct(self, value): ... cdf = self.lognormal.cdf(value) ... sample = self.lognormal.sample(self.shape) ... return cdf, sample >>> shape = (2, 3) >>> net = Net(shape=shape, name="LogNormal") >>> x = np.array([2.0, 3.0, 4.0, 5.0]).astype(np.float32) >>> tx = Tensor(x, dtype=mindspore.float32) >>> cdf, sample = net(tx)