gluonts.mx.distribution.laplace module#
- class gluonts.mx.distribution.laplace.Laplace(mu: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], b: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol])[source]#
Bases:
gluonts.mx.distribution.distribution.DistributionLaplace distribution.
- Parameters
mu – Tensor containing the means, of shape (*batch_shape, *event_shape).
b – Tensor containing the distribution scale, of shape (*batch_shape, *event_shape).
F –
- property F#
- arg_names: Tuple#
- property args: List#
- property batch_shape: Tuple#
Layout of the set of events contemplated by the distribution.
Invoking sample() from a distribution yields a tensor of shape batch_shape + event_shape, and computing log_prob (or loss more in general) on such sample will yield a tensor of shape batch_shape.
This property is available in general only in mx.ndarray mode, when the shape of the distribution arguments can be accessed.
- cdf(x: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol][source]#
Return the value of the cumulative distribution function evaluated at x
- property event_dim: int#
Number of event dimensions, i.e., length of the event_shape tuple.
This is 0 for distributions over scalars, 1 over vectors, 2 over matrices, and so on.
- property event_shape: Tuple#
Shape of each individual event contemplated by the distribution.
For example, distributions over scalars have event_shape = (), over vectors have event_shape = (d, ) where d is the length of the vectors, over matrices have event_shape = (d1, d2), and so on.
Invoking sample() from a distribution yields a tensor of shape batch_shape + event_shape.
This property is available in general only in mx.ndarray mode, when the shape of the distribution arguments can be accessed.
- is_reparameterizable = True#
- log_prob(x: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol][source]#
Compute the log-density of the distribution at x.
- Parameters
x – Tensor of shape (*batch_shape, *event_shape).
- Returns
Tensor of shape batch_shape containing the log-density of the distribution for each event in x.
- Return type
Tensor
- property mean: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]#
Tensor containing the mean of the distribution.
- quantile(level: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol][source]#
Calculates quantiles for the given levels.
- Parameters
level – Level values to use for computing the quantiles. level should be a 1d tensor of level values between 0 and 1.
- Returns
Quantile values corresponding to the levels passed. The return shape is
(num_levels, …DISTRIBUTION_SHAPE…),
where DISTRIBUTION_SHAPE is the shape of the underlying distribution.
- Return type
quantiles
- sample_rep(num_samples=None, dtype=<class 'numpy.float32'>) Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol][source]#
- property stddev: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]#
Tensor containing the standard deviation of the distribution.
- class gluonts.mx.distribution.laplace.LaplaceOutput[source]#
Bases:
gluonts.mx.distribution.distribution_output.DistributionOutput- args_dim: Dict[str, int] = {'b': 1, 'mu': 1}#
- distr_cls#
- classmethod domain_map(F, mu, b)[source]#
Converts arguments to the right shape and domain. The domain depends on the type of distribution, while the correct shape is obtained by reshaping the trailing axis in such a way that the returned tensors define a distribution of the right event_shape.
- property event_shape: Tuple#
Shape of each individual event contemplated by the distributions that this object constructs.