gluonts.mx.distribution.gaussian module¶
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class
gluonts.mx.distribution.gaussian.
Gaussian
(mu: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], sigma: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol])[source]¶ Bases:
gluonts.mx.distribution.distribution.Distribution
Gaussian distribution.
- Parameters
mu – Tensor containing the means, of shape (*batch_shape, *event_shape).
std – Tensor containing the standard deviations, of shape (*batch_shape, *event_shape).
F –
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property
F
¶
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arg_names
= None¶
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property
args
¶
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property
batch_shape
¶ 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.
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property
event_dim
¶ 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.
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property
event_shape
¶ 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.
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is_reparameterizable
= True¶
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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
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property
mean
¶ Tensor containing the mean of the distribution.
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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
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sample
(num_samples: Optional[int] = None, dtype=<class 'numpy.float32'>) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol][source]¶ Draw samples from the distribution.
If num_samples is given the first dimension of the output will be num_samples.
- Parameters
num_samples – Number of samples to to be drawn.
dtype – Data-type of the samples.
- Returns
A tensor containing samples. This has shape (*batch_shape, *eval_shape) if num_samples = None and (num_samples, *batch_shape, *eval_shape) otherwise.
- Return type
Tensor
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sample_rep
(num_samples: Optional[int] = None, dtype=<class 'numpy.float32'>) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol][source]¶
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property
stddev
¶ Tensor containing the standard deviation of the distribution.
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class
gluonts.mx.distribution.gaussian.
GaussianOutput
[source]¶ Bases:
gluonts.mx.distribution.distribution_output.DistributionOutput
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args_dim
: Dict[str, int] = {'mu': 1, 'sigma': 1}¶
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classmethod
domain_map
(F, mu, sigma)[source]¶ Maps raw tensors to valid arguments for constructing a Gaussian distribution.
- Parameters
F –
mu – Tensor of shape (*batch_shape, 1)
sigma – Tensor of shape (*batch_shape, 1)
- Returns
Two squeezed tensors, of shape (*batch_shape): the first has the same entries as mu and the second has entries mapped to the positive orthant.
- Return type
Tuple[Tensor, Tensor]
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property
event_shape
¶ Shape of each individual event contemplated by the distributions that this object constructs.
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