gluonts.mx.distribution.multivariate_gaussian module¶
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class
gluonts.mx.distribution.multivariate_gaussian.
MultivariateGaussian
(mu: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], L: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], F=None)[source]¶ Bases:
gluonts.mx.distribution.distribution.Distribution
Multivariate Gaussian distribution, specified by the mean vector and the Cholesky factor of its covariance matrix.
- Parameters
mu – mean vector, of shape (…, d)
L – Lower triangular Cholesky factor of covariance matrix, of shape (…, d, d)
F – A module that can either refer to the Symbol API or the NDArray API in MXNet
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property
F
¶
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arg_names
= None¶
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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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sample_rep
(num_samples: Optional[int] = None, dtype=<class 'numpy.float32'>) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol][source]¶ Draw samples from the multivariate Gaussian distributions. Internally, Cholesky factorization of the covariance matrix is used:
sample = L v + mu,
where L is the Cholesky factor, v is a standard normal sample.
- Parameters
num_samples – Number of samples to be drawn.
dtype – Data-type of the samples.
- Returns
Tensor with shape (num_samples, …, d).
- Return type
Tensor
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property
variance
¶ Tensor containing the variance of the distribution.
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class
gluonts.mx.distribution.multivariate_gaussian.
MultivariateGaussianOutput
(dim: int)[source]¶ Bases:
gluonts.mx.distribution.distribution_output.DistributionOutput
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distr_cls
= None¶
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domain_map
(F, mu_vector, L_vector)[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.
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property
event_shape
¶ Shape of each individual event contemplated by the distributions that this object constructs.
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