gluonts.mx.distribution.lowrank_multivariate_gaussian module

class gluonts.mx.distribution.lowrank_multivariate_gaussian.LowrankMultivariateGaussian(dim: int, rank: int, mu: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], D: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], W: Optional[Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]] = None)

Bases: gluonts.mx.distribution.distribution.Distribution

Multivariate Gaussian distribution, with covariance matrix parametrized as the sum of a diagonal matrix and a low-rank matrix.

\[\Sigma = D + W W^T\]

When W = None the covariance matrix is just diagonal.

The implementation is strongly inspired from Pytorch: https://github.com/pytorch/pytorch/blob/master/torch/distributions/lowrank_multivariate_normal.py.

Complexity to compute log_prob is \(O(dim * rank + rank^3)\) per element.

Parameters

• dim – Dimension of the distribution’s support

• rank – Rank of W

• mu – Mean tensor, of shape (…, dim)

• D – Diagonal term in the covariance matrix, of shape (…, dim)

• W – Low-rank factor in the covariance matrix, of shape (…, dim, rank) Optional; if not provided, the covariance matrix is just diagonal.

property F

arg_names: Tuple

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.

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.

classmethod fit(F, samples: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], rank: int = 0) → gluonts.mx.distribution.distribution.Distribution

Returns an instance of LowrankMultivariateGaussian after fitting parameters to the given data. Only the special case of rank = 0 is supported at the moment.

Parameters

• F –

• samples – Tensor of shape (num_samples, batch_size, seq_len, target_dim)

• rank – Rank of W

Return type

Distribution instance of type LowrankMultivariateGaussian.

is_reparameterizable = True

log_prob(x: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]

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.

sample_rep(num_samples: typing.Optional[int] = None, dtype=<class 'numpy.float32'>) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]

Draw samples from the multivariate Gaussian distribution:

\[s = \mu + D u + W v,\]

where \(u\) and \(v\) are standard normal samples.

Parameters

• num_samples – number of samples to be drawn.

• dtype – Data-type of the samples.

Return type

tensor with shape (num_samples, …, dim)

property variance: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]

Tensor containing the variance of the distribution.

class gluonts.mx.distribution.lowrank_multivariate_gaussian.LowrankMultivariateGaussianOutput(dim: int, rank: int, sigma_init: float = 1.0, sigma_minimum: float = 0.001)

Bases: gluonts.mx.distribution.distribution_output.DistributionOutput

args_dim: Dict[str, int]

distr_cls: type

distribution(distr_args, loc=None, scale=None, **kwargs) → gluonts.mx.distribution.distribution.Distribution

Construct the associated distribution, given the collection of constructor arguments and, optionally, a scale tensor.

Parameters

• distr_args – Constructor arguments for the underlying Distribution type.

• loc – Optional tensor, of the same shape as the batch_shape+event_shape of the resulting distribution.

• scale – Optional tensor, of the same shape as the batch_shape+event_shape of the resulting distribution.

domain_map(F, mu_vector, D_vector, W_vector=None)

Parameters

• F –

• mu_vector – Tensor of shape (…, dim)

• D_vector – Tensor of shape (…, dim)

• W_vector – Tensor of shape (…, dim * rank )

Returns

A tuple containing tensors mu, D, and W, with shapes (…, dim), (…, dim), and (…, dim, rank), respectively.

Return type

Tuple

property event_shape: Tuple

Shape of each individual event contemplated by the distributions that this object constructs.

get_args_proj(prefix: Optional[str] = None) → gluonts.mx.distribution.distribution_output.ArgProj

gluonts.mx.distribution.lowrank_multivariate_gaussian.capacitance_tril(F, rank: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], W: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], D: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]

Parameters

• F –

• rank –

• W ((..., dim, rank)) –

• D ((..., dim)) –

Return type

the capacitance matrix \(I + W^T D^{-1} W\)

gluonts.mx.distribution.lowrank_multivariate_gaussian.inv_softplus(y)

gluonts.mx.distribution.lowrank_multivariate_gaussian.log_det(F, batch_D: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], batch_capacitance_tril: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]

Uses the matrix determinant lemma.

\[\log|D + W W^T| = \log|C| + \log|D|,\]

where \(C\) is the capacitance matrix \(I + W^T D^{-1} W\), to compute the log determinant.

Parameters

• F –

• batch_D –

• batch_capacitance_tril –

gluonts.mx.distribution.lowrank_multivariate_gaussian.lowrank_log_likelihood(rank: int, mu: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], D: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], W: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], x: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]

gluonts.mx.distribution.lowrank_multivariate_gaussian.mahalanobis_distance(F, W: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], D: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], capacitance_tril: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], x: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]

Uses the Woodbury matrix identity.

\[(W W^T + D)^{-1} = D^{-1} - D^{-1} W C^{-1} W^T D^{-1},\]

where \(C\) is the capacitance matrix \(I + W^T D^{-1} W\), to compute the squared Mahalanobis distance \(x^T (W W^T + D)^{-1} x\).

Parameters

• F –

• W – (…, dim, rank)

• D – (…, dim)

• capacitance_tril – (…, rank, rank)

• x – (…, dim)