TransformedMQF2Distribution#

class pytorch_forecasting.metrics._mqf2_utils.TransformedMQF2Distribution(base_distribution: MQF2Distribution, transforms: list[AffineTransform], validate_args: bool = False)[source]#

Bases: TransformedDistribution

Inherited-members:

Methods

`cdf`(value)	Computes the cumulative distribution function by inverting the transform(s) and computing the score of the base distribution.
`energy_score`(y)
`entropy`()	Returns entropy of distribution, batched over batch_shape.
`enumerate_support`([expand])	Returns tensor containing all values supported by a discrete distribution.
`expand`(batch_shape[, _instance])	Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to batch_shape.
`icdf`(value)	Computes the inverse cumulative distribution function using transform(s) and computing the score of the base distribution.
`log_prob`(y)	Scores the sample by inverting the transform(s) and computing the score using the score of the base distribution and the log abs det jacobian.
`perplexity`()	Returns perplexity of distribution, batched over batch_shape.
`quantile`(alpha[, hidden_state])
`repeat_scale`(scale)
`rsample`([sample_shape])	Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples if the distribution parameters are batched.
`sample`([sample_shape])	Generates a sample_shape shaped sample or sample_shape shaped batch of samples if the distribution parameters are batched.
`sample_n`(n)	Generates n samples or n batches of samples if the distribution parameters are batched.
`scale_input`(y)
`set_default_validate_args`(value)	Sets whether validation is enabled or disabled.

Attributes

`arg_constraints`
`batch_shape`	Returns the shape over which parameters are batched.
`event_shape`	Returns the shape of a single sample (without batching).
`has_enumerate_support`
`has_rsample`	bool(x) -> bool
`mean`	Returns the mean of the distribution.
`mode`	Returns the mode of the distribution.
`stddev`	Returns the standard deviation of the distribution.
`support`	Returns a `Constraint` object representing this distribution's support.
`variance`	Returns the variance of the distribution.

log_prob(y: Tensor) → Tensor[source]#: Scores the sample by inverting the transform(s) and computing the score using the score of the base distribution and the log abs det jacobian.