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def | __init__ (self, base_distribution, transforms, validate_args=None) |
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def | expand (self, batch_shape, _instance=None) |
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def | support (self) |
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def | has_rsample (self) |
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def | sample (self, sample_shape=torch.Size()) |
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def | rsample (self, sample_shape=torch.Size()) |
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def | log_prob (self, value) |
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def | cdf (self, value) |
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def | icdf (self, value) |
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def | __init__ (self, batch_shape=torch.Size(), event_shape=torch.Size(), validate_args=None) |
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def | expand (self, batch_shape, _instance=None) |
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def | batch_shape (self) |
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def | event_shape (self) |
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def | arg_constraints (self) |
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def | support (self) |
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def | mean (self) |
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def | variance (self) |
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def | stddev (self) |
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def | sample (self, sample_shape=torch.Size()) |
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def | rsample (self, sample_shape=torch.Size()) |
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def | sample_n (self, n) |
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def | log_prob (self, value) |
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def | cdf (self, value) |
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def | icdf (self, value) |
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def | enumerate_support (self, expand=True) |
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def | entropy (self) |
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def | perplexity (self) |
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def | __repr__ (self) |
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| arg_constraints |
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| has_rsample |
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| has_enumerate_support |
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| support |
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| arg_constraints |
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def | set_default_validate_args (value) |
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Definition at line 8 of file transformed_distribution.py.
def torch.distributions.transformed_distribution.TransformedDistribution.cdf |
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self, |
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value |
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) |
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Computes the cumulative distribution function by inverting the
transform(s) and computing the score of the base distribution.
Definition at line 132 of file transformed_distribution.py.
def torch.distributions.transformed_distribution.TransformedDistribution.icdf |
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self, |
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value |
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) |
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Computes the inverse cumulative distribution function using
transform(s) and computing the score of the base distribution.
Definition at line 145 of file transformed_distribution.py.
def torch.distributions.transformed_distribution.TransformedDistribution.log_prob |
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self, |
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value |
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) |
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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.
Definition at line 102 of file transformed_distribution.py.
def torch.distributions.transformed_distribution.TransformedDistribution.rsample |
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self, |
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sample_shape = torch.Size() |
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) |
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Generates a sample_shape shaped reparameterized sample or sample_shape
shaped batch of reparameterized samples if the distribution parameters
are batched. Samples first from base distribution and applies
`transform()` for every transform in the list.
Definition at line 90 of file transformed_distribution.py.
def torch.distributions.transformed_distribution.TransformedDistribution.sample |
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self, |
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sample_shape = torch.Size() |
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) |
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Generates a sample_shape shaped sample or sample_shape shaped batch of
samples if the distribution parameters are batched. Samples first from
base distribution and applies `transform()` for every transform in the
list.
Definition at line 77 of file transformed_distribution.py.
The documentation for this class was generated from the following file: