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| def | entropy (self) |
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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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def | set_default_validate_args (value) |
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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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Definition at line 5 of file exp_family.py.
| def torch.distributions.exp_family.ExponentialFamily.entropy |
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self | ) |
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Method to compute the entropy using Bregman divergence of the log normalizer.
Definition at line 49 of file exp_family.py.
The documentation for this class was generated from the following file: