Improve predictive samples #95
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runame
approved these changes
Apr 9, 2022
laplace/baselaplace.py
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| @@ -525,6 +534,10 @@ def __call__(self, x, pred_type='glm', link_approx='probit', n_samples=100): | |||
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| if link_approx not in ['mc', 'probit', 'bridge']: | |||
| raise ValueError(f'Unsupported link approximation {link_approx}.') | |||
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| if generator is not None: | |||
| if type(generator) is not torch.Generator or generator.device != x.device: | |||
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Why not just use not isinstance(generator, torch.Generator)?
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| def test_diagonal_normal_samples(): |
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Is it possible to test against torch Normal/MultivariateNormal samples by using the same generator state or is the implementation not the same?
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Checked it, unfortunately not. But the statistics match when taking many samples.
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Addresses #93 and #91 with following changes:
torch.Generator(Smooth samples #91)