RFF utils for GP function sampling #750
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This pull request was exported from Phabricator. Differential Revision: D25806553 |
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Summary: Pull Request resolved: meta-pytorch#750 see title Differential Revision: D25806553 fbshipit-source-id: cc84bec0a1ea8e693394857fa37dc76ac495ad6c
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This pull request was exported from Phabricator. Differential Revision: D25806553 |
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cc @saitcakmak. Let me know if you have any comments since you've previously worked with RFFs |
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Ltgm. Much cleaner than my version :)
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| weights = torch.randn( | ||
| input_dim, | ||
| num_rff_features, | ||
| dtype=base_kernel.lengthscale.dtype, | ||
| device=base_kernel.lengthscale.device, | ||
| ) |
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Just a thought: Would there be any benefit to using draw_sobol_normal_samples here?
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interesting thought. probably? night be worth looking into.
Summary: Pull Request resolved: meta-pytorch#750 see title Reviewed By: Balandat Differential Revision: D25806553 fbshipit-source-id: d6b276f8dae7f6e05ddafed11a33754d6ddb5547
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This pull request was exported from Phabricator. Differential Revision: D25806553 |
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This pull request has been merged in 7c73b6c. |
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Summary: see title
Differential Revision: D25806553