Added Latin Hypercube sampling #216
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added chebyshev2-noisy method for generators |
shuheng-liu
requested changes
May 14, 2024
shuheng-liu
reviewed
May 15, 2024
neurodiffeq/generators.py
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| elif method == 'latin-hypercube': | ||
| x = _latin_hypercube(xy_min[0], xy_max[0], grid[0]) | ||
| y = _latin_hypercube(xy_min[1], xy_max[1], grid[1]) | ||
| x = lambda: _latin_hypercube(xy_min[0], xy_max[0], grid[0]) |
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I don't think we want to assign x and y as functions. The following line calling torch.meshgrid(x, y, indexing='ij') will likely fail, unless there's some hacky trick in pytorch I'm not familiar with.
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Done as suggested!
shuheng-liu
reviewed
May 15, 2024
neurodiffeq/generators.py
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| elif method == 'latin-hypercube': | ||
| self.examples = _latin_hypercube(t_min, t_max, size) | ||
| self.getter = lambda: self.examples | ||
| self.examples = lambda: _latin_hypercube(t_min, t_max, size) |
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If we don't want to return static (fixed) points every time self.getter() is called, we can combine both lines into
self.getter = lambda: _latin_hypercube(t_min, t_max, size)and skip defining self.examples (or assigning it a dummy value for consistency). It's confusing to see self.examples as a function.
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Removed self.examples
shuheng-liu
requested changes
May 15, 2024
shuheng-liu
approved these changes
May 23, 2024
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