https://github.com/GPflow/GPflow
Revision 2d65476f02cee774de9221f1ac8780f4f9d4fa56 authored by John Bradshaw on 25 September 2017, 12:51:55 UTC, committed by John Bradshaw on 11 October 2017, 11:09:18 UTC
Many kernels correspond to finite feature mappings. Using these features is often useful as they scaled better as a function of number of data points (but badly in terms of sdcaling with feature dimension). * this commit introduces a feature mapping transform that can be obtained from some kernels. * for some kernels eg linear we implement the exact transform * the stationary kernels have feature approximations implemented using the random features approach of Rahmini & Recht * unit tests to check that these are working roughly correctly.
1 parent e534ceb
Tip revision: 2d65476f02cee774de9221f1ac8780f4f9d4fa56 authored by John Bradshaw on 25 September 2017, 12:51:55 UTC
Linearise kernels -- feature map transforms for kernels.
Linearise kernels -- feature map transforms for kernels.
Tip revision: 2d65476
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| gpflow | ||
| notebooks | ||
| testing | ||
| .coveragerc | -rw-r--r-- | 261 bytes |
| .coveralls.yml | -rw-r--r-- | 23 bytes |
| .gitignore | -rw-r--r-- | 793 bytes |
| .pylintrc | -rw-r--r-- | 14.6 KB |
| .travis.yml | -rw-r--r-- | 93 bytes |
| .travis.yml_copy | -rw-r--r-- | 843 bytes |
| Dockerfile | -rw-r--r-- | 1.1 KB |
| LICENSE | -rw-r--r-- | 11.1 KB |
| MANIFEST.in | -rw-r--r-- | 166 bytes |
| README.md | -rw-r--r-- | 5.1 KB |
| RELEASE.md | -rw-r--r-- | 3.5 KB |
| contributing.md | -rw-r--r-- | 4.1 KB |
| docs_require.txt | -rw-r--r-- | 404 bytes |
| roadmap.md | -rw-r--r-- | 506 bytes |
| run_tests.sh | -rwxr-xr-x | 896 bytes |
| setup.py | -rw-r--r-- | 2.5 KB |
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