hasty-hamiltonian: Speedy traversal through parameter space.
Gradient-based traversal through parameter space.
This implementation of HMC algorithm uses lens
as a means to operate over
generic indexed traversable functors, so you can expect it to work if your
target function takes a list, vector, map, sequence, etc. as its argument.
If you don't want to calculate your gradients by hand you can use the handy ad library for automatic differentiation.
Exports a mcmc
function that prints a trace to stdout, as well as a
hamiltonian
transition operator that can be used more generally.
import Numeric.AD (grad) import Numeric.MCMC.Hamiltonian target :: RealFloat a => [a] -> a target [x0, x1] = negate ((x0 + 2 * x1 - 7) ^ 2 + (2 * x0 + x1 - 5) ^ 2) gTarget :: [Double] -> [Double] gTarget = grad target booth :: Target [Double] booth = Target target (Just gTarget) main :: IO () main = withSystemRandom . asGenIO $ mcmc 10000 0.05 20 [0, 0] booth
Downloads
- hasty-hamiltonian-1.1.0.tar.gz [browse] (Cabal source package)
- Package description (as included in the package)
Maintainer's Corner
For package maintainers and hackage trustees
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Versions [RSS] | 1.1.0, 1.1.1, 1.1.2, 1.1.3, 1.1.4, 1.1.5, 1.2.0, 1.3.0, 1.3.2, 1.3.3, 1.3.4 |
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Dependencies | base (<5), ghc-prim, lens, mcmc-types (>=1.0.1), mwc-probability (>=1.0.1), pipes, primitive, transformers [details] |
License | MIT |
Author | Jared Tobin |
Maintainer | jared@jtobin.ca |
Category | Numeric |
Home page | http://jtobin.github.com/hasty-hamiltonian |
Source repo | head: git clone http://github.com/jtobin/hasty-hamiltonian.git |
Uploaded | by JaredTobin at 2015-10-08T08:22:52Z |
Distributions | LTSHaskell:1.3.4, NixOS:1.3.4, Stackage:1.3.4 |
Reverse Dependencies | 1 direct, 1 indirect [details] |
Downloads | 6980 total (2 in the last 30 days) |
Rating | (no votes yet) [estimated by Bayesian average] |
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Status | Docs available [build log] Last success reported on 2015-10-12 [all 1 reports] |