The hasty-hamiltonian package

[ Tags: library, mit, numeric ] [ Propose Tags ]

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, a chain function for collecting results in memory, and 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

Properties

Versions 1.1.0, 1.1.1, 1.1.2, 1.1.3, 1.1.4, 1.1.5, 1.2.0, 1.3.0
Dependencies base (>=4 && <6), kan-extensions (==5.*), lens (==4.*), mcmc-types (>=1.0.1), mwc-probability (>=1.0.1), pipes (==4.*), primitive (>=0.5 && <1.0), transformers (>=0.5 && <1.0) [details]
License MIT
Author Jared Tobin
Maintainer jared@jtobin.ca
Category Numeric
Home page http://github.com/jtobin/hasty-hamiltonian
Source repository head: git clone http://github.com/jtobin/hasty-hamiltonian.git
Uploaded Wed Dec 21 21:15:12 UTC 2016 by JaredTobin
Distributions LTSHaskell:1.3.0, NixOS:1.3.0, Stackage:1.3.0, Tumbleweed:1.3.0
Downloads 603 total (20 in the last 30 days)
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Status Docs available [build log]
Last success reported on 2016-12-21 [all 1 reports]
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