Metadata revisions for hasty-hamiltonian-1.3.2

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No. Time User SHA256
-r1 (hasty-hamiltonian-1.3.2-r1) 2019-09-13T23:56:34Z JaredTobin 5f80b901dc97a49b0938916fcd64346b9020b82c937de76ffde30ee3e31f08cd
  • Changed description from

    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 <https://hackage.haskell.org/package/ad 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
    to
    Gradient-based traversal through parameter space.
    
    This implementation of the 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 <https://hackage.haskell.org/package/ad 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

-r0 (hasty-hamiltonian-1.3.2-r0) 2018-03-14T22:05:04Z JaredTobin a2be41f2454bb04a56098e761314e63c3c84cefe9d69b65afc9f6733b1a8ff00