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refine the docstring for hmc

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Zhenwen Dai 2014-11-03 12:18:04 +00:00
parent 6625e27fec
commit 74765d0e0d
4 changed files with 14 additions and 43 deletions
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24
GPy/inference/mcmc/hmc.py
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@ -1,22 +1,23 @@
"""HMC implementation"""
# ## Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
class HMC:
"""
An implementation of Hybrid Monte Carlo (HMC) for GPy models
An implementation of Hybrid Monte Carlo (HMC) for GPy models
Initialize an object for HMC sampling. Note that the status of the model (model parameters) will be changed during sampling.
:param model: the GPy model that will be sampled
:type model: GPy.core.Model
:param M: the mass matrix (an identity matrix by default)
:type M: numpy.ndarray
:param stepsize: the step size for HMC sampling
:type stepsize: float
"""
def __init__(self, model, M=None,stepsize=1e-1):
"""
Initialize an object for HMC sampling. Note that the status of the model (model parameters) will be changed during sampling
:param model: the GPy model that will be sampled
:type model: GPy.core.Model
:param M: the mass matrix (an identity matrix by default)
:type M: numpy.ndarray
:param stepsize: the step size for HMC sampling
:type stepsize: float
"""
self.model = model
self.stepsize = stepsize
self.p = np.empty_like(model.optimizer_array.copy())
@ -29,6 +30,7 @@ class HMC:
def sample(self, num_samples=1000, hmc_iters=20):
"""
Sample the (unfixed) model parameters.
:param num_samples: the number of samples to draw (1000 by default)
:type num_samples: int
:param hmc_iters: the number of leap-frog iterations (20 by default)