Implements Look Ahead Hamiltonian Monte Carlo (LAHMC) and standard Hamiltonian Monte Carlo (HMC) in both Python and MATLAB.
LAHMC is described in the paper:
Sohl-Dickstein, Jascha and Mudigonda, Mayur and DeWeese, Michael R.
Hamiltonian Monte Carlo Without Detailed Balance.
International Conference on Machine Learning. 2014
http://arxiv.org/abs/1409.5191
The following code draws samples from an isotropic Gaussian distribution using LAHMC.
from LAHMC import LAHMC
import numpy as np
# Define the energy function and gradient
def E(X, sigma=1.):
""" Energy function for isotropic Gaussian """
return np.sum(X**2, axis=0).reshape((1,-1))/2./sigma**2
def dEdX(X, sigma=1.):
""" Energy function gradient for isotropic Gaussian """
return X/sigma**2
# Initialize the sample locations -- 2 dimensions, 100 particles
Xinit = np.random.randn(2,100)
# initialize the sampler.
sampler = LAHMC(Xinit, E, dEdX, epsilon=0.1, beta=0.1, kwargs={'sigma':0.1})
# perform 10 sampling steps for all 100 particles
X = sampler.sample(num_steps = 10)
# perform another 10 sampling steps
X = sampler.sample(num_steps = 10)More detailed documentation, and additional options, can be found in python/LAHMC.py
The following code draws samples from an isotropic Gaussian distribution using LAHMC.
% opts holds all parameters which will be passed to the sampler
opts = [];
opts.epsilon = 0.1;
opts.beta = 0.1;
% number of sampling steps
opts.T = 10;
% energy function and gradient
opts.E = @E_gauss;
opts.dEdX = @dEdX_gauss;
% state will hold the particle positions and velocities between
% sampler calls, as well as counters for the number of transitions
% and function evaluations
state = []
% Initialize sample locations -- 2 dimensions, 100 particles
opts.Xinit = randn(2,100);
% Gaussian coupling matrix expected by E_gauss and dEdX_gauss
J = eye(2)*100;
% perform 10 sampling steps for all 100 particles
[X, state] = LAHMC(opts, state, J);
% perform another 10 sampling steps
[X, state] = LAHMC(opts, state, J);More detailed documentation, and additional options, can be found in matlab/LAHMC.m.
Code reproducing Figure 2 and Table 1 of the paper, and demonstrating usage of the sampler, can be found in python/generate_figure_2.py and matlab/generate_figure_2.m. The exact plots appearing in the paper were generated using the MATLAB version of the code.