Structural Bioinformatics Library
Template C++ / Python API for developping structural bioinformatics applications.

Authors: F. Cazals and A. Chevallier and S. Pion
Sampling a high dimensional distribution is a very challenging problem, in particular when the target distribution allocates its mass on a typical set of small dimension.
One efficient solution to deal with such cases is Hamiltonian Monte Carlo (HMC) [20] and [148] .
Based on a generic algorithm able to sample an isotropic Gaussian distribution defined on a bounded open set [55], this package proposes an implementation for the particular case of a polytope and a Gaussian distribution target distribution .
In a nutshell, the proposed method goes as follows:
To sample a distribution in a high dimensional space, HMC defines a Markov chain in phase space , with coordinates for the position , and coordinates for the velocity .
As the name suggests, HMC is governed by an ODE system defined by Hamiltonâ€™s equations. In a nutshell, a HMC step involves three steps which are (i) picking a random velocity p, (ii) traveling deterministically following Hamiltonian dynamic, and (iii) projecting down in configuration space.
A variant/extension of this algorithm was proposed in [55] , where reflexion on the boundary of the domain are used.
In our case, the distribution is a Gaussian centered at the origin of variance , and exact trajectories are known for HMC.
In the sequel, we consider a polytope defined by linear inequalities, that is
For the particular case of a polytope , reflexions are easily obtained from the intersection points with the hyperplanes bounding . This flow, illustrated on Fig. fighmcstep, is denoted as follow:
The overall algorithm is sketched on Fig. figalgo.
The HMC algorithm: example trajectory staring at and ending at 
The HMC algorithm with reflexions on the boundary of the domain , taken to be a polytope in this package. 
In key issue in implementing geometric algorithms is robustness, as rounding operations may trigger erroneous evaluations and branching decisions.
As detailed in [55] , numerical robustness is ensured using multiprecision calculations, based on
This package provides the following program sblHMCpolytopesampler.exe, whose main inputs are as follows:
Target Gaussian distribution : variance of the isotropic Gaussian. Note that we always assume that the Gaussian is centered at the origin.
Polytope specification as a linear system: CSV files for and to define the polytope . (Nb: it is assumed that previous systems defines a bounded domain.)
Starting point:
Ff no starting point provided, uses the origin as a default. Recall, though, that this point is expected to be in the interior of the convex.
The software package is divided into 3 major components:
HMC internal. The function SBL::GT::T_HMC_internal is a core function of this package: it computes the flow (see the algorithm). However, it simply stops if the maximum number of reflection is reached, and it is up to the caller of the function to discard the result (this is done by the Polytope sampler class). Furthermore, this function takes a template parameter which describe the number type used and the associated functions required by SBL::GT::T_HMC_internal.
Kernels: double precision and multiprecision. Two kernels define all the necessary components required by HMC internal (such as function as cos, sin, etc) for different number types. These kernels are:
Polytope sampler. SBL::GT::T_HMC_polytope_sampler is the class that should be used by users that still wishes to use C++ directly. It's constructor takes a CSV file for and , the variance of the Gaussian, and an optional CSV file for the starting point. If no file is provided for the starting point, the point is used instead.
As noted above, numerical robustness is ensured using multiprecision calculations, based on the iRRAM library.
The src directory of the package contains a CMakeList.txt file, which assume that iRRAM is installed in a directory referenced by the environment variable IRRAM_DIR. The pointed directory must contain subdirectories lib and include.
To generate the executable sblHMCpolytopesampler.exe, one proceeds as follows:
cd sbl/Core/Hamiltonian_Monte_Carlo/src/Hamiltonian_Monte_Carlo mkdir build cmake .. make
The sampler can also be used from MATLAB. The corresponding code is provided in the directory Hamiltonian_Monte_Carlo/matlab .
To begin with, the program must be compiled using a MEX file. Open MATLAB in the directory Hamiltonian_Monte_Carlo/matlab and execute the following command to compile the MEX file:
mex HMC_Matlab.cpp CXXFLAGS="\$CXXFLAGS std=c++17" I${IRRAM_DIR}/include L${IRRAM_DIR}/lib liRRAM lmpfr lgmp
To use the MEX function, copy HMC_Matlab.mex* and libiRRAM.so to your working directory. The MEX function takes the following arguments (in this order)
The following example shows how to sample a Cube in dimension 2.
%general parameters dim=2; a = 1; travel_time = 1.5; num_steps = 1000; %starting point in a corner x0 = zeros(dim,1); for i = 1:dim x0(i) = 0.99; end %hyperplanes defining a cube A = [1 0; 1 0 ; 0 1; 0 1]; b = [1;1;1;1]; %saving results here x_HMC = zeros(num_steps,dim); %main loop x_ = x0; for i =1:num_steps u_ = randn(dim,1); [x_,u_] = HMC_Matlab(x_,u_,A,b,a,travel_time,100); x_HMC(i,:) = x_; end %plot result scatter(x_HMC(:,1),x_HMC(:,2));
The linear systems involves one equation per face, whence the following:
1,0,0 0,1,0 0,0,1 1,0,0 0,1,0 0,0,1
1 1 1 1 1 1
In this case, we need four equation:
1,0,0 0,1,0 0,0,1 1,1,1
0 0 0 1
0.001 0.001 0.001
See the following jupyter demos:
# http://www.open3d.org/docs/release/tutorial/Basic/pointcloud.html
# http://www.open3d.org/docs/release/tutorial/Basic/working_with_numpy.html
# http://www.open3d.org/docs/0.8.0/tutorial/Basic/visualization.html
import os
import numpy as np
import open3d as o3d
from open3d import JVisualizer
from SBL_pytools import SBL_pytools as sblpyt
# enclosing cube
def line_set_cube(a):
points = [[a, a, a], [a, a, a], [a, a, a], [a, a, a], [a, a, a], [a, a, a], [a, a, a], [a,a,a] ]
lines = [[0, 1], [0, 2], [1, 3], [2, 3], [4, 5], [4, 6], [5, 7], [6, 7], [0, 4], [1, 5], [2, 6], [3, 7]]
colors = [[1, 0, 0] for i in range(len(lines))]
line_set = o3d.geometry.LineSet()
line_set.points = o3d.utility.Vector3dVector(points)
line_set.lines = o3d.utility.Vector2iVector(lines)
line_set.colors = o3d.utility.Vector3dVector(colors)
return line_set
# enclosing cube
def line_set_stdsimplex(a):
points = [[0,0,0], [a, 0, 0], [0, a, 0], [0,0,a] ]
lines = [[0, 1], [0, 2],[0,3],[1,2],[1,3],[2,3]]
colors = [[1, 0, 0] for i in range(len(lines))]
line_set = o3d.geometry.LineSet()
line_set.points = o3d.utility.Vector3dVector(points)
line_set.lines = o3d.utility.Vector2iVector(lines)
line_set.colors = o3d.utility.Vector3dVector(colors)
return line_set
def process_case(model_name, dimension, sigma, num_samples, starting_point_file=None):
A_file = "data/%s_dim%s_A.csv" % (model_name, dimension)
b_file = "data/%s_dim%s_b.csv" % (model_name, dimension)
ofname = "%sdim%snumsamples%s.xyz" % (model_name, dimension, num_samples)
if (not os.path.isfile(A_file)) or (not os.path.isfile(b_file)):
print("Check A and b files; exiting"); return;
# generate the samples
cmd = "sblHMCpolytopesampler.exe A %s b %s n %s o %s" % (A_file, b_file, num_samples, ofname)
cmd += " sigma %s" % sigma
if starting_point_file:
cmd += " starting_pt_file %s" % starting_point_file
print(("Running %s" % cmd))
os.system(cmd)
print("Done")
if model_name == "cube":
line_set = line_set_cube(1)
elif model_name == "stdsimplex":
line_set = line_set_stdsimplex(1)
# generate the samples and directly visualize them
if dimension == 3:
pcd = o3d.io.read_point_cloud(ofname)
#print(pcd)
#print(np.asarray(pcd.points))
o3d.visualization.draw_geometries([pcd,line_set])
# generate the samples and pick the first three coordinates
elif dimension > 3:
data = np.loadtxt(ofname, usecols=[0,1,2])
#print(data)
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector(data)
o3d.visualization.draw_geometries([pcd,line_set])
To illustrate the role of the Gaussian width, we successively process two cases, namely $\sigma = 1$, and $\sigma = 0.1$. Static pictures also feature the enclosing cube.
process_case("cube", 3, 1, 10000)
sblpyt.show_image("fig/cubedim3sig1n10000.png")
process_case("cube", 3, 0.1, 10000)
sblpyt.show_image("fig/cubedim3sig0dot1n10000.png")
To illustrate the role of the Gaussian width, we successively process two cases, namely $\sigma = 1$, and $\sigma = 0.1$ Static pictures also feature the enclosing simplex.
process_case("stdsimplex", 3, 1, 10000, "data/stdsimplex_dim3_p.csv")
sblpyt.show_image("fig/stdsimplexdim3sig1n10000.png")
process_case("stdsimplex", 3, 0.1, 10000, "data/stdsimplex_dim3_p.csv")
sblpyt.show_image("fig/stdsimplexdim3sig0dot1n10000.png")
process_case("cube", 10, 1, 10000)
sblpyt.show_image("fig/cubedim10sig1n10000.png")