Monthly Downloads: 8
Programming language: Haskell
License: BSD 3-clause "New" or "Revised" License
Tags: Math    
Latest version: v1.5

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Boltzmann Brain Build Status Hackage License

Boltzmann Brain is a Haskell library and standalone application meant for random generation of combinatorial structures. Using an easy and intuitive context-free text input representing a combinatorial specification of rational or algebraic objects, Boltzmann Brain allows its users to:

  • sample random structures following the given input specification;
  • visualize sampled objects using the GraphViz graph visualization software, and finally
    • compile a self-contained, dedicated analytic sampler for optimal sampling efficiency.

Remarkably, using Boltzmann Brain it is possible to control the outcome distribution of generated objects and, in particular, skew it to one needs. You provide the target distribution, we handle the sampling process for you!

If you can specify it, you can sample it!

Random tree

  1. Overview
  2. Sampler tuning
  3. Basic usage
  4. Advanced usage
  5. Installation
  6. References

Citing Boltzmann Brain

If you use Boltzmann Brain or its components for published work, we encourage you to cite the accompanying paper:

Maciej Bendkowski, Olivier Bodini, Sergey Dovgal

Polynomial tuning of multiparametric combinatorial samplers


Boltzmann Brain (bb for short) is an open-source analytic sampler compiler. Given a textual representation of the combinatorial system, bb constructs a dedicated analytic sampler. The sampler itself is a self-contained, and reusable Haskell module which, by construction, is guaranteed to sample random objects following the given, feasible target distribution. Using state-of-the-art optimization techniques, bb compiles an efficient sampler implementation which can be further modified, incorporated in other software or used as a standalone module.

The input specification format mimics that of Haskell algebraic data types where in addition each type constructor may be annotated with an additional weight parameter. For instance:

-- Motzkin trees
MotzkinTree = Leaf
            | Unary MotzkinTree (2)
            | Binary MotzkinTree MotzkinTree.

In the above example, a MotzkinTree data type is defined. It contains three constructors:

  • a constant Leaf of weight one (default value if not annotated);
    • a unary Unary constructor of weight two, and
    • a binary contructor Binary of default weight one.

The definition ends with an obligatory dot.

Each definition constitutes an algebraic data type where each inhabitant has an intrinsic size, defined as the sum of all its building constructor weights.

Given a system of (possibly) mutually recursive types, Boltzmann Brain automatically detects the specification kind (either rational or algebraic) and constructs a singular, rejection-based analytic sampler able to sample uniformly random, conditioned on size, inhabitants of the system types. Though the exact size of the outcome is a random variable, the outcome sampler allows to control the desired lower and upper bounds of the generated objects.

Sampler tuning

Boltzmann Brain supports a target frequency calibration using convex optimisation techniques. These are implemented as a Python library Paganini built using cvxpy. Boltzmann Brain communicates with Paganini through a tiny executable called medulla (see the medulla subdirectory). Consider the following example of a specification defining Motzkin trees with some arbitrary size notion:

-- Motzkin trees
MotzkinTree = Leaf
            | Unary MotzkinTree (2) [0.3]
            | Binary MotzkinTree MotzkinTree (2).

Here, the Unary construct is given weight 2 and a target frequency of 0.3. In consequence, the system is to be tuned such that the Unary node contributes, on average, 30% of the total size of constructed Motzkin trees. It is hence possible to distort the natural frequency of each constructor in the given system.

Note however, such an additional non-trivial tuning procedure causes a not insignificant change in the underlying probability model. In extreme cases, such as for instance requiring 80% of internal nodes in plane binary trees, the constructed sampler might be virtually ineffective due to the sparsity of tuned structures.

Please tune with caution!

Basic usage

For standard help/usage hints type bb -h (or paganini -h). For more advanced options, Boltzmann Brain provides its own annotation system (see example below):

-- Motzkin trees
@module    Sampler
@precision 1.0e-12
@maxiter   30

@withIO    y
@withLists y
@withShow  y

M = Leaf
  | Unary M [0.3]
  | Binary M M.

The @module annotation controls the name of the generated Haskell module (it defaults to Sampler if not explicitly given). Next two annotations @precision and @maxiter are parameters passed to Paganini and control the quality of the tuning procedure. If not provided, some reasonable default values are assumed (depending on the detected system type). The last three parameters control some additional parameters used while generating the sampler code. Specifically, whether to generate addtional IO (input/output) generators, whether to generate list samplers for each type in the system, and finally whether to include deriving Show clauses for each type in the system. By default, @withIO and @withShow are enabled (to disable them, set them to n or no); @withLists is by default disabled if not stated otherwise in the input specification.

Using annotations it is also possible to control the sampling parameters of Boltzmann Brain . Consider the following example:

-- Random sampling of Motzkin trees

-- Parameters for "tuning"
@precision 1.0e-12
@maxiter   30

-- Sampling parameters
@lowerBound 100
@upperBound 10000
@generate   M

M = Leaf
  | Unary M [0.3]
  | Binary M M.

In the above example, three more annotations are used. The first two dictate the admissible size window of the generated structures whereas the third one specifies the type from which we want to generate. If no bounds are provided, Boltzmann Brain uses some (small) default ones. If no @generate annotation is provided, Boltzmann Brain assumes some default type.

Advanced usage

For parameter tuning, Boltzmann Brain invokes the external medulla script. Usually, Boltzmann Brain automatically calls medulla once tuning is in order. If no special handling is required, it just suffices to have paganini available in the system; Boltzmann Brain will automatically pass it necessary data and retrieve the tuning data.

However, it needed, a manual tuning workflow is also supported. To tune a combinatorial specification "by hand", you can start with generating a Paganini representation of the system, e.g. using

bb spec -i specification.in -o specification.out.

Boltzmann Brain ensures that the input specification is sound and well-founded. Otherwise, (arguably) user-friendly error messages are provided. Once the Paganini specification is generated, we type

medulla -i paganini.pg > bb.param

which runs Paganini and outputs the required tuning data for bb. You can alter the default agruments of paganini scripts such as tuning precision, optimisation problem solver, maximum number of iterations, and explicitly specify if the type of the grammar is rational (for rational specifications the optimization problem might become unbounded).

Finally, we need to tell bb to use the tuning data typing, e.g.:

bb compile -o Sampler.hs -t bb.param -i specification.in


Boltzmann Brain consists of two executables, bb and medulla. The former one is implemented in Haskell whereas the latter is implemented in Python. Both applications rely on some (few) external libraries to work, such as LAPACK or BLAS. The following sections explain several common installation methods.

We start with the recommended method of compiling Boltzmann Brain from sources. The following section explains the compilation process under Ubuntu 16.04, however, let us note that with little modifications it should also work under other Linux distributions.

Linux (Ubuntu 16.04 or newer)

We start with installing all the required system dependencies:

apt-get update
apt-get install -y cmake curl git libblas-dev liblapack-dev python3 python3-pip

The above script installs BLAS and LAPACK as well as python3 and its package manager pip. Since the target destination of both bb and medulla is going to be ~/.local/bin, before proceeding please make sure that it is included in your PATH.

Next, we install required python dependencies

 pip3 install --user --upgrade pip
 pip3 install --user numpy scipy
 pip3 install --user cvxpy
 pip3 install --user paganini

Note that the above packages play the central role in the system tuning procedure. Without them, medulla cannot not work properly. Next, we clone the current repository

git clone https://github.com/maciej-bendkowski/boltzmann-brain.git

and install medulla:

cd boltzmann-brain/medulla
python3 setup.py install --user --prefix=

We can check that medulla is installed by typing

medulla -h

If medulla is available, we should see a help/usage message.

Finally, we have to prepare to install bb. For that purpose, we are going to download Haskell's Stack tool chain. Note that stack is able to download and isolate various GHC (Glasgow Haskell Compiler) instances, avoiding the infamous Cabal hell.

To install stack for linux x86_64 type

curl -L https://get.haskellstack.org/stable/linux-x86_64.tar.gz | tar xz --wildcards --strip-components=1 -C ~/.local/bin '*/stack'

Now, all we need is to go back to the main boltzmann-brain folder and use stack install:

stack setup --resolver=lts-14.16
stack install happy --resolver=lts-14.16
stack install
Nix support (linux / macOS)

nix is a package manager that works under linux and macos. Support for installing dependencies and a assembling a development environment for building and running boltzmann-brain is provided by the shell.nix file in the top-level directory. To use it, you need to have nixpkgs installed already. Then you can install boltzmann-brain as follows:

git clone https://github.com/maciej-bendkowski/boltzmann-brain.git
nix-shell # Enters a shell which provides GHC, python, paganini, and installs medulla all in one step. 

That's it!

Now you can build bb and then run an example. For instance:

cabal build # compile and build bb 
cabal run bb -- sample -i examples/algebraic/boolean.alg


  • Nix support was tested under nixos with ghc 8.6.5 and cabal-install 3. Earlier versions of GHC may be difficult to get working due to changes in package dependencies. As of 2019-12-05 nix support requires a recent version of the nixpkgs repostory (e.g. nixpkgs-unstable).

  • Additional nix derivations are currently (temporarily) needed to support cvxpy. After adding them to nixpkgs, you can follow the previously given instructions. Those derivations can be found in https://github.com/teh/nixpkgs/tree/cvxpy. If you mangage nixpkgs by using git, then one way to get cvxpy is to go your nixpkgs directory and enter:

git remote add teh [email protected]:teh/nixpkgs.git
git checkout -b cvxpy teh/cvxpy
git rebase nixpkgs-unstable
  • Once support for cxvpy is mainstreamed into nixpkgs, step 3 will become unnecessary. Perhaps by 2020-03, at the latest.

  • If you get nixpkgs updates via nix channels then a custom overlay will be needed (not provided here).

macOS >= 10.12

In the following section we explain how to compile Boltzmann Brain in OSX.

  • First, you need python to be installed (both versions 2 or 3 should be fine). If you don't have python installed, you can find it at https://wiki.python.org/moin/BeginnersGuide/Download.

  • Using the dedicated python package manager pip you can install the paganini framework

    pip install paganini

    Normally this works, but if some packages are outdated, consider upgrading them.

  • The Boltzmann Brain package bb can be installed with package manager homebrew

    brew tap electric-tric/bb
    brew install boltzmann-brain

    If you don't have homebrew, you can download the binary file from our releases webpage.


On Mac OS older versions like 10.9 package managers like brew can only install stack from source because the binaries are not maintained. This takes a long time. In some cases it is faster to completely update the operating system before attempting to install some of the prerequisites.

  • For some versions of python, the package manager pip doesn't come by default. pip is already installed if you use python2 >= 2.7.9 or python3 >= 3.4. Otherwise see instructions.

  • If some of your packages, for example numpy are installed but outdated, the installation process sometimes gives an error. For such packages try

    pip install --upgrade numpy sympy cvxpy
  • The package manager pip should not be used with sudo. If you don't have the right to write to some specific directory, try adding --user flag, for example

    pip install --user paganini
  • Within python, several additional packages should be installed. Normally pip handles the dependencies, so you just need to execute

    pip install paganini

    When you launch medulla, the program tells you the list of packages that are missing. In order to install the packages, type into the command line

    pip install six cvxpy numpy sympy

    Note that the last two packages come by default with Scientific Computing Tools for Python

  • The hmatrix package in Haskell requires prominent linear algebra packages LAPACK and BLAS (which are sometimes called "one of the achievements of the human species"). For some systems these packages are already included, but if they are not, you can follow the instructions on the official website.

For more help on installation, please consult also our Travis CI tool chain.

Pre-compiled binaries

We use Travis CI in order to build bb for Linux and OSX, both in the x86_64 architecture. Pre-compiled binaries of bb are available at out releases webpage.

Package managers

We offer Hackage. You can install it by typing

stack install boltzmann-brain


Boltzmann Brain heavily relies on published work of numerous excellent authors. Below, you can find a short (and definitely inexhaustive) list of papers on the subject:

*Note that all licence references and agreements mentioned in the boltzmann-brain README section above are relevant to that project's source code only.