init

.gitignore

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+# Development files and python cache
+/*.pyc
+/*.egg
+/*.egg-info
+
+# Notebook checkpoints
+.ipynb_checkpoints
+
+# System
+.DS_Store
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*,cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+#Ipython Notebook
+.ipynb_checkpoints
+
+# Latex-log files:
+*.aux
+*.log
+*.out
+*.gz
+*.toc

.travis.yml

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+sudo: required
+dist: trusty
+language: python
+matrix:
+  include:
+    - python: 3.5
+notifications:
+  email: false
+addons:
+  apt_packages:
+    - pandoc
+before_install:
+  - "export DISPLAY=:99.0"
+  - "sh -e /etc/init.d/xvfb start"
+install:
+  - sudo apt-get install libopenblas-dev
+  - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh;
+  - bash miniconda.sh -b -p $HOME/miniconda
+  - export PATH="$HOME/miniconda/bin:$PATH"
+  - hash -r
+  - conda config --set always_yes yes --set changeps1 no
+  - conda update -q conda
+  - conda info -a
+  - conda create -q -n testenv python=$TRAVIS_PYTHON_VERSION ipython numpy scipy pytest matplotlib mkl mkl-service sphinx
+  - source activate testenv
+  - pip install travis-sphinx nbsphinx
+  - export PATH=$HOME/.local/bin:$PATH
+  - pip install pep8 pytest-pep8 python-coveralls pytest-cov
+  - pip install sphinxcontrib-napoleon sphinx_rtd_theme
+  - pip install theano
+  - echo -e "\n[blas]\nldflags = -lopenblas\n" >> ~/.theanorc
+  - python setup.py install
+script:
+  - PYTHONPATH=$PWD:$PYTHONPATH pytest --cov=delfi;
+  - travis-sphinx build -n -s docs/
+after_success:
+  - coveralls
+  - travis-sphinx deploy

LICENSE.txt

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+Modified work Copyright (c) 2017, Jan-Matthis Lueckmann, Pedro J. Goncalves, Jakob H. Macke
+Original work Copyright (c) 2016, George Papamakarios
+
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+The views and conclusions contained in the software and documentation are those
+of the authors and should not be interpreted as representing official policies,
+either expressed or implied, of anybody else.

README.md

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+# delfi
+
+delfi is a Python package for density estimation likelihood-free inference.
+
+**Important: The code in this repository is still experimental, and APIs are subject to change without warning.**
+
+
+## Documentation
+
+For installation instructions and getting started, an early-stage documentation is available
+at [http://www.mackelab.org/delfi/](http://www.mackelab.org/delfi/)
+
+
+## Status
+
+[![Build Status](https://travis-ci.org/mackelab/delfi.svg?branch=master)](https://travis-ci.org/mackelab/delfi) [![Docs](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](http://www.mackelab.org/delfi/) [![PyPI version](https://badge.fury.io/py/delfi.svg)](https://badge.fury.io/py/delfi)

delfi/__init__.py

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+from delfi.version import __version__, VERSION

delfi/distribution/BaseDistribution.py

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+import abc
+import numpy as np
+
+from delfi.utils.meta import ABCMetaDoc
+
+
+class BaseDistribution(metaclass=ABCMetaDoc):
+    """Abstract base class for distributions
+
+    Distributions must at least implement abstract properties and methods of
+    this class.
+
+    Parameters
+    ----------
+    ndim : int
+        Number of ndimensions of the distribution
+    seed : int or None
+        If provided, random number generator will be seeded
+    """
+    def __init__(self, ndim, seed=None):
+        self.ndim = ndim
+
+        self.seed = seed
+        if seed is not None:
+            self.rng = np.random.RandomState(seed=seed)
+        else:
+            self.rng = np.random.RandomState()
+
+    @abc.abstractproperty
+    def mean(self):
+        """Means"""
+        pass
+
+    @abc.abstractproperty
+    def std(self):
+        """Standard deviations of marginals"""
+        pass
+
+    @abc.abstractmethod
+    def eval(self, x, ii=None, log=True):
+        """Method to evaluate pdf
+
+        Parameters
+        ----------
+        x : int or list or np.array
+            Rows are inputs to evaluate at
+        ii : list
+            A list of indices specifying which marginal to evaluate.
+            If None, the joint pdf is evaluated
+        log : bool, defaulting to True
+            If True, the log pdf is evaluated
+
+        Returns
+        -------
+        scalar
+        """
+        pass
+
+    @abc.abstractmethod
+    def gen(self, n_samples=1):
+        """Method to generate samples
+
+        Parameters
+        ----------
+        n_samples : int
+            Number of samples to generate
+
+        Returns
+        -------
+        n_samples x self.ndim
+        """
+        pass
+
+    def gen_newseed(self):
+        """Generates a new random seed"""
+        if self.seed is None:
+            return None
+        else:
+            return self.rng.randint(0, 2**31)

delfi/distribution/Discrete.py

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+import numpy as np
+
+from delfi.distribution.BaseDistribution import BaseDistribution
+
+
+class Discrete(BaseDistribution):
+    def __init__(self, p, seed=None):
+        """Discrete distribution
+
+        Parameters
+        ----------
+        p : list or np.array, 1d
+            Probabilities of elements, must sum to 1
+        seed : int or None
+            If provided, random number generator will be seeded
+        """
+        super().__init__(ndim=1, seed=seed)
+
+        p = np.asarray(p)
+        assert p.ndim == 1, 'p must be a 1-d array'
+        assert np.isclose(np.sum(p), 1), 'p must sum to 1'
+        self.p = p
+
+    @property
+    def mean(self):
+        """Means"""
+        pass
+
+    @property
+    def std(self):
+        """Standard deviations of marginals"""
+        pass
+
+    @copy_ancestor_docstring
+    def eval(self, x, ii=None, log=True):
+        raise NotImplementedError("To be implemented")
+
+    @copy_ancestor_docstring
+    def gen(self, n_samples=1, seed=None):
+        # See BaseDistribution.py for docstring
+        c = np.cumsum(self.p[:-1])[np.newaxis, :]  # cdf
+        r = self.rng.rand(n_samples, 1)
+        return np.sum((r > c).astype(int), axis=1).reshape(-1, 1)

delfi/distribution/Gamma.py

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+import numpy as np
+from scipy.stats import gamma
+from delfi.distribution.BaseDistribution import BaseDistribution
+
+
+class Gamma(BaseDistribution):
+    def __init__(self, alpha=1., beta=1., seed=None):
+        """Univariate (!) Gamma distribution
+
+        Parameters
+        ----------
+        alpha : list, or np.array, 1d
+            Shape parameters
+        beta : list, or np.array, 1d
+            inverse scale paramters
+        seed : int or None
+            If provided, random number generator will be seeded
+        """
+        super().__init__(ndim=1, seed=seed)
+
+        alpha, beta = np.atleast_1d(alpha), np.atleast_1d(beta)
+        assert alpha.ndim == 1, 'alpha must be a 1-d array'
+        assert alpha.size == beta.size, 'alpha and beta must match in size'
+        assert np.all(alpha > 0.), 'Should be greater than zero.'
+        assert np.all(beta > 0.), 'Should be greater than zero.'
+        self.alpha = alpha
+        self.beta = beta
+        self._gamma = gamma(a=alpha, scale=1./beta)
+
+    @property
+    def mean(self):
+        """Means"""
+        return self.alpha / self.beta
+
+    @property
+    def std(self):
+        """Standard deviations of marginals"""
+        return np.sqrt( self.alpha ) / self.beta
+
+    @copy_ancestor_docstring
+    def eval(self, x, ii=None, log=True):
+        # univariate distribution only, i.e. ii=[0] in any case
+        return self._gamma.logpdf(x) if log else self._gamma.pdf(x)
+
+    @copy_ancestor_docstring
+    def gen(self, n_samples=1, seed=None):
+        # See BaseDistribution.py for docstring
+
+        x = self.rng.gamma(shape=self.alpha, 
+                           scale=1./self.beta, 
+                           size=(n_samples, self.ndim))
+        return x