Introduce random_state arguments.

README.rst

@@ -39,7 +39,7 @@ The full documentation for the mixedvines package is available at
 Requirements
 ------------
 
-The package is compatible with Python 2.7 and 3.x and additionaly requires
+The package requires Python 3.4 or greater and additionaly requires
 `NumPy and SciPy
 <http://www.scipy.org/install.html>`_.
 
@@ -158,7 +158,7 @@ References
 License
 -------
 
-Copyright (C) 2017-2019 Arno Onken
+Copyright (C) 2017-2019, 2021 Arno Onken
 
 This file is part of the mixedvines package.
 

docs/conf.py

@@ -1,7 +1,6 @@
 # -*- coding: utf-8 -*-
 #
-# mixedvines documentation build configuration file, created by
-# sphinx-quickstart on Mon Apr 24 17:23:24 2017.
+# mixedvines documentation build configuration file.
 #
 # This file is execfile()d with the current directory set to its
 # containing dir.
@@ -68,17 +67,17 @@ def __getattr__(cls, name):
 
 # General information about the project.
 project = u'mixedvines'
-copyright = u'2017-2019 Arno Onken'
+copyright = u'2017-2019, 2021 Arno Onken'
 author = u'Arno Onken'
 
 # The version info for the project you're documenting, acts as replacement for
 # |version| and |release|, also used in various other places throughout the
 # built documents.
 #
 # The short X.Y version.
-version = u'1.2'
+version = u'1.3'
 # The full version, including alpha/beta/rc tags.
-release = u'1.2.2'
+release = u'1.3.0'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
@@ -152,7 +151,7 @@ def __getattr__(cls, name):
 # The name for this set of Sphinx documents.
 # "<project> v<release> documentation" by default.
 #
-# html_title = u'mixedvines v1.2.2'
+# html_title = u'mixedvines v1.3.0'
 
 # A shorter title for the navigation bar.  Default is the same as html_title.
 #

docs/index.rst

@@ -1,8 +1,3 @@
-.. mixedvines documentation master file, created by
-   sphinx-quickstart on Mon Apr 24 17:23:24 2017.
-   You can adapt this file completely to your liking, but it should at least
-   contain the root `toctree` directive.
-
 Welcome to mixedvines's documentation!
 ======================================
 
@@ -26,4 +21,3 @@ Indices and tables
 * :ref:`genindex`
 * :ref:`modindex`
 * :ref:`search`
-

docs/requirements.rst

@@ -2,6 +2,6 @@ Requirements
 ============
 
 
-The package is compatible with Python 2.7 and 3.x and additionaly requires
+The package requires Python 3.4 or greater and additionaly requires
 `NumPy and SciPy
 <http://www.scipy.org/install.html>`_.

mixedvines/__init__.py

@@ -1,5 +1,5 @@
 # -*- coding: utf-8 -*-
-# Copyright (C) 2017-2019 Arno Onken
+# Copyright (C) 2017-2019, 2021 Arno Onken
 #
 # This file is part of the mixedvines package.
 #
@@ -15,7 +15,6 @@
 #
 # You should have received a copy of the GNU General Public License along with
 # this program.  If not, see <http://www.gnu.org/licenses/>.
-from __future__ import absolute_import
 from . import marginal
 from . import copula
 from . import mixedvine

mixedvines/copula.py

@@ -1,5 +1,5 @@
 # -*- coding: utf-8 -*-
-# Copyright (C) 2017-2019 Arno Onken
+# Copyright (C) 2017-2019, 2021 Arno Onken
 #
 # This file is part of the mixedvines package.
 #
@@ -18,23 +18,14 @@
 '''
 This module implements copula distributions.
 '''
-from __future__ import division
 import sys
 import abc
 from scipy.optimize import minimize
-from scipy.stats import norm, uniform, mvn
+from scipy.stats import norm, uniform, multivariate_normal
 import numpy as np
 
 
-# Ensure abstract base class compatibility
-if sys.version_info[0] == 3 and sys.version_info[1] >= 4 \
-        or sys.version_info[0] > 3:
-    ABC = abc.ABC
-else:
-    ABC = abc.ABCMeta('ABC', (), {})
-
-
-class Copula(ABC):
+class Copula(abc.ABC):
     '''
     This abstract class represents a copula.
 
@@ -64,11 +55,11 @@ class Copula(ABC):
         Log of the cumulative distribution function.
     cdf(samples)
         Cumulative distribution function.
-    ccdf(samples, axis=1)
+    ccdf(samples, axis)
         Conditional cumulative distribution function.
-    ppcf(samples, axis=1)
+    ppcf(samples, axis)
         Inverse of the conditional cumulative distribution function.
-    rvs(size=1)
+    rvs(size=1, random_state)
         Generate random variates.
     estimate_theta(samples)
         Estimates the `theta` parameters from the given samples.
@@ -309,7 +300,7 @@ def __axis_wrapper(self, fun, samples, axis):
             Function to be called with `samples` as argument.
         samples : array_like
             n-by-2 matrix of samples where n is the number of samples.
-        axis : integer
+        axis : int
             The axis to condition the cumulative distribution function on.
 
         Returns
@@ -368,7 +359,7 @@ def ccdf(self, samples, axis=1):
         ----------
         samples : array_like
             n-by-2 matrix of samples where n is the number of samples.
-        axis : integer, optional
+        axis : int, optional
             The axis to condition the cumulative distribution function on.
             (Default: 1)
 
@@ -409,7 +400,7 @@ def ppcf(self, samples, axis=1):
         ----------
         samples : array_like
             n-by-2 matrix of samples where n is the number of samples.
-        axis : integer, optional
+        axis : int, optional
             The axis to condition the cumulative distribution function on.
             (Default: 1)
 
@@ -421,21 +412,27 @@ def ppcf(self, samples, axis=1):
         '''
         return self.__axis_wrapper(self._ppcf, samples, axis)
 
-    def rvs(self, size=1):
+    def rvs(self, size=1, random_state=None):
         '''
         Generates random variates from the copula.
 
         Parameters
         ----------
-        size : integer, optional
+        size : int, optional
             The number of samples to generate.  (Default: 1)
+        random_state : {None, int, RandomState, Generator}, optional
+            The random state to use for random variate generation.  `None`
+            corresponds to the `RandomState` singleton.  For an int, a new
+            `RandomState` is generated and seeded.  For a `RandomState` or
+            `Generator`, the object is used.  (Default: `None`)
 
         Returns
         -------
         samples : array_like
             n-by-2 matrix of samples where n is the number of samples.
         '''
-        samples = np.stack((uniform.rvs(size=size), uniform.rvs(size=size)),
+        samples = np.stack((uniform.rvs(size=size, random_state=random_state),
+                            uniform.rvs(size=size, random_state=random_state)),
                            axis=1)
         samples[:, 0] = self.ppcf(samples)
         return samples
@@ -565,24 +562,9 @@ def _logpdf(self, samples):
         return vals
 
     def _logcdf(self, samples):
-        lower = np.full(2, -np.inf)
         upper = norm.ppf(samples)
-        limit_flags = np.zeros(2)
-        if upper.shape[0] > 0:
-
-            def func1d(upper1d):
-                '''
-                Calculates the multivariate normal cumulative distribution
-                function of a single sample.
-                '''
-                return mvn.mvndst(lower, upper1d, limit_flags, self.theta)[1]
-
-            vals = np.apply_along_axis(func1d, -1, upper)
-        else:
-            vals = np.empty((0, ))
-        old_settings = np.seterr(divide='ignore')
-        vals = np.log(vals)
-        np.seterr(**old_settings)
+        cov = [[1.0, self.theta], [self.theta, 1.0]]
+        vals = multivariate_normal.logcdf(upper, None, cov)
         vals[np.any(samples == 0.0, axis=1)] = -np.inf
         vals[samples[:, 0] == 1.0] = np.log(samples[samples[:, 0] == 1.0, 1])
         vals[samples[:, 1] == 1.0] = np.log(samples[samples[:, 1] == 1.0, 0])

mixedvines/marginal.py

@@ -1,5 +1,5 @@
 # -*- coding: utf-8 -*-
-# Copyright (C) 2017-2019 Arno Onken
+# Copyright (C) 2017-2019, 2021 Arno Onken
 #
 # This file is part of the mixedvines package.
 #
@@ -19,7 +19,6 @@
 This module implements univariate marginal distribution that are either
 continuous or discrete.
 '''
-from __future__ import division
 from scipy.stats import rv_continuous, norm, gamma, poisson, binom, nbinom
 import numpy as np
 
@@ -54,7 +53,7 @@ class Marginal(object):
         Cumulative distribution function.
     ppf(samples)
         Inverse of the cumulative distribution function.
-    rvs(size=1)
+    rvs(size, random_state)
         Generate random variates.
     fit(samples, is_continuous)
         Fit a distribution to samples.
@@ -147,21 +146,26 @@ def ppf(self, samples):
         '''
         return self.rv_mixed.ppf(samples)
 
-    def rvs(self, size=1):
+    def rvs(self, size=1, random_state=None):
         '''
         Generates random variates from the distribution.
 
         Parameters
         ----------
-        size : integer, optional
+        size : int, optional
             The number of samples to generate.  (Default: 1)
+        random_state : {None, int, RandomState, Generator}, optional
+            The random state to use for random variate generation.  `None`
+            corresponds to the `RandomState` singleton.  For an int, a new
+            `RandomState` is generated and seeded.  For a `RandomState` or
+            `Generator`, the object is used.  (Default: `None`)
 
         Returns
         -------
         array_like
             Array of samples.
         '''
-        return self.rv_mixed.rvs(size)
+        return self.rv_mixed.rvs(size, random_state=random_state)
 
     @staticmethod
     def fit(samples, is_continuous):