Merge pull request #103 from IntelPython/add-numpy-interface-tests

Add `interfaces.numpy_random` tests

Author: ndgrigorian

.flake8

@@ -6,7 +6,7 @@ extend-ignore =
     D100,
     # missing docstring in public class:
     D101,
-    # missing docstring in public method:
+    # missing docstring in public function:
     D103,
     # missing docstring in public package:
     D104,
@@ -26,6 +26,7 @@ extend-ignore =
 per-file-ignores =
     mkl_random/__init__.py: F401
     mkl_random/interfaces/__init__.py: F401
+    mkl_random/tests/**/*.py: D102
 
 filename = *.py, *.pyx, *.pxi, *.pxd
 max_line_length = 80

CHANGELOG.md

@@ -11,6 +11,14 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 * Added `mkl_random.interfaces` with `mkl_random.interfaces.numpy_random` interface, which aliases `mkl_random` functionality to more strictly adhere to NumPy's API (i.e., drops arguments and functions which are not part of standard NumPy) [gh-92](https://github.com/IntelPython/mkl_random/pull/92)
 
+* Added third-party tests from `numpy.random` which tests the `mkl_random.interfaces.numpy_random` interface [gh-103](https://github.com/IntelPython/mkl_random/pull/103)
+
+### Changed
+* Updates to `mkl_random` implementations to better align with newer versions of `numpy.random` [gh-103](https://github.com/IntelPython/mkl_random/pull/103)
+
+### Fixed
+* Various bugfixes including a hang in `zipf` when called with `np.nan` and size-1 1D arrays being cast to scalars [gh-103](https://github.com/IntelPython/mkl_random/pull/103)
+
 ### Removed
 * Dropped support for Python 3.9 [gh-81](https://github.com/IntelPython/mkl_random/pull/81)
 

mkl_random/interfaces/_numpy_random.py

@@ -281,6 +281,18 @@ def exponential(self, scale=1.0, size=None):
         """
         return super().exponential(scale=scale, size=size)
 
+    def tomaxint(self, size=None):
+        """
+        tomaxint(size=None)
+
+        Return a sample of uniformly distributed random integers in the
+        interval [0, ``np.iinfo("long").max``].
+
+        For full documentation refer to `numpy.random.RandomState.tomaxint`.
+
+        """
+        return super().tomaxint(size=size)
+
     def standard_exponential(self, size=None):
         """
         standard_exponential(size=None)
@@ -314,27 +326,29 @@ def gamma(self, shape, scale=1.0, size=None):
         """
         return super().gamma(shape=shape, scale=scale, size=size)
 
-    def f(self, dfn, dfd, size=None):
+    def f(self, dfnum, dfden, size=None):
         """
-        f(dfn, dfd, size=None)
+        f(dfnum, dfden, size=None)
 
         Draw samples from an F distribution.
 
         For full documentation refer to `numpy.random.f`.
 
         """
-        return super().f(dfn=dfn, dfd=dfd, size=size)
+        return super().f(dfnum=dfnum, dfden=dfden, size=size)
 
-    def noncentral_f(self, dfn, dfd, nonc, size=None):
+    def noncentral_f(self, dfnum, dfden, nonc, size=None):
         """
-        noncentral_f(dfn, dfd, nonc, size=None)
+        noncentral_f(dfnum, dfden, nonc, size=None)
 
         Draw samples from a non-central F distribution.
 
         For full documentation refer to `numpy.random.noncentral_f`.
 
         """
-        return super().noncentral_f(dfn=dfn, dfd=dfd, nonc=nonc, size=size)
+        return super().noncentral_f(
+            dfnum=dfnum, dfden=dfden, nonc=nonc, size=size
+        )
 
     def chisquare(self, df, size=None):
         """