Remove previous Weibull test

python/mxnet/ndarray/numpy/random.py

@@ -462,7 +462,7 @@ def exponential(scale, size):
         return _npi.exponential(scale=scale, size=size)
 
 
-def weibull(a, size):
+def weibull(a, size=None):
     r"""Draw samples from a 1-parameter Weibull distribution with given
     parameter a, via inversion.
 
@@ -515,7 +515,7 @@ def weibull(a, size):
         return _npi.weibull(a=a, size=size)
 
 
-def pareto(a, size):
+def pareto(a, size=None):
     r"""Draw samples from a Pareto II or Lomax distribution with specified shape a.
 
     Parameters
@@ -558,7 +558,7 @@ def pareto(a, size):
         return _npi.pareto(a=a, size=size)
 
 
-def power(a, size):
+def power(a, size=None):
     r"""Draw samples in [0, 1] from a power distribution with given parameter a.
 
     Parameters

python/mxnet/symbol/numpy/random.py

@@ -469,7 +469,7 @@ def exponential(scale=1.0, size=None):
         return _npi.exponential(scale=scale, size=size)
 
 
-def weibull(a, size):
+def weibull(a, size=None):
     r"""Draw samples from a 1-parameter Weibull distribution with given parameter a
     via inversion.
 
@@ -524,7 +524,7 @@ def weibull(a, size):
         return _npi.weibull(a=a, size=size)
 
 
-def pareto(a, size):
+def pareto(a, size=None):
     r"""Draw samples from a Pareto II or Lomax distribution with specified shape a.
 
     Parameters
@@ -567,7 +567,7 @@ def pareto(a, size):
         return _npi.pareto(a=a, size=size)
 
 
-def power(a, size):
+def power(a, size=None):
     r"""Draw samples in [0, 1] from a power distribution with given parameter a.
 
     Parameters

tests/python/unittest/test_numpy_op.py

@@ -3598,36 +3598,6 @@ def _test_exponential_exception(scale):
     assertRaises(ValueError, _test_exponential_exception, -1)
 
 
-@with_seed()
-@use_np
-def test_np_random_weibull():
-    class TestRandomWeibull(HybridBlock):
-        def __init__(self, shape):
-            super(TestRandomWeibull, self).__init__()
-            self._shape = shape
-
-        def hybrid_forward(self, F, a):
-            return F.np.random.weibull(a, self._shape)
-
-    shapes = [(), (1,), (2, 3), (4, 0, 5), 6, (7, 8), None]
-    for hybridize in [False, True]:
-        for shape in shapes:
-            test_weibull = TestRandomWeibull(shape)
-            if hybridize:
-                test_weibull.hybridize()
-            np_out = _np.random.weibull(1, size = shape)
-            mx_out = test_weibull(np.array([1]))
-
-    for shape in shapes:
-        mx_out = np.random.weibull(np.array([1]), shape)
-        np_out = _np.random.weibull(np.array([1]).asnumpy(), shape)
-        assert_almost_equal(mx_out.asnumpy().shape, np_out.shape)
-
-    def _test_weibull_exception(a):
-        output = np.random.weibull(a=a).asnumpy()
-    assertRaises(ValueError, _test_weibull_exception, -1)
-
-
 @with_seed()
 @use_np
 def test_np_random_a():
@@ -3687,7 +3657,7 @@ def hybrid_forward(self, F, a):
             expected_shape = a.shape
             assert mx_out.shape == expected_shape
 
-    # test illegal parameter values
+    # test illegal parameter values (as numpy produces)
     def _test_exception(a):
         output = op(a=a).asnumpy()
     for op in op_names: