address the comment

python/mxnet/ndarray/numpy/random.py

@@ -17,8 +17,10 @@
 
 """Namespace for operators used in Gluon dispatched by F=ndarray."""
 from __future__ import absolute_import
+import numpy as np
 from ...context import current_context
 from . import _internal as _npi
+from ..ndarray import NDArray
 from ...base import numeric_types
 
 
@@ -237,7 +239,7 @@ def multinomial(n, pvals, size=None):
         return _npi.multinomial(pvals, pvals=None, n=n, size=size)
     else:
         if isinstance(pvals, np.ndarray):
-            pvals = pvals.tolist()
+            raise ValueError('numpy ndarray is not supported!')
         if any(isinstance(i, list) for i in pvals):
             raise ValueError('object too deep for desired array')
         return _npi.multinomial(n=n, pvals=pvals, size=size)

tests/python/unittest/test_numpy_ndarray.py

@@ -774,29 +774,24 @@ def test_np_multinomial():
     pvals_list = [[0.0, 0.1, 0.2, 0.3, 0.4], [0.4, 0.3, 0.2, 0.1, 0.0]]
     sizes = [None, (), (3,), (2, 5, 7), (4, 9)]
     experiements = 10000
-    for pvals_type in [list, _np.ndarray]:
-        for have_size in [False, True]:
-            for pvals in pvals_list:
-                if have_size:
-                    for size in sizes:
-                        if pvals_type == mx.nd.NDArray:
-                            pvals = mx.nd.array(pvals).as_np_ndarray()
-                        elif pvals_type == _np.ndarray:
-                            pvals = _np.array(pvals)
-                        freq = mx.np.random.multinomial(experiements, pvals, size=size).asnumpy() / _np.float32(experiements)
-                        # for those cases that didn't need reshape
-                        if size in [None, ()]:
-                            mx.test_utils.assert_almost_equal(freq, pvals, rtol=0.20, atol=1e-1)
-                        else:
-                            # check the shape
-                            assert freq.shape == size + (len(pvals),), 'freq.shape={}, size + (len(pvals))={}'.format(freq.shape, size + (len(pvals)))
-                            freq = freq.reshape((-1, len(pvals)))
-                            # check the value for each row
-                            for i in range(freq.shape[0]):
-                                mx.test_utils.assert_almost_equal(freq[i, :], pvals, rtol=0.20, atol=1e-1)
-                else:
-                    freq = mx.np.random.multinomial(experiements, pvals).asnumpy() / _np.float32(experiements)
-                    mx.test_utils.assert_almost_equal(freq, pvals, rtol=0.20, atol=1e-1)
+    for have_size in [False, True]:
+        for pvals in pvals_list:
+            if have_size:
+                for size in sizes:
+                    freq = mx.np.random.multinomial(experiements, pvals, size=size).asnumpy() / _np.float32(experiements)
+                    # for those cases that didn't need reshape
+                    if size in [None, ()]:
+                        mx.test_utils.assert_almost_equal(freq, pvals, rtol=0.20, atol=1e-1)
+                    else:
+                        # check the shape
+                        assert freq.shape == size + (len(pvals),), 'freq.shape={}, size + (len(pvals))={}'.format(freq.shape, size + (len(pvals)))
+                        freq = freq.reshape((-1, len(pvals)))
+                        # check the value for each row
+                        for i in range(freq.shape[0]):
+                            mx.test_utils.assert_almost_equal(freq[i, :], pvals, rtol=0.20, atol=1e-1)
+            else:
+                freq = mx.np.random.multinomial(experiements, pvals).asnumpy() / _np.float32(experiements)
+                mx.test_utils.assert_almost_equal(freq, pvals, rtol=0.20, atol=1e-1)
     # check the zero dimension
     sizes = [(0), (0, 2), (4, 0, 2), (3, 0, 1, 2, 0)]
     for pvals in pvals_list: