[Numpy] Backport np.random.randn to master

python/mxnet/numpy/random.py

@@ -20,7 +20,7 @@
 from __future__ import absolute_import
 from ..ndarray import numpy as _mx_nd_np
 
-__all__ = ["randint", "uniform", "normal", "choice", "rand", "multinomial", "shuffle"]
+__all__ = ["randint", "uniform", "normal", "choice", "rand", "multinomial", "shuffle", "randn"]
 
 
 def randint(low, high=None, size=None, dtype=None, ctx=None, out=None):
@@ -357,3 +357,44 @@ def shuffle(x):
            [0., 1., 2.]])
     """
     _mx_nd_np.random.shuffle(x)
+
+
+def randn(*size, **kwargs):
+    r"""Return a sample (or samples) from the "standard normal" distribution.
+    If positive, int_like or int-convertible arguments are provided,
+    `randn` generates an array of shape ``(d0, d1, ..., dn)``, filled
+    with random floats sampled from a univariate "normal" (Gaussian)
+    distribution of mean 0 and variance 1 (if any of the :math:`d_i` are
+    floats, they are first converted to integers by truncation). A single
+    float randomly sampled from the distribution is returned if no
+    argument is provided.
+    This is a convenience function.  If you want an interface that takes a
+    tuple as the first argument, use `numpy.random.standard_normal` instead.
+    Parameters
+    ----------
+    d0, d1, ..., dn : int, optional
+        The dimensions of the returned array, should be all positive.
+        If no argument is given a single Python float is returned.
+    Returns
+    -------
+    Z : ndarray
+        A ``(d0, d1, ..., dn)``-shaped array of floating-point samples from
+        the standard normal distribution, or a single such float if
+        no parameters were supplied.
+    Notes
+    -----
+    For random samples from :math:`N(\mu, \sigma^2)`, use:
+    ``sigma * np.random.randn(...) + mu``
+    Examples
+    --------
+    >>> np.random.randn()
+    2.1923875335537315 #random
+    Two-by-four array of samples from N(3, 6.25):
+    >>> 2.5 * np.random.randn(2, 4) + 3
+    array([[-4.49401501,  4.00950034, -1.81814867,  7.29718677],  #random
+        [ 0.39924804,  4.68456316,  4.99394529,  4.84057254]]) #random
+    """
+    output_shape = ()
+    for s in size:
+        output_shape += (s,)
+    return _mx_nd_np.random.normal(0, 1, size=output_shape, **kwargs)

tests/python/unittest/test_numpy_op.py

@@ -3089,6 +3089,28 @@ def hybrid_forward(self, F, x):
                 assert out.shape == expected_shape
 
 
+@with_seed()
+@use_np
+def test_np_randn():
+    # Test shapes.
+    shapes = [
+        (3, 3),
+        (3, 4),
+        (0, 0),
+        (3, 3, 3),
+        (0, 0, 0),
+        (2, 2, 4, 3),
+        (2, 2, 4, 3),
+        (2, 0, 3, 0),
+        (2, 0, 2, 3)
+    ]
+    dtypes = ['float16', 'float32', 'float64']
+    for dtype in dtypes:
+        for shape in shapes:
+            data_mx = np.random.randn(*shape, dtype=dtype)
+            assert data_mx.shape == shape
+
+
 @with_seed()
 @use_np
 def test_random_seed():