[GitHub] [incubator-mxnet] tingying2020 commented on a change in pull request #15382: [numpy][doc-fix] sum, copy, tile, argmax, sign, log, degrees

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tingying2020 commented on a change in pull request #15382: [numpy][doc-fix] sum, copy, tile, argmax, sign, log, degrees
URL: https://github.com/apache/incubator-mxnet/pull/15382#discussion_r298870494
 
 

 ##########
 File path: python/mxnet/numpy/multiarray.py
 ##########
 @@ -2074,6 +2082,252 @@ def sqrt(x, out=None, **kwargs):
     return _mx_nd_np.sqrt(x, out=out, **kwargs)
 
 
+@set_module('mxnet.numpy')
+def tile(x, reps):
+    """
+    Construct an array by repeating A the number of times given by reps.
+
+    If `reps` has length ``d``, the result will have dimension of
+    ``max(d, A.ndim)``.
+
+    If ``A.ndim < d``, `A` is promoted to be d-dimensional by prepending new
+    axes. So a shape (3,) array is promoted to (1, 3) for 2-D replication,
+    or shape (1, 1, 3) for 3-D replication. If this is not the desired
+    behavior, promote `A` to d-dimensions manually before calling this
+    function.
+
+    If ``A.ndim > d``, `reps` is promoted to `A`.ndim by pre-pending 1's to it.
+    Thus for an `A` of shape (2, 3, 4, 5), a `reps` of (2, 2) is treated as
+    (1, 1, 2, 2).
+
+    Parameters
+    ----------
+    A : ndarray or scalar
+        A input array or a scalar to repeat.
+    reps : a single integer or tuple of integers
+        The number of repetitions of `A` along each axis.
+
+    Returns
+    -------
+    c : ndarray
+        The tiled output array.
+
+    Examples
+    --------
+    >>> a = np.array([0, 1, 2])
+    >>> np.tile(a, 2)
+    array([0., 1., 2., 0., 1., 2.])
+    >>> np.tile(a, (2, 2))
+    array([[0., 1., 2., 0., 1., 2.],
+           [0., 1., 2., 0., 1., 2.]])
+    >>> np.tile(a, (2, 1, 2))
+    array([[[0., 1., 2., 0., 1., 2.]],
+           [[0., 1., 2., 0., 1., 2.]]])
+
+    >>> b = np.array([[1, 2], [3, 4]])
+    >>> np.tile(b, 2)
+    array([[1., 2., 1., 2.],
+           [3., 4., 3., 4.]])
+    >>> np.(b, (2, 1))
+    array([[1., 2.],
+           [3., 4.],
+           [1., 2.],
+           [3., 4.]])
+
+    >>> c = np.array([1,2,3,4])
+    >>> np.tile(c,(4,1))
+    array([[1., 2., 3., 4.],
+           [1., 2., 3., 4.],
+           [1., 2., 3., 4.],
+           [1., 2., 3., 4.]])
+
+    Scalar as input:
+
+    >>> np.tile(2, 3)
+    array([2, 2, 2]) # repeating integer 2
+
+    """
+    return _mx_nd_np.tile(x, reps)
+
+
+@set_module('mxnet.numpy')
+def sign(x, out=None):
+    """
+    Returns an element-wise indication of the sign of a number.
+
+    The `sign` function returns ``-1 if x < 0, 0 if x==0, 1 if x > 0``. Only supports real number.
+
+    Parameters
+    ----------
+    x : ndarray or a scalar
+        Input values.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+        If provided, it must have the same shape and dtype as input ndarray.
+        If not provided or `None`, a freshly-allocated array is returned.
+
+    Returns
+    -------
+    y : ndarray
+        The sign of `x`.
+        This is a scalar if `x` is a scalar.
+
+    Notes
+    -------
+    This function differs from the original `numpy.sign
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.sign.html>`_ in
+    the following aspects:
+
+    - Only supports real number as input elements.
+    - Input type does not support Python native iterables(list, tuple, ...).
+    - ``out`` param: cannot perform auto broadcasting. ``out`` ndarray's shape must be the same as the expected output.
+    - ``out`` param: cannot perform auto type change. ``out`` ndarray's dtype must be the same as the expected output.
+    - ``out`` param does not support scalar input case.
+
+    Examples
+    --------
+    >>> a = np.array([-5., 4.5])
+    >>> np.sign(a)
+    array([-1.,  1.])
+
+    Scalars as input:
+
+    >>> np.sign(4.0)
+    1.0
+    >>> np.sign(0)
+    0
+
+    Use ``out`` parameter:
+
+    >>> b = np.zeros((2, ))
+    >>> np.sign(a, out=b)
+    array([-1.,  1.])
+    >>> b
+    array([-1.,  1.])
+
+    """
+    return _mx_nd_np.sign(x, out=out)
+
+
+@set_module('mxnet.symbol.numpy')
+def log(x, out=None, **kwargs):
+    r"""
+    log(x, out=None)
+
+    Natural logarithm, element-wise.
+
+    The natural logarithm `log` is the inverse of the exponential function,
+    so that `log(exp(x)) = x`. The natural logarithm is logarithm in base
+    `e`.
+
+    Parameters
+    ----------
+    x : ndarray
+        Input value. Elements must be of real value.
+    out : ndarray or None, optional
+        A location into which the result is stored.
+        If provided, it must have the same shape and dtype as input ndarray.
+        If not provided or `None`, a freshly-allocated array is returned.
+
+    Returns
+    -------
+    y : ndarray
+        The natural logarithm of `x`, element-wise.
+        This is a scalar if `x` is a scalar.
+
+    Notes
+    -----
+    This function differs from the original `numpy.log
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.log.html>`_ in
+    the following aspects:
+
+    - Does not support complex number for now
+    - Input type does not support Python native iterables(list, tuple, ...).
+    - ``out`` param: cannot perform auto broadcasting. ``out`` ndarray's shape must be the same as the expected output.
+    - ``out`` param: cannot perform auto type change. ``out`` ndarray's dtype must be the same as the expected output.
+    - ``out`` param does not support scalar input case.
+
+    References
+    ----------
+    .. [1] M. Abramowitz and I.A. Stegun, "Handbook of Mathematical Functions",
 
 Review comment:
   Maybe you should add reference number above.

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