add numpy op bitwise_xor, hsplit, moveaxis, rot90

add numpy op bitwise_xor

fix some format problems

add numpy op moveaxis

fix code style

add numpy op rot90

fix pylint error

updata rot90

address comments

add  numpy op hsplit

refactor split compute function

refactor moveaxis code

Author: gyshi

python/mxnet/_numpy_op_doc.py

@@ -704,3 +704,47 @@ def _np_squeeze(a, axis=None, out=None):
     (1, 3)
     """
     pass
+
+
+def _np_moveaxis(a, source, destination):
+    """Move axes of an array to new positions.
+    Other axes remain in their original order.
+
+    Parameters
+    ----------
+    a : ndarray
+        The array whose axes should be reordered.
+        source : int or sequence of int
+        Original positions of the axes to move. These must be unique.
+        destination : int or sequence of int
+        Destination positions for each of the original axes. These must also be
+        unique.
+
+    Returns
+    -------
+    result : ndarray
+        Array with moved axes. This array is a view of the input array.
+
+    See Also
+    --------
+        transpose: Permute the dimensions of an array.
+        swapaxes: Interchange two axes of an array.
+
+    Examples
+    --------
+    >>> x = np.zeros((3, 4, 5))
+    >>> np.moveaxis(x, 0, -1).shape
+    (4, 5, 3)
+    >>> np.moveaxis(x, -1, 0).shape
+    (5, 3, 4)
+    These all achieve the same result:
+    >>> np.transpose(x).shape
+    (5, 4, 3)
+    >>> np.swapaxes(x, 0, -1).shape
+    (5, 4, 3)
+    >>> np.moveaxis(x, [0, 1], [-1, -2]).shape
+    (5, 4, 3)
+    >>> np.moveaxis(x, [0, 1, 2], [-1, -2, -3]).shape
+    (5, 4, 3)
+    """
+    pass

python/mxnet/ndarray/numpy/_op.py

@@ -36,7 +36,8 @@
            'stack', 'vstack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax',
            'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
            'around', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
-           'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal']
+           'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal',
+           'hsplit', 'rot90']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -2479,6 +2480,121 @@ def split(ary, indices_or_sections, axis=0):
 # pylint: enable=redefined-outer-name
 
 
+# pylint: disable=redefined-outer-name
+@set_module('mxnet.ndarray.numpy')
+def hsplit(ary, indices_or_sections):
+    """Split an array into multiple sub-arrays horizontally (column-wise).
+
+    This is equivalent to ``split`` with ``axis=0`` if ``ary`` has one
+    dimension, and otherwise that with ``axis=1``.
+
+    Parameters
+    ----------
+    ary : ndarray
+        Array to be divided into sub-arrays.
+    indices_or_sections : int, list of ints or tuple of ints.
+        If `indices_or_sections` is an integer, N, the array will be divided
+        into N equal arrays along `axis`.  If such a split is not possible,
+        an error is raised.
+
+        If `indices_or_sections` is a list of sorted integers, the entries
+        indicate where along `axis` the array is split.
+
+        If an index exceeds the dimension of the array along `axis`,
+        it will raises errors. so index must less than or euqal to
+        the dimension of the array along axis.
+
+    Returns
+    -------
+    sub-arrays : list of ndarrays
+        A list of sub-arrays.
+
+    Notes
+    ------
+    - If `indices_or_sections` is given as an integer, but a split
+      does not result in equal division.It will raises ValueErrors.
+
+    - If indices_or_sections is an integer, and the number is 1, it will
+      raises an error. Because single output from split is not supported yet...
+
+    See Also
+    --------
+    split : Split an array into multiple sub-arrays of equal size.
+
+    Examples
+    --------
+    >>> x = np.arange(16.0).reshape(4, 4)
+    >>> x
+    array([[ 0.,  1.,  2.,  3.],
+           [ 4.,  5.,  6.,  7.],
+           [ 8.,  9., 10., 11.],
+           [12., 13., 14., 15.]])
+    >>> np.hsplit(x, 2)
+    [array([[ 0.,  1.],
+           [ 4.,  5.],
+           [ 8.,  9.],
+           [12., 13.]]),
+    array([[ 2.,  3.],
+           [ 6.,  7.],
+           [10., 11.],
+           [14., 15.]])]
+    >>> np.hsplit(x, [3, 6])
+    [array([[ 0.,  1.,  2.],
+           [ 4.,  5.,  6.],
+           [ 8.,  9., 10.],
+           [12., 13., 14.]]),
+    array([[ 3.],
+           [ 7.],
+           [11.],
+           [15.]]),
+    array([], shape=(4, 0), dtype=float32)]
+
+    With a higher dimensional array the split is still along the second axis.
+
+    >>> x = np.arange(8.0).reshape(2, 2, 2)
+    >>> x
+    array([[[ 0.,  1.],
+            [ 2.,  3.]],
+           [[ 4.,  5.],
+            [ 6.,  7.]]])
+    >>> np.hsplit(x, 2)
+    [array([[[ 0.,  1.]],
+            [[ 4.,  5.]]]),
+     array([[[ 2.,  3.]],
+            [[ 6.,  7.]]])]
+
+    If ``ary`` has one dimension, 'axis' = 0.
+    >>> x = np.arange(4)
+    array([0., 1., 2., 3.])
+    >>> np.hsplit(x, 2)
+    [array([0., 1.]), array([2., 3.])]
+
+    If you want to produce an empty sub-array, you can see an example.
+    >>> np.hsplit(x, [2, 2])
+    [array([0., 1.]), array([], dtype=float32), array([2., 3.])]
+    """
+    indices = []
+    axis = 1
+    if (len(ary.shape) == 1):
+        axis = 0
+    axis_size = ary.shape[axis]
+    if isinstance(indices_or_sections, int):
+        sections = indices_or_sections
+        if axis_size % sections:
+            raise ValueError('array hsplit does not result in an equal division')
+        section_size = int(axis_size / sections)
+        indices = [i * section_size for i in range(sections)]
+    elif isinstance(indices_or_sections, (list, set, tuple)):
+        indices = [0] + list(indices_or_sections)
+    else:
+        raise ValueError('indices_or_sections must either int or tuple of ints')
+    ret = _npi.hsplit(ary, indices, axis, False)
+    if not isinstance(ret, list):
+        raise NotImplementedError('single output from hsplit is not supported yet...')
+    return ret
+# pylint: enable=redefined-outer-name
+
+
 @set_module('mxnet.ndarray.numpy')
 def vsplit(ary, indices_or_sections):
     r"""
@@ -4124,3 +4240,49 @@ def less_equal(x1, x2, out=None):
     """
     return _ufunc_helper(x1, x2, _npi.less_equal, _np.less_equal, _npi.less_equal_scalar,
                          _npi.greater_equal_scalar, out)
+
+
+@set_module('mxnet.ndarray.numpy')
+def rot90(m, k=1, axes=(0, 1)):
+    """
+    Rotate an array by 90 degrees in the plane specified by axes.
+    Rotation direction is from the first towards the second axis.
+    Parameters
+    ----------
+    m : ndarray
+        Array of two or more dimensions.
+    k : integer
+        Number of times the array is rotated by 90 degrees.
+    axes: (2,) array_like
+        The array is rotated in the plane defined by the axes.
+        Axes must be different.
+
+    Returns
+    -------
+    y : ndarray
+        A rotated view of `m`.
+
+    -----
+    rot90(m, k=1, axes=(1,0)) is the reverse of rot90(m, k=1, axes=(0,1))
+    rot90(m, k=1, axes=(1,0)) is equivalent to rot90(m, k=-1, axes=(0,1))
+    Examples
+    --------
+    >>> m = np.array([[1,2],[3,4]], 'int')
+    >>> m
+    array([[1, 2],
+           [3, 4]], dtype=int64)
+    >>> np.rot90(m)
+    array([[2, 4],
+           [1, 3]], dtype=int64)
+    >>> np.rot90(m, 2)
+    array([[4, 3],
+           [2, 1]], dtype=int64)
+    >>> m = np.arange(8).reshape((2,2,2))
+    >>> np.rot90(m, 1, (1,2))
+    array([[[1., 3.],
+            [0., 2.]],
+
+           [[5., 7.],
+            [4., 6.]]])
+    """
+    return _npi.rot90(m, k=k, axes=axes)

python/mxnet/numpy/multiarray.py

@@ -55,8 +55,7 @@
            'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming',
            'blackman', 'flip', 'around', 'arctan2', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril',
            'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less',
-           'greater_equal', 'less_equal']
-
+           'greater_equal', 'less_equal', 'hsplit', 'rot90']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -4114,8 +4113,7 @@ def vsplit(ary, indices_or_sections):
             [4., 5., 6., 7.]]), array([[ 8.,  9., 10., 11.],
             [12., 13., 14., 15.]])]
 
-    With a higher dimensional array the split is still along the first axis.
-
+    >>> # With a higher dimensional array the split is still along the first axis.
     >>> x = np.arange(8.0).reshape(2, 2, 2)
     >>> x
     array([[[ 0.,  1.],
@@ -5651,3 +5649,136 @@ def less_equal(x1, x2, out=None):
     array([True])
     """
     return _mx_nd_np.less_equal(x1, x2, out)
+
+
+@set_module('mxnet.numpy')
+def rot90(m, k=1, axes=(0, 1)):
+    """
+    Rotate an array by 90 degrees in the plane specified by axes.
+    Rotation direction is from the first towards the second axis.
+    Parameters
+    ----------
+    m : ndarray
+        Array of two or more dimensions.
+    k : integer
+        Number of times the array is rotated by 90 degrees.
+    axes: (2,) array_like
+        The array is rotated in the plane defined by the axes.
+        Axes must be different.
+
+    Returns
+    -------
+    y : ndarray
+        A rotated view of `m`.
+
+    -----
+    rot90(m, k=1, axes=(1,0)) is the reverse of rot90(m, k=1, axes=(0,1))
+    rot90(m, k=1, axes=(1,0)) is equivalent to rot90(m, k=-1, axes=(0,1))
+    Examples
+    --------
+    >>> m = np.array([[1,2],[3,4]], 'int')
+    >>> m
+    array([[1, 2],
+           [3, 4]], dtype=int64)
+    >>> np.rot90(m)
+    array([[2, 4],
+           [1, 3]], dtype=int64)
+    >>> np.rot90(m, 2)
+    array([[4, 3],
+           [2, 1]], dtype=int64)
+    >>> m = np.arange(8).reshape((2,2,2))
+    >>> np.rot90(m, 1, (1,2))
+    array([[[1., 3.],
+            [0., 2.]],
+
+           [[5., 7.],
+            [4., 6.]]])
+    """
+    return _mx_nd_np.rot90(m, k=k, axes=axes)
+
+
+@set_module('mxnet.numpy')
+def hsplit(ary, indices_or_sections):
+    """Split an array into multiple sub-arrays horizontally (column-wise).
+    This is equivalent to ``split`` with ``axis=0`` if ``ary`` has one
+    dimension, and otherwise that with ``axis=1``.
+
+    Parameters
+    ----------
+    ary : ndarray
+        Array to be divided into sub-arrays.
+    indices_or_sections : int, list of ints or tuple of ints.
+        If `indices_or_sections` is an integer, N, the array will be divided
+        into N equal arrays along `axis`.  If such a split is not possible,
+        an error is raised.
+        If `indices_or_sections` is a list of sorted integers, the entries
+        indicate where along `axis` the array is split.
+        If an index exceeds the dimension of the array along `axis`,
+        it will raises errors. so index must less than or euqal to
+        the dimension of the array along axis.
+
+    Returns
+    -------
+    sub-arrays : list of ndarrays
+        A list of sub-arrays.
+
+    Notes
+    ------
+    - If `indices_or_sections` is given as an integer, but a split
+      does not result in equal division.It will raises ValueErrors.
+    - If indices_or_sections is an integer, and the number is 1, it will
+      raises an error. Because single output from split is not supported yet...
+
+    See Also
+    --------
+    split : Split an array into multiple sub-arrays of equal size.
+
+    Examples
+    --------
+    >>> x = np.arange(16.0).reshape(4, 4)
+    >>> x
+    array([[ 0.,  1.,  2.,  3.],
+           [ 4.,  5.,  6.,  7.],
+           [ 8.,  9., 10., 11.],
+           [12., 13., 14., 15.]])
+    >>> np.hsplit(x, 2)
+    [array([[ 0.,  1.],
+           [ 4.,  5.],
+           [ 8.,  9.],
+           [12., 13.]]),
+    array([[ 2.,  3.],
+           [ 6.,  7.],
+           [10., 11.],
+           [14., 15.]])]
+    >>> np.hsplit(x, [3, 6])
+    [array([[ 0.,  1.,  2.],
+           [ 4.,  5.,  6.],
+           [ 8.,  9., 10.],
+           [12., 13., 14.]]),
+    array([[ 3.],
+           [ 7.],
+           [11.],
+           [15.]]),
+    array([], shape=(4, 0), dtype=float32)]
+    With a higher dimensional array the split is still along the second axis.
+    >>> x = np.arange(8.0).reshape(2, 2, 2)
+    >>> x
+    array([[[ 0.,  1.],
+            [ 2.,  3.]],
+           [[ 4.,  5.],
+            [ 6.,  7.]]])
+    >>> np.hsplit(x, 2)
+    [array([[[ 0.,  1.]],
+            [[ 4.,  5.]]]),
+     array([[[ 2.,  3.]],
+            [[ 6.,  7.]]])]
+    If ``ary`` has one dimension, 'axis' = 0.
+    >>> x = np.arange(4)
+    array([0., 1., 2., 3.])
+    >>> np.hsplit(x, 2)
+    [array([0., 1.]), array([2., 3.])]
+    If you want to produce an empty sub-array, you can see an example.
+    >>> np.hsplit(x, [2, 2])
+    [array([0., 1.]), array([], dtype=float32), array([2., 3.])]
+    """
+    return _mx_nd_np.hsplit(ary, indices_or_sections)