[Numpy] add numpy op bitwise_xor, hsplit, moveaxis, rot90

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)