get rid of most operators

python/mxnet/_numpy_op_doc.py

@@ -20,43 +20,6 @@
 """Doc placeholder for numpy ops with prefix _np."""
 
 
-def _np_reshape(a, newshape, order='C'):
-    """
-    reshape(a, newshape, order='C')
-
-    Gives a new shape to an array without changing its data.
-
-    Parameters
-    ----------
-    a : ndarray
-        Array to be reshaped.
-    newshape : int or tuple of ints
-        The new shape should be compatible with the original shape. If
-        an integer, then the result will be a 1-D array of that length.
-        One shape dimension can be -1. In this case, the value is
-        inferred from the length of the array and remaining dimensions.
-    order : {'C'}, optional
-        Read the elements of `a` using this index order, and place the
-        elements into the reshaped array using this index order.  'C'
-        means to read / write the elements using C-like index order,
-        with the last axis index changing fastest, back to the first
-        axis index changing slowest. Other order types such as 'F'/'A'
-        may be added in the future.
-
-    Returns
-    -------
-    reshaped_array : ndarray
-        It will be always a copy of the original array. This behavior is different
-        from the official NumPy package where views of the original array may be
-        generated.
-
-    See Also
-    --------
-    ndarray.reshape : Equivalent method.
-    """
-    pass
-
-
 def _np_ones_like(a):
     """Return an array of ones with the same shape and type as a given array.
 
@@ -89,89 +52,3 @@ def _np_zeros_like(a):
         Array of zeros with the same shape and type as `a`.
     """
     pass
-
-
-def _np_repeat(a, repeats, axis=None):
-    """Repeat elements of an array.
-
-    Parameters
-    ----------
-    a : ndarray
-        Input array.
-    repeats : int or array of ints
-        The number of repetitions for each element.  `repeats` is broadcasted
-        to fit the shape of the given axis.
-    axis : int, optional
-        The axis along which to repeat values.  By default, use the
-        flattened input array, and return a flat output array.
-
-    Returns
-    -------
-    repeated_array : ndarray
-        Output array which has the same shape as `a`, except along
-        the given axis.
-    """
-    pass
-
-
-def _np_dot(a, b, out=None):
-    """dot(a, b, out=None)
-
-    Dot product of two arrays. Specifically,
-    
-    - If both `a` and `b` are 1-D arrays, it is inner product of vectors
-    
-    - If both `a` and `b` are 2-D arrays, it is matrix multiplication,
-    
-    - If either `a` or `b` is 0-D (scalar), it is equivalent to :func:`multiply`
-      and using ``np.multiply(a, b)`` or ``a * b`` is preferred.
-    
-    - If `a` is an N-D array and `b` is a 1-D array, it is a sum product over
-      the last axis of `a` and `b`.
-    
-    - If `a` is an N-D array and `b` is a 2-D array, it is a
-      sum product over the last axis of `a` and the second-to-last axis of `b`::
-    
-        dot(a, b)[i,j,k] = sum(a[i,j,:] * b[:,k])
-
-    Parameters
-    ----------
-    a : ndarray
-        First argument.
-    b : ndarray
-        Second argument.
-    
-    out : ndarray, optional
-        Output argument. It must have the same shape and type as the expected output.
-
-    Returns
-    -------
-    output : ndarray
-        Returns the dot product of `a` and `b`.  If `a` and `b` are both
-        scalars or both 1-D arrays then a scalar is returned; otherwise
-        an array is returned.
-        If `out` is given, then it is returned
-
-    Examples
-    --------
-    >>> a = np.array(3)
-    >>> b = np.array(4)
-    >>> np.dot(a, b)
-    array(12.)
-
-    For 2-D arrays it is the matrix product:
-
-    >>> a = np.array([[1, 0], [0, 1]])
-    >>> b = np.array([[4, 1], [2, 2]])
-    >>> np.dot(a, b)
-    array([[4., 1.],
-           [2., 2.]])
-
-    >>> a = np.arange(3*4*5*6).reshape((3,4,5,6))
-    >>> b = np.arange(5*6)[::-1].reshape((6,5))
-    >>> np.dot(a, b)[2,3,2,2]
-    array(29884.)
-    >>> np.sum(a[2,3,2,:] * b[:,2])
-    array(29884.)
-    """
-    pass