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