Revert "remove numpy docs"

This reverts commit c104695.

python/mxnet/_numpy_op_doc.py

@@ -0,0 +1,177 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# pylint: skip-file
+
+"""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.
+
+ Parameters
+ ----------
+ a : ndarray
+ The shape and data-type of `a` define these same attributes of
+ the returned array.
+
+ Returns
+ -------
+ out : ndarray
+ Array of ones with the same shape and type as `a`.
+ """
+ pass
+
+
+def _np_zeros_like(a):
+ """Return an array of zeros with the same shape and type as a given array.
+
+ Parameters
+ ----------
+ a : ndarray
+ The shape and data-type of `a` define these same attributes of
+ the returned array.
+
+ Returns
+ -------
+ out : ndarray
+ 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