From c104695b28a26738b8700d80c70814e0f583ac55 Mon Sep 17 00:00:00 2001 From: Hao Jin Date: Wed, 31 Jul 2019 20:29:07 +0000 Subject: [PATCH] remove numpy docs --- python/mxnet/_numpy_op_doc.py | 177 ---------------------------------- 1 file changed, 177 deletions(-) delete mode 100644 python/mxnet/_numpy_op_doc.py diff --git a/python/mxnet/_numpy_op_doc.py b/python/mxnet/_numpy_op_doc.py deleted file mode 100644 index 867eac710c21..000000000000 --- a/python/mxnet/_numpy_op_doc.py +++ /dev/null @@ -1,177 +0,0 @@ -# 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