From 59810d69b2934cfec350859dc0317693a315a6aa Mon Sep 17 00:00:00 2001 From: huangmengxi Date: Thu, 4 Mar 2021 16:04:37 +0800 Subject: [PATCH] fix docstring --- mindspore/numpy/array_creations.py | 11 ++++++----- mindspore/numpy/array_ops.py | 20 ++++++++++---------- mindspore/numpy/math_ops.py | 16 ++++++++-------- 3 files changed, 24 insertions(+), 23 deletions(-) diff --git a/mindspore/numpy/array_creations.py b/mindspore/numpy/array_creations.py index 2ad65a80ad..0fb2d71800 100644 --- a/mindspore/numpy/array_creations.py +++ b/mindspore/numpy/array_creations.py @@ -530,11 +530,12 @@ def logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None, axis=0): start) and ends with base ** stop (see endpoint below). Args: - start (Union[int, list(int), tuple(int), tensor]): The starting value of the sequence. - stop (Union[int, list(int), tuple(int), tensor]): The end value of the sequence, - unless `endpoint` is set to False. In that case, the sequence consists - of all but the last of `num + 1` evenly spaced samples, so that `stop` - is excluded. Note that the step size changes when `endpoint` is False. + start (Union[int, list(int), tuple(int), tensor]): ``base ** start`` is the starting + value of the sequence. + stop (Union[int, list(int), tuple(int), tensor]): ``base ** stop`` is the final value of + the sequence, unless `endpoint` is False. In that case, ``num + 1`` values are spaced + over the interval in log-space, of which all but the last (a sequence of length num) + are returned. num (int, optional): Number of samples to generate. Default is 50. endpoint (bool, optional): If True, `stop` is the last sample. Otherwise, it is not included. Default is True. diff --git a/mindspore/numpy/array_ops.py b/mindspore/numpy/array_ops.py index 17b5d0e205..78d1cccce1 100644 --- a/mindspore/numpy/array_ops.py +++ b/mindspore/numpy/array_ops.py @@ -809,13 +809,13 @@ def atleast_1d(*arys): >>> c = np.ones(5) >>> output = np.atleast_1d(a, b, c) >>> print(output) - (Tensor(shape=[2, 3], dtype=Float32, value= + [Tensor(shape=[2, 3], dtype=Float32, value= [[1.00000000e+000, 1.00000000e+000, 1.00000000e+000], [1.00000000e+000, 1.00000000e+000, 1.00000000e+000]]), Tensor(shape=[1], dtype=Float32, value= [1.00000000e+000]), Tensor(shape=[5], dtype=Float32, value= [1.00000000e+000, 1.00000000e+000, 1.00000000e+000, - 1.00000000e+000, 1.00000000e+000])) + 1.00000000e+000, 1.00000000e+000])] """ return _atleast_xd(1, arys) @@ -846,13 +846,13 @@ def atleast_2d(*arys): >>> c = np.ones(5) >>> output = np.atleast_2d(a, b, c) >>> print(output) - (Tensor(shape=[2, 3], dtype=Float32, value= + [Tensor(shape=[2, 3], dtype=Float32, value= [[1.00000000e+000, 1.00000000e+000, 1.00000000e+000], [1.00000000e+000, 1.00000000e+000, 1.00000000e+000]]), Tensor(shape=[1, 1], dtype=Float32, value= [[1.00000000e+000]]), Tensor(shape=[1, 5], dtype=Float32, value= [[1.00000000e+000, 1.00000000e+000, 1.00000000e+000, - 1.00000000e+000, 1.00000000e+000]])) + 1.00000000e+000, 1.00000000e+000]])] """ return _atleast_xd(2, arys) @@ -886,13 +886,13 @@ def atleast_3d(*arys): >>> c = np.ones(5) >>> output = np.atleast_3d(a, b, c) >>> print(output) - (Tensor(shape=[2, 3, 1], dtype=Float32, value= + [Tensor(shape=[2, 3, 1], dtype=Float32, value= [[[1.00000000e+000], [1.00000000e+000], [1.00000000e+000]], [[1.00000000e+000], [1.00000000e+000], [1.00000000e+000]]]), Tensor(shape=[1, 1, 1], dtype=Float32, value= [[[1.00000000e+000]]]), Tensor(shape=[1, 5, 1], dtype=Float32, value= [[[1.00000000e+000], [1.00000000e+000], [1.00000000e+000], - [1.00000000e+000], [1.00000000e+000]]])) + [1.00000000e+000], [1.00000000e+000]]])] """ res = [] for arr in arys: @@ -1378,7 +1378,7 @@ def split(x, indices_or_sections, axis=0): Examples: >>> import mindspore.numpy as np - >>> input_x = np.arange(9) + >>> input_x = np.arange(9).astype('float32') >>> output = np.split(input_x, 3) >>> print(output) (Tensor(shape=[3], dtype=Float32, @@ -1455,7 +1455,7 @@ def vsplit(x, indices_or_sections): Examples: >>> import mindspore.numpy as np - >>> input_x = np.arange(9).reshape((3, 3)) + >>> input_x = np.arange(9).reshape((3, 3)).astype('float32') >>> output = np.vsplit(input_x, 3) >>> print(output) (Tensor(shape=[1, 3], dtype=Float32, @@ -1497,7 +1497,7 @@ def hsplit(x, indices_or_sections): Examples: >>> import mindspore.numpy as np - >>> input_x = np.arange(6).reshape((2, 3)) + >>> input_x = np.arange(6).reshape((2, 3)).astype('float32') >>> output = np.hsplit(input_x, 3) >>> print(output) (Tensor(shape=[2, 1], dtype=Float32, @@ -1542,7 +1542,7 @@ def dsplit(x, indices_or_sections): Examples: >>> import mindspore.numpy as np - >>> input_x = np.arange(6).reshape((1, 2, 3)) + >>> input_x = np.arange(6).reshape((1, 2, 3)).astype('float32') >>> output = np.dsplit(input_x, 3) >>> print(output) (Tensor(shape=[1, 2, 1], dtype=Float32, diff --git a/mindspore/numpy/math_ops.py b/mindspore/numpy/math_ops.py index 46f54389fd..a9be03cd08 100644 --- a/mindspore/numpy/math_ops.py +++ b/mindspore/numpy/math_ops.py @@ -1474,10 +1474,10 @@ def log(x, out=None, where=True, dtype=None): ``Ascend`` ``GPU`` ``CPU`` Examples: - >>> x = np.array([1, 2, 3]).astype('float32') + >>> x = np.array([2, 3, 4]).astype('float32') >>> output = np.log(x) >>> print(output) - [1.09861 1.3862929 1.6094407] + [0.69314575 1.09861 1.3862929 ] """ return _apply_tensor_op(F.log, x, out=out, where=where, dtype=dtype) @@ -1718,8 +1718,8 @@ def amin(a, axis=None, keepdims=False, initial=None, where=True): [0. 1.] >>> output = np.amin(a, axis=1) >>> print(output) - [1. 3.] - >>> output = np.amax(a, where=np.array([False, True]), initial=10, axis=0) + [0, 2] + >>> output = np.amin(a, where=np.array([False, True]), initial=10, axis=0) >>> print(output) [10. 1.] """ @@ -2288,10 +2288,10 @@ def positive(a, out=None, where=True, dtype=None): Examples: >>> import mindspore.numpy as np - >>> a = np.asarray([1, -1]) + >>> a = np.asarray([1, -1]).astype('float32') >>> output = np.positive(a) >>> print(output) - [1, -1] + [1. -1.] """ _check_input_tensor(a) neg_tensor = F.neg_tensor(a) @@ -2328,10 +2328,10 @@ def negative(a, out=None, where=True, dtype=None): Examples: >>> import mindspore.numpy as np - >>> a = np.asarray([1, -1]) + >>> a = np.asarray([1, -1]).astype('float32') >>> output = np.negative(a) >>> print(output) - [-1, 1] + [-1. 1.] """ _check_input_tensor(a) return _apply_tensor_op(F.neg_tensor, a, out=out, where=where, dtype=dtype)