From 44bb28ff3c2013d1779894f3a7768cc5deffe83b Mon Sep 17 00:00:00 2001 From: huangmengxi Date: Mon, 12 Jul 2021 02:49:49 +0000 Subject: [PATCH] fix numpy docstrings --- mindspore/numpy/array_creations.py | 24 ++++++++-------- mindspore/numpy/array_ops.py | 32 ++++++++++----------- mindspore/numpy/math_ops.py | 45 ++++++++++++++---------------- 3 files changed, 49 insertions(+), 52 deletions(-) diff --git a/mindspore/numpy/array_creations.py b/mindspore/numpy/array_creations.py index e7ebb63d54c..b5e83924e36 100644 --- a/mindspore/numpy/array_creations.py +++ b/mindspore/numpy/array_creations.py @@ -1116,26 +1116,26 @@ def diagonal(a, offset=0, axis1=0, axis2=1): Examples: >>> import mindspore.numpy as np - >>> a = np.arange(4).reshape(2,2) + >>> a = np.arange(4).reshape(2,2).astype(np.float32) >>> print(a) - [[0 1] - [2 3]] + [[0. 1.] + [2. 3.]] >>> output = np.diagonal(a) >>> print(output) - [0 3] + [0. 3.] >>> output = np.diagonal(a, 1) >>> print(output) - [1] - >>> a = np.arange(8).reshape(2, 2, 2) + [1.] + >>> a = np.arange(8).reshape(2, 2, 2).astype(np.float32) >>> print(a) - [[[0 1] - [2 3]] - [[4 5] - [6 7]]] + [[[0. 1.] + [2. 3.]] + [[4. 5.] + [6. 7.]]] >>> output = np.diagonal(a, 0, 0, 1) >>> print(output) - [[0 6] - [1 7]] + [[0. 6.] + [1. 7.]] """ return a.diagonal(offset=offset, axis1=axis1, axis2=axis2) diff --git a/mindspore/numpy/array_ops.py b/mindspore/numpy/array_ops.py index 132f30e9e51..e7f01776ab3 100644 --- a/mindspore/numpy/array_ops.py +++ b/mindspore/numpy/array_ops.py @@ -1653,16 +1653,16 @@ def flip(m, axis=None): >>> A = np.arange(8.0).reshape((2,2,2)) >>> output = np.flip(A) >>> print(output) - [[[7, 6], - [5, 4]], - [[3, 2], - [1, 0]]] + [[[7. 6] + [5. 4]] + [[3. 2] + [1. 0]]] >>> output = np.flip(A, (0, 2)) >>> print(output) - [[[5, 4], - [7, 6]], - [[1, 0], - [3, 2]]] + [[[5. 4] + [7. 6]] + [[1. 0] + [3. 2]]] """ _check_input_tensor(m) ndim = F.rank(m) @@ -1707,10 +1707,10 @@ def flipud(m): >>> A = np.arange(8.0).reshape((2,2,2)) >>> output = np.flipud(A) >>> print(output) - [[[4., 5.], - [6., 7.]], - [[0., 1.], - [2., 3.]]] + [[[4. 5.] + [6. 7.]] + [[0. 1.] + [2. 3.]]] """ return flip(m, 0) @@ -1740,10 +1740,10 @@ def fliplr(m): >>> A = np.arange(8.0).reshape((2,2,2)) >>> output = np.fliplr(A) >>> print(output) - [[[2., 3.], - [0., 1.]], - [[6., 7.], - [4., 5.]]] + [[[2. 3.] + [0. 1.]] + [[6. 7.] + [4. 5.]]] """ return flip(m, 1) diff --git a/mindspore/numpy/math_ops.py b/mindspore/numpy/math_ops.py index b898b008f67..ed25813e789 100644 --- a/mindspore/numpy/math_ops.py +++ b/mindspore/numpy/math_ops.py @@ -2213,7 +2213,7 @@ def convolve(a, v, mode='full'): >>> import mindspore.numpy as np >>> output = np.convolve([1., 2., 3., 4., 5.], [2., 3.], mode="valid") >>> print(output) - [ 3. 6. 9. 12.] + [ 7. 12. 17. 22.] """ if not isinstance(a, Tensor): a = asarray_const(a) @@ -2926,6 +2926,7 @@ def cross(a, b, axisa=- 1, axisb=- 1, axisc=- 1, axis=None): [[-3 6 -3] [ 3 -6 3]] >>> output = np.cross(x, y, axisc=0) + >>> print(output) [[-3 3] [ 6 -6] [-3 3]] @@ -3785,7 +3786,7 @@ def arctan2(x1, x2, dtype=None): if both `x1` and `x2` are scalars. Supported Platforms: - ``Ascend`` ``CPU`` + ``Ascend`` ``CPU`` ``GPU`` Examples: >>> import mindspore.numpy as np @@ -4430,6 +4431,7 @@ def sign(x, dtype=None): Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are not supported. Complex inputs are not supported now. + On Ascend, integer inputs are not supported. Args: x (Union[int, float, list, tuple, Tensor]): Input values. @@ -4683,14 +4685,14 @@ def histogram(a, bins=10, range=None, weights=None, density=False): # pylint: di Examples: >>> from mindspore import numpy as np >>> print(np.histogram([1, 2, 1], bins=[0, 1, 2, 3])) - (Tensor(shape=[3], dtype=Float32, value= [0, 2, 1]), + (Tensor(shape=[3], dtype=Float32, value= [ 0.00000000e+00, 2.00000000e+00, 1.00000000e+00]), Tensor(shape=[4], dtype=Int32, value= [0, 1, 2, 3])) >>> print(np.histogram(np.arange(4), bins=np.arange(5), density=True)) (Tensor(shape=[4], dtype=Float32, value= [ 2.50000000e-01, 2.50000000e-01, 2.50000000e-01, 2.50000000e-01]), Tensor(shape=[5], dtype=Int32, value= [0, 1, 2, 3, 4])) >>> print(np.histogram([[1, 2, 1], [1, 0, 1]], bins=[0,1,2,3])) - (Tensor(shape=[3], dtype=Float32, value= [1, 4, 1]), + (Tensor(shape=[3], dtype=Float32, value= [ 1.00000000e+00, 4.00000000e+00, 1.00000000e+00]), Tensor(shape=[4], dtype=Int32, value= [0, 1, 2, 3])) """ a = _to_tensor(a) @@ -4779,14 +4781,13 @@ def histogramdd(sample, bins=10, range=None, weights=None, density=False): # pyl [12 13 14]] >>> print(np.histogramdd(sample, bins=(2, 3, 4))) (Tensor(shape=[2, 3, 4], dtype=Float32, value= - [[[1, 1, 0, 0], - [0, 0, 0, 0], - [0, 0, 0, 0]], - [[0, 0, 0, 0], - [0, 0, 1, 0], - [0, 0, 0, 2]]]), - [Tensor(shape=[3], dtype=Float32, value= - [ 0.00000000e+00, 6.00000000e+00, 1.20000000e+01]), + [[[ 1.00000000e+00, 1.00000000e+00, 0.00000000e+00, 0.00000000e+00], + [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], + [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]], + [[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], + [ 0.00000000e+00, 0.00000000e+00, 1.00000000e+00, 0.00000000e+00], + [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.00000000e+00]]]), + [Tensor(shape=[3], dtype=Float32, value= [ 0.00000000e+00, 6.00000000e+00, 1.20000000e+01]), Tensor(shape=[4], dtype=Float32, value= [ 1.00000000e+00, 5.00000000e+00, 9.00000000e+00, 1.30000000e+01]), Tensor(shape=[5], dtype=Float32, value= @@ -4900,17 +4901,13 @@ def histogram2d(x, y, bins=10, range=None, weights=None, density=False): # pylin >>> from mindspore import numpy as np >>> x = np.arange(5) >>> y = np.arange(2, 7) - >>> print(np.histogram2d(x, y, bins=(4, 6))) - (Tensor(shape=[4, 6], dtype=Float32, value= - [[1, 0, 0, 0, 0, 0], - [0, 1, 0, 0, 0, 0], - [0, 0, 0, 1, 0, 0] - [0, 0, 0, 0, 1, 1]]), - Tensor(shape=[5], dtype=Float32, value= - [ 0.00000000e+00, 1.00000000e+00, 2.00000000e+00, 3.00000000e+00, 4.00000000e+00]), - Tensor(shape=[7], dtype=Float32, value= - [ 2.00000000e+00, 2.66666675e+00, 3.33333349e+00, 4.00000000e+00, 4.66666698e+00, - 5.33333349e+00, 6.00000000e+00])) + >>> print(np.histogram2d(x, y, bins=(2, 3))) + (Tensor(shape=[2, 3], dtype=Float32, value= + [[ 2.00000000e+00, 0.00000000e+00, 0.00000000e+00], + [ 0.00000000e+00, 1.00000000e+00, 2.00000000e+00]]), + Tensor(shape=[3], dtype=Float32, value= [ 0.00000000e+00, 2.00000000e+00, 4.00000000e+00]), + Tensor(shape=[4], dtype=Float32, value= + [ 2.00000000e+00, 3.33333349e+00, 4.66666698e+00, 6.00000000e+00])) """ count, bin_edges = histogramdd((x, y), bins=bins, range=range, weights=weights, density=density) return count, bin_edges[0], bin_edges[1] @@ -5367,7 +5364,7 @@ def cumprod(a, axis=None, dtype=None): ValueError: If axis is out of range. Supported Platforms: - ``Ascend`` + ``Ascend`` ``GPU`` Examples: >>> import mindspore.numpy as np