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!16008 numpy-native fix docstrings 

From: @jachua
Reviewed-by: @liangchenghui,@guoqi1024
Signed-off-by: @liangchenghui
This commit is contained in:
mindspore-ci-bot 2021-05-07 10:02:17 +08:00 committed by Gitee
parent 5482a7a855 5aef276bc8
commit 463738a494
4 changed files with 22 additions and 30 deletions
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7
mindspore/numpy/array_creations.py
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@ -1878,9 +1878,10 @@ def blackman(M):
Examples:
>>> import mindspore.numpy as np
>>> print(np.hamming(12))
[0.08000001 0.15302339 0.34890914 0.6054648 0.841236 0.9813669
0.9813668 0.8412359 0.6054647 0.34890908 0.15302327 0.08000001]
>>> print(np.blackman(12))
[-1.4901161e-08 3.2606430e-02 1.5990365e-01 4.1439798e-01
7.3604518e-01 9.6704674e-01 9.6704674e-01 7.3604518e-01
4.1439798e-01 1.5990365e-01 3.2606430e-02 -1.4901161e-08]
"""
if not _check_window_size(M):
return ones(_max(0, M))

6
mindspore/numpy/array_ops.py
View File

@ -1853,7 +1853,7 @@ def take(a, indices, axis=None, mode='clip'):
mode (‘raise’, ‘wrap’, ‘clip’, optional): Specifies how out-of-bounds
indices will behave.
‘raise’ – raise an error (default);
‘raise’ – raise an error;
‘wrap’ – wrap around;
@ -2175,7 +2175,7 @@ def choose(a, choices, mode='clip'):
mode (‘raise’, ‘wrap’, ‘clip’, optional): Specifies how indices outside
``[0, n-1]`` will be treated:
‘raise’ – raise an error (default);
‘raise’ – raise an error;
‘wrap’ – wrap around;
@ -2420,7 +2420,7 @@ def piecewise(x, condlist, funclist, *args, **kw):
>>> import mindspore.numpy as np
>>> x = np.linspace(-2.5, 2.5, 6)
>>> print(np.piecewise(x, [x < 0, x >= 0], [-1, 1]))
[2.5 1.5 0.5 0.5 1.5 2.5]
[-1 -1 -1 1 1 1]
"""
x = _to_tensor(x)
choicelist = funclist

31
mindspore/numpy/math_ops.py
View File

@ -2507,6 +2507,7 @@ def nanmax(a, axis=None, dtype=None, keepdims=False):
``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> a = np.array([[1, 2], [3, np.nan]])
>>> output = np.nanmax(a)
>>> print(output)
@ -2554,6 +2555,7 @@ def nanmin(a, axis=None, dtype=None, keepdims=False):
``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> a = np.array([[1, 2], [3, np.nan]])
>>> output = np.nanmin(a)
>>> print(output)
@ -4242,19 +4244,14 @@ def argmax(a, axis=None):
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> a = np.arange(10, 16).reshape(2, 3)
>>> print(np.argmax(a))
5
>>> a = np.arange(10, 16).reshape(2, 3)
>>> print(np.argmax(a), axis=0)
>>> print(np.argmax(a, axis=0))
[1 1 1]
>>> a = np.arange(10, 16).reshape(2, 3)
>>> print(np.argmax(a), axis=0)
>>> print(np.argmax(a, axis=0))
[2 2]
>>> b = np.array([0, 5, 2, 3, 4, 5])
>>> b[1] = 5
>>> print(np.argmax(b))
1
"""
a = _to_tensor(a)
return a.argmax(axis)
@ -4283,19 +4280,14 @@ def argmin(a, axis=None):
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> a = np.arange(10, 16).reshape(2, 3)
>>> print(np.argmin(a))
0
>>> a = np.arange(10, 16).reshape(2, 3)
>>> print(np.argmin(a), axis=0)
>>> print(np.argmin(a, axis=0))
[0 0 0]
>>> a = np.arange(10, 16).reshape(2, 3)
>>> print(np.argmin(a), axis=0)
>>> print(np.argmin(a, axis=0))
[0 0]
>>> b = np.array([0, 5, 2, 3, 4, 5])
>>> b[1] = 5
>>> print(np.argmin(b))
0
"""
a = _to_tensor(a)
return a.argmin(axis)
@ -4400,7 +4392,6 @@ def interp(x, xp, fp, left=None, right=None):
>>> xp = [1, 2, 3]
>>> fp = [3, 2, 0]
>>> print(np.interp([0, 1, 1.5, 2.72, 3.14], xp, fp))
>>> print(np.searchsorted([1,2,3,4,5], [-10, 10, 2, 3]))
[3. 3. 2.5 0.55999994 0. ]
>>> UNDEF = -99.0
>>> print(np.interp(3.14, xp, fp, right=UNDEF))
@ -4735,7 +4726,7 @@ def histogram(a, bins=10, range=None, weights=None, density=False): # pylint: di
if density:
count = F.cast(count, mstype.float32)
count = count/diff(bin_edges)/F.reduce_sum(count)
return count, bin_edges
return count.astype(mstype.int32), bin_edges
@constexpr
@ -4874,7 +4865,7 @@ def histogramdd(sample, bins=10, range=None, weights=None, density=False): # pyl
shape = _expanded_shape(ndim, dedges[i].size, i)
count /= _to_tensor(dedges[i]).reshape(shape)
count /= s
return count, bin_edges
return count.astype(mstype.int32), bin_edges
def histogram2d(x, y, bins=10, range=None, weights=None, density=False): # pylint: disable=redefined-builtin
@ -4938,7 +4929,7 @@ def histogram2d(x, y, bins=10, range=None, weights=None, density=False): # pylin
5.33333349e+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]
return count.astype(mstype.int32), bin_edges[0], bin_edges[1]
def matrix_power(a, n):

8
tests/st/numpy_native/test_math_ops.py
View File

@ -2176,15 +2176,15 @@ def test_histogram():
for bins in [(1, 2, 3), [2], 1, 5, 10]:
# pylint: disable=redefined-builtin
for range in [None, (3, 3), (2, 20)]:
match_res(mnp.histogram, onp.histogram, x, bins=bins, range=range, error=3)
match_res(mnp.histogram, onp.histogram, x, bins=bins, range=range, density=True, error=3)
match_res(mnp.histogram, onp.histogram, x, bins=bins, range=range, error=1)
match_res(mnp.histogram, onp.histogram, x, bins=bins, range=range, density=True, error=1)
mnp_res = mnp.histogram(to_tensor(x), bins=bins, range=range, weights=to_tensor(weights))
onp_res = onp.histogram(x, bins=bins, range=range, weights=weights)
match_all_arrays(mnp_res, onp_res, error=3)
match_all_arrays(mnp_res, onp_res, error=1)
mnp_res = mnp.histogram(to_tensor(x), bins=bins, range=range,
weights=to_tensor(weights), density=True)
onp_res = onp.histogram(x, bins=bins, range=range, weights=weights, density=True)
match_all_arrays(mnp_res, onp_res, error=3)
match_all_arrays(mnp_res, onp_res, error=1)
@pytest.mark.level1