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!17900 numpy-native reduce histogram2d test case fractional precision 

From: @jachua
Reviewed-by: @guoqi1024,@liangchenghui
Signed-off-by: @guoqi1024
This commit is contained in:
mindspore-ci-bot 2021-06-08 14:14:33 +08:00 committed by Gitee
parent 635c2b0adb 849ba87a88
commit af31b0f4a0
1 changed files with 14 additions and 14 deletions
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28
tests/st/numpy_native/test_math_ops.py
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@ -2212,30 +2212,30 @@ def test_histogramdd():
for range in [None, [[0, 5], [2, 7], [1, 3]]]: for range in [None, [[0, 5], [2, 7], [1, 3]]]:
mnp_res = mnp.histogramdd(to_tensor(x), bins=bins, range=range) mnp_res = mnp.histogramdd(to_tensor(x), bins=bins, range=range)
onp_res = onp.histogramdd(x, bins=bins, range=range) onp_res = onp.histogramdd(x, bins=bins, range=range)
match_all_arrays(mnp_res[0], onp_res[0], error=3) match_all_arrays(mnp_res[0], onp_res[0], error=1)
match_all_arrays(mnp_res[1], onp_res[1], error=3) match_all_arrays(mnp_res[1], onp_res[1], error=1)
mnp_res = mnp.histogramdd(to_tensor(x), bins=bins, range=range, density=True) mnp_res = mnp.histogramdd(to_tensor(x), bins=bins, range=range, density=True)
onp_res = onp.histogramdd(x, bins=bins, range=range, density=True) onp_res = onp.histogramdd(x, bins=bins, range=range, density=True)
match_all_arrays(mnp_res[0], onp_res[0], error=3) match_all_arrays(mnp_res[0], onp_res[0], error=1)
match_all_arrays(mnp_res[1], onp_res[1], error=3) match_all_arrays(mnp_res[1], onp_res[1], error=1)
mnp_res = mnp.histogramdd(to_tensor(x), bins=bins, range=range, weights=to_tensor(weights)) mnp_res = mnp.histogramdd(to_tensor(x), bins=bins, range=range, weights=to_tensor(weights))
onp_res = onp.histogramdd(x, bins=bins, range=range, weights=weights) onp_res = onp.histogramdd(x, bins=bins, range=range, weights=weights)
match_all_arrays(mnp_res[0], onp_res[0], error=3) match_all_arrays(mnp_res[0], onp_res[0], error=1)
match_all_arrays(mnp_res[1], onp_res[1], error=3) match_all_arrays(mnp_res[1], onp_res[1], error=1)
mnp_res = mnp.histogramdd(to_tensor(x), bins=bins, range=range, mnp_res = mnp.histogramdd(to_tensor(x), bins=bins, range=range,
weights=to_tensor(weights), density=True) weights=to_tensor(weights), density=True)
mnp_res = mnp.histogramdd(mnp_y, bins=bins, range=range, weights=to_tensor(weights), mnp_res = mnp.histogramdd(mnp_y, bins=bins, range=range, weights=to_tensor(weights),
density=True) density=True)
onp_res = onp.histogramdd(y, bins, range=range, weights=weights, density=True) onp_res = onp.histogramdd(y, bins, range=range, weights=weights, density=True)
match_all_arrays(mnp_res[0], onp_res[0], error=3) match_all_arrays(mnp_res[0], onp_res[0], error=1)
match_all_arrays(mnp_res[1], onp_res[1], error=3) match_all_arrays(mnp_res[1], onp_res[1], error=1)
bins = onp.arange(24).reshape(3, 8) bins = onp.arange(24).reshape(3, 8)
mnp_res = mnp.histogramdd(to_tensor(x), bins=to_tensor(bins)) mnp_res = mnp.histogramdd(to_tensor(x), bins=to_tensor(bins))
onp_res = onp.histogramdd(x, bins=bins) onp_res = onp.histogramdd(x, bins=bins)
match_all_arrays(mnp_res[0], onp_res[0], error=3) match_all_arrays(mnp_res[0], onp_res[0], error=1)
match_all_arrays(mnp_res[1], onp_res[1], error=3) match_all_arrays(mnp_res[1], onp_res[1], error=1)
@pytest.mark.level1 @pytest.mark.level1
@ -2252,17 +2252,17 @@ def test_histogram2d():
for bins in [(5, 7), 4, [onp.arange(5).tolist(), onp.arange(2, 10).tolist()], [8, [1, 2, 3]]]: for bins in [(5, 7), 4, [onp.arange(5).tolist(), onp.arange(2, 10).tolist()], [8, [1, 2, 3]]]:
# pylint: disable=redefined-builtin # pylint: disable=redefined-builtin
for range in [None, [(3, 3), (2, 20)]]: for range in [None, [(3, 3), (2, 20)]]:
match_res(mnp.histogram2d, onp.histogram2d, x, y, bins=bins, range=range, error=3) match_res(mnp.histogram2d, onp.histogram2d, x, y, bins=bins, range=range, error=1)
match_res(mnp.histogram2d, onp.histogram2d, x, y, bins=bins, range=range, density=True, match_res(mnp.histogram2d, onp.histogram2d, x, y, bins=bins, range=range, density=True,
error=3) error=1)
mnp_res = mnp.histogram2d(to_tensor(x), to_tensor(y), bins=bins, range=range, mnp_res = mnp.histogram2d(to_tensor(x), to_tensor(y), bins=bins, range=range,
weights=to_tensor(weights)) weights=to_tensor(weights))
onp_res = onp.histogram2d(x, y, bins=bins, range=range, weights=weights) onp_res = onp.histogram2d(x, y, 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.histogram2d(to_tensor(x), to_tensor(y), bins=bins, range=range, mnp_res = mnp.histogram2d(to_tensor(x), to_tensor(y), bins=bins, range=range,
weights=to_tensor(weights), density=True) weights=to_tensor(weights), density=True)
onp_res = onp.histogram2d(x, y, bins=bins, range=range, weights=weights, density=True) onp_res = onp.histogram2d(x, y, 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 @pytest.mark.level1