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new 0f65ef6 nb fix (#18858)
0f65ef6 is described below
commit 0f65ef614ecc1b286e3e67076c2e54c4a48b359a
Author: Xi Wang <[email protected]>
AuthorDate: Wed Aug 5 10:48:50 2020 +0800
nb fix (#18858)
---
tests/python/unittest/test_gluon_probability_v1.py | 4 ++--
tests/python/unittest/test_gluon_probability_v2.py | 4 ++--
2 files changed, 4 insertions(+), 4 deletions(-)
diff --git a/tests/python/unittest/test_gluon_probability_v1.py
b/tests/python/unittest/test_gluon_probability_v1.py
index 82395dd..fe1a2c7 100644
--- a/tests/python/unittest/test_gluon_probability_v1.py
+++ b/tests/python/unittest/test_gluon_probability_v1.py
@@ -540,7 +540,7 @@ def test_gluon_negative_binomial_v1():
# Test log_prob
for shape, hybridize, use_logit in itertools.product(shapes, [True,
False], [True, False]):
n = np.random.randint(1, 10, size=shape).astype('float32')
- prob = np.random.uniform(low=0.1, size=shape).astype('float32')
+ prob = np.random.uniform(low=0.2, high=0.6,
size=shape).astype('float32')
sample = np.random.randint(0, 10, size=shape).astype('float32')
param = prob
if use_logit:
@@ -559,7 +559,7 @@ def test_gluon_negative_binomial_v1():
for func in ['mean', 'variance']:
for use_logit in [True, False]:
n = np.random.randint(1, 10, size=shape).astype('float32')
- prob = np.random.uniform(low=0.1, size=shape).astype('float32')
+ prob = np.random.uniform(low=0.2, high=0.6,
size=shape).astype('float32')
net = TestNegativeBinomial(func, use_logit)
param = prob
if use_logit:
diff --git a/tests/python/unittest/test_gluon_probability_v2.py
b/tests/python/unittest/test_gluon_probability_v2.py
index dc8ac14..50eaa5d 100644
--- a/tests/python/unittest/test_gluon_probability_v2.py
+++ b/tests/python/unittest/test_gluon_probability_v2.py
@@ -540,7 +540,7 @@ def test_gluon_negative_binomial():
# Test log_prob
for shape, hybridize, use_logit in itertools.product(shapes, [True,
False], [True, False]):
n = np.random.randint(1, 10, size=shape).astype('float32')
- prob = np.random.uniform(low=0.1, size=shape)
+ prob = np.random.uniform(low=0.2, high=0.6, size=shape)
sample = np.random.randint(0, 10, size=shape).astype('float32')
param = prob
if use_logit:
@@ -559,7 +559,7 @@ def test_gluon_negative_binomial():
for func in ['mean', 'variance']:
for use_logit in [True, False]:
n = np.random.randint(1, 10, size=shape).astype('float32')
- prob = np.random.uniform(low=0.1, size=shape)
+ prob = np.random.uniform(low=0.2, high=0.6, size=shape)
net = TestNegativeBinomial(func, use_logit)
param = prob
if use_logit:
