def test_normalize_integer():
for dtype in ['int', 'int8', 'int16', 'int32', 'int64',
'uint8', 'uint16', 'uint32', 'uint64']:
a = Integer(2, 30, transform="normalize", dtype=dtype)
for X in range(2, 31):
X_orig = a.inverse_transform(a.transform(X))
assert_array_equal(X_orig, X)
for dtype in [int, np.int8, np.int16, np.int32, np.int64,
np.uint8, np.uint16, np.uint32, np.uint64]:
a = Integer(2, 30, transform="normalize", dtype=dtype)
for X in range(2, 31):
X_orig = a.inverse_transform(a.transform(X))
assert_array_equal(X_orig, X)
assert isinstance(X_orig, dtype)
def test_integer():
a = Integer(1, 10)
for i in range(50):
yield (check_limits, a.rvs(random_state=i), 1, 11)
random_values = a.rvs(random_state=0, n_samples=10)
assert_array_equal(random_values.shape, (10))
assert_array_equal(a.transform(random_values), random_values)
assert_array_equal(a.inverse_transform(random_values), random_values)
def test_integer():
a = Integer(1, 10)
for i in range(50):
yield (check_limits, a.rvs(random_state=i), 1, 11)
random_values = a.rvs(random_state=0, n_samples=10)
assert_array_equal(random_values.shape, (10))
assert_array_equal(a.transform(random_values), random_values)
assert_array_equal(a.inverse_transform(random_values), random_values)
def test_integer():
a = Integer(1, 10)
for i in range(50):
r = a.rvs(random_state=i)
assert_less_equal(1, r)
assert_greater_equal(11, r)
assert_true(r in a)
random_values = a.rvs(random_state=0, n_samples=10)
assert_array_equal(random_values.shape, (10))
assert_array_equal(a.transform(random_values), random_values)
assert_array_equal(a.inverse_transform(random_values), random_values)
def test_integer():
a = Integer(1, 10)
for i in range(50):
r = a.rvs(random_state=i)
assert 1 <= r
assert 11 >= r
assert r in a
random_values = a.rvs(random_state=0, n_samples=10)
assert_array_equal(random_values.shape, (10))
assert_array_equal(a.transform(random_values), random_values)
assert_array_equal(a.inverse_transform(random_values), random_values)
def test_normalize_integer():
a = Integer(2, 30, transform="normalize")
for i in range(50):
check_limits(a.rvs(random_state=i), 2, 30)
assert_array_equal(a.transformed_bounds, (0, 1))
X = rng.randint(2, 31, dtype=np.int64)
# Check transformed values are in [0, 1]
assert np.all(a.transform(X) <= np.ones_like(X))
assert np.all(np.zeros_like(X) <= a.transform(X))
# Check inverse transform
X_orig = a.inverse_transform(a.transform(X))
assert isinstance(X_orig, np.int64)
assert_array_equal(X_orig, X)
a = Integer(2, 30, transform="normalize", dtype=int)
X = rng.randint(2, 31, dtype=int)
# Check inverse transform
X_orig = a.inverse_transform(a.transform(X))
assert isinstance(X_orig, int)
a = Integer(2, 30, transform="normalize", dtype='int')
X = rng.randint(2, 31, dtype=int)
# Check inverse transform
X_orig = a.inverse_transform(a.transform(X))
assert isinstance(X_orig, int)
a = Integer(2,
30,
prior="log-uniform",
base=2,
transform="normalize",
dtype=int)
for i in range(50):
check_limits(a.rvs(random_state=i), 2, 30)
assert_array_equal(a.transformed_bounds, (0, 1))
X = rng.randint(2, 31, dtype=int)
# Check transformed values are in [0, 1]
assert np.all(a.transform(X) <= np.ones_like(X))
assert np.all(np.zeros_like(X) <= a.transform(X))
# Check inverse transform
X_orig = a.inverse_transform(a.transform(X))
assert isinstance(X_orig, int)
assert_array_equal(X_orig, X)