def test_random_uniform(vector_array):
with pytest.raises(Exception):
vector_array.random(-1)
for c in (0, 1, 2, 30):
for low in (-1e-3, 0, 7):
for high in (0.5, 7):
if c > 0 and high <= low:
with pytest.raises(ValueError):
vector_array.random(c, low=low, high=high)
continue
seed = 123
try:
v = vector_array.random(c, low=low, high=high, seed=seed)
except ValueError as e:
if high <= low:
continue
raise e
assert v.space == vector_array.space
assert len(v) == c
if min(v.dim, c) > 0:
assert np.all(v.sup_norm() < max(abs(low), abs(high)))
try:
x = v.to_numpy()
assert x.shape == (c, v.dim)
assert np.all(x < high)
assert np.all(x >= low)
except NotImplementedError:
pass
vv = vector_array.random(c,
distribution='uniform',
low=low,
high=high,
seed=seed)
assert np.allclose((v - vv).sup_norm(), 0.)
def test_random_uniform(vector_array):
with pytest.raises(Exception):
vector_array.random(-1)
for c in (0, 1, 2, 30):
for low in (-1e-3, 0, 7):
for high in (0.5, 7):
if c > 0 and high <= low:
with pytest.raises(ValueError):
vector_array.random(c, low=low, high=high)
continue
seed = 123
try:
v = vector_array.random(c, low=low, high=high, seed=seed)
except ValueError as e:
if high <= low:
continue
raise e
assert v.space == vector_array.space
assert len(v) == c
if min(v.dim, c) > 0:
assert np.all(v.sup_norm() < max(abs(low), abs(high)))
try:
x = v.to_numpy()
assert x.shape == (c, v.dim)
assert np.all(x < high)
assert np.all(x >= low)
except NotImplementedError:
pass
vv = vector_array.random(c, distribution='uniform', low=low, high=high, seed=seed)
assert np.allclose((v - vv).sup_norm(), 0.)
def test_random_normal(vector_array):
with pytest.raises(Exception):
vector_array.random(-1)
for c in (0, 1, 2, 30):
for loc in (-1e-3, 0, 7):
for scale in (-1, 0.5, 7):
if c > 0 and scale <= 0:
with pytest.raises(ValueError):
vector_array.random(c, 'normal', loc=loc, scale=scale)
continue
seed = 123
try:
v = vector_array.random(c,
'normal',
loc=loc,
scale=scale,
seed=seed)
except ValueError as e:
if scale <= 0:
continue
raise e
assert v.space == vector_array.space
assert len(v) == c
try:
x = v.to_numpy()
assert x.shape == (c, v.dim)
import scipy.stats
n = x.size
if n == 0:
continue
# test for expected value
norm = scipy.stats.norm()
gamma = 1 - 1e-7
alpha = 1 - gamma
lower = np.sum(x) / n - norm.ppf(1 - alpha /
2) * scale / np.sqrt(n)
upper = np.sum(x) / n + norm.ppf(1 - alpha /
2) * scale / np.sqrt(n)
assert lower <= loc <= upper
except NotImplementedError:
pass
vv = vector_array.random(c,
'normal',
loc=loc,
scale=scale,
seed=seed)
assert np.allclose((v - vv).sup_norm(), 0.)
def test_random_normal(vector_array):
with pytest.raises(Exception):
vector_array.random(-1)
for c in (0, 1, 2, 30):
for loc in (-1e-3, 0, 7):
for scale in (-1, 0.5, 7):
if c > 0 and scale <= 0:
with pytest.raises(ValueError):
vector_array.random(c, 'normal', loc=loc, scale=scale)
continue
seed = 123
try:
v = vector_array.random(c, 'normal', loc=loc, scale=scale, seed=seed)
except ValueError as e:
if scale <= 0:
continue
raise e
assert v.space == vector_array.space
assert len(v) == c
try:
x = v.to_numpy()
assert x.shape == (c, v.dim)
import scipy.stats
n = x.size
if n == 0:
continue
# test for expected value
norm = scipy.stats.norm()
gamma = 1 - 1e-7
alpha = 1 - gamma
lower = np.sum(x)/n - norm.ppf(1 - alpha/2) * scale / np.sqrt(n)
upper = np.sum(x)/n + norm.ppf(1 - alpha/2) * scale / np.sqrt(n)
assert lower <= loc <= upper
except NotImplementedError:
pass
vv = vector_array.random(c, 'normal', loc=loc, scale=scale, seed=seed)
assert np.allclose((v - vv).sup_norm(), 0.)