def test_select_proportional_to_weight(self):
# Tests that multinomial_wo_replacement selects elements, on average,
# proportional to the their probabilities
th_rng = RandomStream(12345)
p = fmatrix()
n = iscalar()
m = th_rng.multinomial_wo_replacement(pvals=p, n=n)
f = function([p, n], m, allow_input_downcast=True)
n_elements = 100
n_selected = 10
mean_rtol = 0.0005
np.random.seed(12345)
pvals = np.random.randint(1, 100, (1, n_elements)).astype(config.floatX)
pvals /= pvals.sum(1)
avg_pvals = np.zeros((n_elements,), dtype=config.floatX)
for rep in range(10000):
res = f(pvals, n_selected)
res = np.squeeze(res)
avg_pvals[res] += 1
avg_pvals /= avg_pvals.sum()
avg_diff = np.mean(abs(avg_pvals - pvals))
assert avg_diff < mean_rtol
def test_f16_nonzero(mode=None, op_to_check=rng_mrg.mrg_uniform):
srng = MRG_RandomStream(seed=utt.fetch_seed())
m = srng.uniform(size=(1000, 1000), dtype="float16")
assert m.dtype == "float16", m.type
f = function([], m, mode=mode)
assert any(isinstance(n.op, op_to_check) for n in f.maker.fgraph.apply_nodes)
m_val = f()
assert np.all((0 < m_val) & (m_val < 1))
def test_get_substream_rstates():
with config.change_flags(compute_test_value="raise"):
n_streams = 100
dtype = "float32"
rng = MRG_RandomStream(np.random.randint(2147462579))
rng.get_substream_rstates(n_streams, dtype)
def test_get_substream_rstates():
with config.change_flags(compute_test_value="raise"):
n_streams = 100
dtype = "float32"
rng = MRG_RandomStream(
np.random.default_rng(utt.fetch_seed()).integers(2147462579))
rng.get_substream_rstates(n_streams, dtype)
def test_multiple_rng_aliasing():
# Test that when we have multiple random number generators, we do not alias
# the state_updates member. `state_updates` can be useful when attempting to
# copy the (random) state between two similar aesara graphs. The test is
# meant to detect a previous bug where state_updates was initialized as a
# class-attribute, instead of the __init__ function.
rng1 = MRG_RandomStream(1234)
rng2 = MRG_RandomStream(2392)
assert rng1.state_updates is not rng2.state_updates
def test_undefined_grad_opt():
# Make sure that undefined grad get removed in optimized graph.
random = MRG_RandomStream(np.random.randint(1, 2147462579))
pvals = shared(np.random.rand(10, 20).astype(config.floatX))
pvals = pvals / pvals.sum(axis=1)
pvals = zero_grad(pvals)
samples = random.multinomial(pvals=pvals, n=1)
samples = cast(samples, pvals.dtype)
samples = zero_grad(samples)
cost = tt_sum(samples + pvals)
grad_out = grad(cost, samples)
f = function([], grad_out)
assert not any(
[isinstance(node.op, UndefinedGrad) for node in f.maker.fgraph.apply_nodes]
)
def test_simple_shared_mrg_random():
aesara_rng = MRG_RandomStream(10)
values, updates = scan(
lambda: aesara_rng.uniform((2, ), -1, 1),
[],
[],
[],
n_steps=5,
truncate_gradient=-1,
go_backwards=False,
)
my_f = function([], values, updates=updates, allow_input_downcast=True)
# Just check for run-time errors
my_f()
my_f()
def test_seed_fn():
idx = ivector()
for new_seed, same in [(234, True), (None, True), (23, False)]:
random = MRG_RandomStream(234)
fn1 = function([], random.uniform((2, 2), dtype="float32"))
fn2 = function([], random.uniform((3, 3), nstreams=2, dtype="float32"))
fn3 = function([idx], random.uniform(idx, nstreams=3, ndim=1, dtype="float32"))
fn1_val0 = fn1()
fn1_val1 = fn1()
assert not np.allclose(fn1_val0, fn1_val1)
fn2_val0 = fn2()
fn2_val1 = fn2()
assert not np.allclose(fn2_val0, fn2_val1)
fn3_val0 = fn3([4])
fn3_val1 = fn3([4])
assert not np.allclose(fn3_val0, fn3_val1)
assert fn1_val0.size == 4
assert fn2_val0.size == 9
random.seed(new_seed)
fn1_val2 = fn1()
fn1_val3 = fn1()
fn2_val2 = fn2()
fn2_val3 = fn2()
fn3_val2 = fn3([4])
fn3_val3 = fn3([4])
assert np.allclose(fn1_val0, fn1_val2) == same
assert np.allclose(fn1_val1, fn1_val3) == same
assert np.allclose(fn2_val0, fn2_val2) == same
assert np.allclose(fn2_val1, fn2_val3) == same
assert np.allclose(fn3_val0, fn3_val2) == same
assert np.allclose(fn3_val1, fn3_val3) == same
def test_undefined_grad_opt():
# Make sure that undefined grad get removed in optimized graph.
random = MRG_RandomStream(np.random.default_rng().integers(1, 2147462579))
pvals = aesara.shared(np.random.random((10, 20)).astype(config.floatX))
pvals = pvals / pvals.sum(axis=1)
pvals = zero_grad(pvals)
samples = random.multinomial(pvals=pvals, n=1)
samples = at.cast(samples, pvals.dtype)
samples = zero_grad(samples)
cost = at_sum(samples + pvals)
grad_res = grad(cost, samples)
f = aesara.function([], grad_res)
assert not any(
isinstance(node.op, UndefinedGrad) for node in f.maker.fgraph.apply_nodes
)
def test_dump_load_mrg(self):
rng = MRG_RandomStream()
with open("test", "wb") as f:
dump(rng, f)
with open("test", "rb") as f:
rng = load(f)
assert type(rng) == MRG_RandomStream
def test_fail_select_alot(self):
# Tests that multinomial_wo_replacement fails when asked to sample more
# elements than the actual number of elements
th_rng = RandomStream(12345)
p = fmatrix()
n = iscalar()
m = th_rng.multinomial_wo_replacement(pvals=p, n=n)
f = function([p, n], m, allow_input_downcast=True)
n_elements = 100
n_selected = 200
np.random.seed(12345)
pvals = np.random.randint(1, 100, (1, n_elements)).astype(config.floatX)
pvals /= pvals.sum(1)
with pytest.raises(ValueError):
f(pvals, n_selected)
def test_cpu_target_with_shared_variable():
srng = MRG_RandomStream()
s = np.random.rand(2, 3).astype("float32")
x = gpuarray_shared_constructor(s, name="x")
try:
# To have aesara.shared(x) try to move on the GPU
aesara.compile.shared_constructor(gpuarray_shared_constructor)
y = srng.uniform(x.shape, target="cpu")
y.name = "y"
z = (x * y).sum()
z.name = "z"
fz = aesara.function([], z, mode=mode)
nodes = fz.maker.fgraph.toposort()
assert not any(isinstance(node.op, GPUA_mrg_uniform) for node in nodes)
finally:
aesara.compile.shared_constructor(gpuarray_shared_constructor,
remove=True)
def test_multinomial():
steps = 100
if (
config.mode in ("DEBUG_MODE", "DebugMode", "FAST_COMPILE")
or config.mode == "Mode"
and config.linker in ["py"]
):
sample_size = (49, 5)
else:
sample_size = (450, 6)
pvals = np.asarray(np.random.uniform(size=sample_size))
pvals = np.apply_along_axis(lambda row: row / np.sum(row), 1, pvals)
R = MRG_RandomStream(234)
# Note: we specify `nstreams` to avoid a warning.
m = R.multinomial(pvals=pvals, dtype=config.floatX, nstreams=30 * 256)
f = function([], m)
f()
basic_multinomialtest(f, steps, sample_size, pvals, n_samples=1, prefix="mrg ")
def test_multinomial_n_samples():
if (
config.mode in ("DEBUG_MODE", "DebugMode", "FAST_COMPILE")
or config.mode == "Mode"
and config.linker in ["py"]
):
sample_size = (49, 5)
else:
sample_size = (450, 6)
pvals = np.asarray(np.random.uniform(size=sample_size))
pvals = np.apply_along_axis(lambda row: row / np.sum(row), 1, pvals)
R = MRG_RandomStream(234)
for n_samples, steps in zip([5, 10, 100, 1000], [20, 10, 1, 1]):
m = R.multinomial(
pvals=pvals, n=n_samples, dtype=config.floatX, nstreams=30 * 256
)
f = function([], m)
basic_multinomialtest(f, steps, sample_size, pvals, n_samples, prefix="mrg ")
sys.stdout.flush()
def test_select_distinct(self):
# Tests that multinomial_wo_replacement always selects distinct elements
th_rng = RandomStream(12345)
p = fmatrix()
n = iscalar()
m = th_rng.multinomial_wo_replacement(pvals=p, n=n)
f = function([p, n], m, allow_input_downcast=True)
n_elements = 1000
all_indices = range(n_elements)
np.random.seed(12345)
for i in [5, 10, 50, 100, 500, n_elements]:
pvals = np.random.randint(1, 100, (1, n_elements)).astype(config.floatX)
pvals /= pvals.sum(1)
res = f(pvals, i)
res = np.squeeze(res)
assert len(res) == i
assert np.all(np.in1d(np.unique(res), all_indices)), res
def test_GPUA_full_fill():
# Make sure the whole sample buffer is filled. Also make sure
# large samples are consistent with CPU results.
# This needs to be large to trigger the problem on GPU
size = (10, 1000)
R = MRG_RandomStream(234)
uni = R.uniform(size, nstreams=60 * 256)
f_cpu = aesara.function([], uni)
rstate_gpu = gpuarray_shared_constructor(
R.state_updates[-1][0].get_value())
new_rstate, sample = GPUA_mrg_uniform.new(rstate_gpu,
ndim=None,
dtype="float32",
size=size)
rstate_gpu.default_update = new_rstate
f_gpu = aesara.function([], sample, mode=mode)
utt.assert_allclose(f_cpu(), f_gpu())
def test_target_parameter():
srng = MRG_RandomStream()
pvals = np.array([[0.98, 0.01, 0.01], [0.01, 0.49, 0.50]])
def basic_target_parameter_test(x):
f = function([], x)
assert isinstance(f(), np.ndarray)
basic_target_parameter_test(srng.uniform((3, 2), target="cpu"))
basic_target_parameter_test(srng.normal((3, 2), target="cpu"))
basic_target_parameter_test(srng.truncated_normal((3, 2), target="cpu"))
basic_target_parameter_test(srng.binomial((3, 2), target="cpu"))
basic_target_parameter_test(
srng.multinomial(pvals=pvals.astype("float32"), target="cpu"))
basic_target_parameter_test(
srng.choice(p=pvals.astype("float32"), replace=False, target="cpu"))
basic_target_parameter_test(
srng.multinomial_wo_replacement(pvals=pvals.astype("float32"),
target="cpu"))
def test_consistency_randomstreams():
# Verify that the random numbers generated by MRG_RandomStream
# are the same as the reference (Java) implementation by L'Ecuyer et al.
seed = 12345
n_samples = 5
n_streams = 12
n_substreams = 7
samples = []
rng = MRG_RandomStream(seed=seed)
for i in range(n_streams):
stream_samples = []
u = rng.uniform(size=(n_substreams, ), nstreams=n_substreams)
f = function([], u)
for j in range(n_samples):
s = f()
stream_samples.append(s)
stream_samples = np.array(stream_samples)
stream_samples = stream_samples.T.flatten()
samples.append(stream_samples)
samples = np.array(samples).flatten()
assert np.allclose(samples, java_samples)
def test_gradient_scan():
# Test for a crash when using MRG inside scan and taking the gradient
# See https://groups.google.com/d/msg/theano-dev/UbcYyU5m-M8/UO9UgXqnQP0J
aesara_rng = MRG_RandomStream(10)
w = shared(np.ones(1, dtype="float32"))
def one_step(x):
return x + aesara_rng.uniform((1, ), dtype="float32") * w
x = vector(dtype="float32")
values, updates = scan(one_step, outputs_info=x, n_steps=10)
gw = grad(aet_sum(values[-1]), w)
f = function([x], gw)
f(np.arange(1, dtype="float32"))
def check_binomial(mean, size, const_size, var_input, input, steps, rtol):
R = MRG_RandomStream(234)
u = R.binomial(size=size, p=mean)
f = function(var_input, u)
f(*input)
# Increase the number of steps if sizes implies only a few samples
if np.prod(const_size) < 10:
steps_ = steps * 100
else:
steps_ = steps
check_basics(
f,
steps_,
const_size,
prefix="mrg cpu",
inputs=input,
allow_01=True,
target_avg=mean,
mean_rtol=rtol,
)
RR = RandomStream(234)
uu = RR.binomial(1, mean, size=size)
ff = function(var_input, uu)
# It's not our problem if numpy generates 0 or 1
check_basics(
ff,
steps_,
const_size,
prefix="numpy",
allow_01=True,
inputs=input,
target_avg=mean,
mean_rtol=rtol,
)
def test_broadcastable():
R = MRG_RandomStream(234)
x = matrix()
size1 = (10, 1)
size2 = (x.shape[0], 1)
pvals_1 = np.random.uniform(0, 1, size=size1)
pvals_1 = pvals_1 / sum(pvals_1)
pvals_2 = R.uniform(size=size2)
pvals_2 = pvals_2 / at_sum(pvals_2)
for distribution in [
R.uniform,
R.normal,
R.truncated_normal,
R.binomial,
R.multinomial,
R.multinomial_wo_replacement,
]:
# multinomial or multinomial_wo_replacement does not support "size" argument,
# the sizes of them are implicitly defined with "pvals" argument.
if distribution in [R.multinomial, R.multinomial_wo_replacement]:
# check when all dimensions are constant
uu = distribution(pvals=pvals_1)
assert uu.broadcastable == (False, True)
# check when some dimensions are aesara variables
uu = distribution(pvals=pvals_2)
assert uu.broadcastable == (False, True)
else:
# check when all dimensions are constant
uu = distribution(size=size1)
assert uu.broadcastable == (False, True)
# check when some dimensions are aesara variables
uu = distribution(size=size2)
assert uu.broadcastable == (False, True)
def test_gradient_scan(mode):
aesara_rng = MRG_RandomStream(10)
w = shared(np.ones(1, dtype="float32"))
def one_step(x):
return x + aesara_rng.uniform((1, ), dtype="float32") * w
x = vector(dtype="float32")
values, updates = scan(one_step, outputs_info=x, n_steps=10)
gw = grad(at_sum(values[-1]), w)
f = function([x], gw, mode=mode)
assert np.allclose(
f(np.arange(1, dtype=np.float32)),
np.array([0.13928187], dtype=np.float32),
rtol=1e6,
)
def test_overflow_cpu():
# run with AESARA_FLAGS=mode=FAST_RUN,device=cpu,floatX=float32
rng = MRG_RandomStream(np.random.default_rng(utt.fetch_seed()).integers(1234))
fct = rng.uniform
with config.change_flags(compute_test_value="off"):
# should raise error as the size overflows
sizes = [
(2 ** 31,),
(2 ** 32,),
(
2 ** 15,
2 ** 16,
),
(2, 2 ** 15, 2 ** 15),
]
rng_mrg_overflow(sizes, fct, config.mode, should_raise_error=True)
# should not raise error
sizes = [(2 ** 5,), (2 ** 5, 2 ** 5), (2 ** 5, 2 ** 5, 2 ** 5)]
rng_mrg_overflow(sizes, fct, config.mode, should_raise_error=False)
# should support int32 sizes
sizes = [(np.int32(2 ** 10),), (np.int32(2), np.int32(2 ** 10), np.int32(2 ** 10))]
rng_mrg_overflow(sizes, fct, config.mode, should_raise_error=False)
def test_deterministic():
seed = utt.fetch_seed()
sample_size = (10, 20)
R = MRG_RandomStream(seed=seed)
u = R.uniform(size=sample_size)
f = function([], u)
fsample1 = f()
fsample2 = f()
assert not np.allclose(fsample1, fsample2)
R2 = MRG_RandomStream(seed=seed)
u2 = R2.uniform(size=sample_size)
g = function([], u2)
gsample1 = g()
gsample2 = g()
assert np.allclose(fsample1, gsample1)
assert np.allclose(fsample2, gsample2)
def test_truncated_normal():
# just a copy of test_normal0 for truncated normal
steps = 50
std = 2.0
if (config.mode in ("DEBUG_MODE", "DebugMode", "FAST_COMPILE")
or config.mode == "Mode" and config.linker in ["py"]):
sample_size = (25, 30)
default_rtol = 0.02
else:
sample_size = (999, 50)
default_rtol = 0.01
sample_size_odd = (sample_size[0], sample_size[1] - 1)
x = matrix()
test_cases = [
(sample_size, sample_size, [], [], -5.0, default_rtol, default_rtol),
(
x.shape,
sample_size,
[x],
[np.zeros(sample_size, dtype=config.floatX)],
-5.0,
default_rtol,
default_rtol,
),
# test odd value
(
x.shape,
sample_size_odd,
[x],
[np.zeros(sample_size_odd, dtype=config.floatX)],
-5.0,
default_rtol,
default_rtol,
),
(
sample_size,
sample_size,
[],
[],
np.arange(np.prod(sample_size),
dtype="float32").reshape(sample_size),
10.0 * std / np.sqrt(steps),
default_rtol,
),
# test empty size (scalar)
((), (), [], [], -5.0, default_rtol, 0.02),
# test with few samples at the same time
((1, ), (1, ), [], [], -5.0, default_rtol, 0.02),
((3, ), (3, ), [], [], -5.0, default_rtol, 0.02),
]
for size, const_size, var_input, input, avg, rtol, std_tol in test_cases:
R = MRG_RandomStream(234)
# Note: we specify `nstreams` to avoid a warning.
n = R.truncated_normal(
size=size,
avg=avg,
std=std,
nstreams=rng_mrg.guess_n_streams(size, warn=False),
)
f = function(var_input, n)
# Increase the number of steps if size implies only a few samples
if np.prod(const_size) < 10:
steps_ = steps * 60
else:
steps_ = steps
check_basics(
f,
steps_,
const_size,
target_avg=avg,
target_std=std,
prefix="mrg ",
allow_01=True,
inputs=input,
mean_rtol=rtol,
std_tol=std_tol,
)
sys.stdout.flush()
def test_undefined_grad():
srng = MRG_RandomStream(seed=1234)
# checking uniform distribution
low = scalar()
out = srng.uniform((), low=low)
with pytest.raises(NullTypeGradError):
grad(out, low)
high = scalar()
out = srng.uniform((), low=0, high=high)
with pytest.raises(NullTypeGradError):
grad(out, high)
out = srng.uniform((), low=low, high=high)
with pytest.raises(NullTypeGradError):
grad(out, (low, high))
# checking binomial distribution
prob = scalar()
out = srng.binomial((), p=prob)
with pytest.raises(NullTypeGradError):
grad(out, prob)
# checking multinomial distribution
prob1 = scalar()
prob2 = scalar()
p = [as_tensor_variable([prob1, 0.5, 0.25])]
out = srng.multinomial(size=None, pvals=p, n=4)[0]
with pytest.raises(NullTypeGradError):
grad(at_sum(out), prob1)
p = [as_tensor_variable([prob1, prob2])]
out = srng.multinomial(size=None, pvals=p, n=4)[0]
with pytest.raises(NullTypeGradError):
grad(at_sum(out), (prob1, prob2))
# checking choice
p = [as_tensor_variable([prob1, prob2, 0.1, 0.2])]
out = srng.choice(a=None, size=1, p=p, replace=False)[0]
with pytest.raises(NullTypeGradError):
grad(out[0], (prob1, prob2))
p = [as_tensor_variable([prob1, prob2])]
out = srng.choice(a=None, size=1, p=p, replace=False)[0]
with pytest.raises(NullTypeGradError):
grad(out[0], (prob1, prob2))
p = [as_tensor_variable([prob1, 0.2, 0.3])]
out = srng.choice(a=None, size=1, p=p, replace=False)[0]
with pytest.raises(NullTypeGradError):
grad(out[0], prob1)
# checking normal distribution
avg = scalar()
out = srng.normal((), avg=avg)
with pytest.raises(NullTypeGradError):
grad(out, avg)
std = scalar()
out = srng.normal((), avg=0, std=std)
with pytest.raises(NullTypeGradError):
grad(out, std)
out = srng.normal((), avg=avg, std=std)
with pytest.raises(NullTypeGradError):
grad(out, (avg, std))
# checking truncated normal distribution
avg = scalar()
out = srng.truncated_normal((), avg=avg)
with pytest.raises(NullTypeGradError):
grad(out, avg)
std = scalar()
out = srng.truncated_normal((), avg=0, std=std)
with pytest.raises(NullTypeGradError):
grad(out, std)
out = srng.truncated_normal((), avg=avg, std=std)
with pytest.raises(NullTypeGradError):
grad(out, (avg, std))
def test_uniform():
# TODO: test param low, high
# TODO: test size=None
# TODO: test ndim!=size.ndim
# TODO: test bad seed
# TODO: test size=Var, with shape that change from call to call
if (config.mode in ("DEBUG_MODE", "DebugMode", "FAST_COMPILE")
or config.mode == "Mode" and config.linker in ["py"]):
sample_size = (10, 100)
steps = 50
else:
sample_size = (500, 50)
steps = int(1e3)
x = matrix()
for size, const_size, var_input, input in [
(sample_size, sample_size, [], []),
(x.shape, sample_size, [x],
[np.zeros(sample_size, dtype=config.floatX)]),
(
(x.shape[0], sample_size[1]),
sample_size,
[x],
[np.zeros(sample_size, dtype=config.floatX)],
),
# test empty size (scalar)
((), (), [], []),
]:
# TEST CPU IMPLEMENTATION
# The python and C implementation are tested with DebugMode
x = matrix()
R = MRG_RandomStream(234)
# Note: we specify `nstreams` to avoid a warning.
# TODO Look for all occurrences of `guess_n_streams` and `30 * 256`
# for such situations: it would be better to instead filter the
# warning using the warning module.
u = R.uniform(size=size,
nstreams=rng_mrg.guess_n_streams(size, warn=False))
f = function(var_input, u)
assert any(
isinstance(node.op, mrg_uniform)
for node in f.maker.fgraph.toposort())
f(*input)
# Increase the number of steps if sizes implies only a few samples
if np.prod(const_size) < 10:
steps_ = steps * 100
else:
steps_ = steps
check_basics(f, steps_, const_size, prefix="mrg cpu", inputs=input)
RR = RandomStream(234)
uu = RR.uniform(size=size)
ff = function(var_input, uu)
# It's not our problem if numpy generates 0 or 1
check_basics(ff,
steps_,
const_size,
prefix="numpy",
allow_01=True,
inputs=input)
def __init__(self, seed=123):
self.rng = MRG_RandomStream(seed)
self.y = self.rng.uniform(size=(1, ))
def test_normal_truncation():
# just a copy of test_normal0 with extra bound check
steps = 50
std = 2.0
# standard deviation is slightly less than for a regular Gaussian
# constant taken from scipy.stats.truncnorm.std(a=-2, b=2, loc=0., scale=1.)
target_std = 0.87962566103423978 * std
if (config.mode in ("DEBUG_MODE", "DebugMode", "FAST_COMPILE")
or config.mode == "Mode" and config.linker in ["py"]):
sample_size = (25, 30)
default_rtol = 0.02
else:
sample_size = (999, 50)
default_rtol = 0.01
sample_size_odd = (sample_size[0], sample_size[1] - 1)
x = matrix()
test_cases = [
(sample_size, sample_size, [], [], -5.0, default_rtol, default_rtol),
(
x.shape,
sample_size,
[x],
[np.zeros(sample_size, dtype=config.floatX)],
-5.0,
default_rtol,
default_rtol,
),
# test odd value
(
x.shape,
sample_size_odd,
[x],
[np.zeros(sample_size_odd, dtype=config.floatX)],
-5.0,
default_rtol,
default_rtol,
),
(
sample_size,
sample_size,
[],
[],
np.arange(np.prod(sample_size),
dtype="float32").reshape(sample_size),
10.0 * std / np.sqrt(steps),
default_rtol,
),
# test empty size (scalar)
((), (), [], [], -5.0, default_rtol, 0.02),
# test with few samples at the same time
((1, ), (1, ), [], [], -5.0, default_rtol, 0.02),
((3, ), (3, ), [], [], -5.0, default_rtol, 0.02),
]
for size, const_size, var_input, input, avg, rtol, std_tol in test_cases:
R = MRG_RandomStream(234)
# Note: we specify `nstreams` to avoid a warning.
n = R.normal(
size=size,
avg=avg,
std=std,
truncate=True,
nstreams=rng_mrg.guess_n_streams(size, warn=False),
)
f = function(var_input, n)
# check if truncated at 2*std
samples = f(*input)
assert np.all(
avg + 2 * std - samples >= 0), "bad upper bound? {} {}".format(
samples,
avg + 2 * std,
)
assert np.all(samples -
(avg - 2 * std) >= 0), "bad lower bound? {} {}".format(
samples,
avg - 2 * std,
)
# Increase the number of steps if size implies only a few samples
if np.prod(const_size) < 10:
steps_ = steps * 50
else:
steps_ = steps
check_basics(
f,
steps_,
const_size,
target_avg=avg,
target_std=target_std,
prefix="mrg ",
allow_01=True,
inputs=input,
mean_rtol=rtol,
std_tol=std_tol,
)
sys.stdout.flush()
def test_bad_size(self):
R = MRG_RandomStream(234)
for size in [
(0, 100),
(-1, 100),
(1, 0),
]:
with pytest.raises(ValueError):
R.uniform(size)
with pytest.raises(ValueError):
R.binomial(size)
with pytest.raises(ValueError):
R.multinomial(size, 1, [])
with pytest.raises(ValueError):
R.normal(size)
with pytest.raises(ValueError):
R.truncated_normal(size)
