def test_binomial_vector(self):
rng_R = random_state_type()
n = tensor.lvector()
prob = tensor.vector()
post_r, out = binomial(rng_R, n=n, p=prob)
assert out.ndim == 1
f = compile.function([rng_R, n, prob], [post_r, out],
accept_inplace=True)
n_val = [1, 2, 3]
prob_val = np.asarray([0.1, 0.2, 0.3], dtype=config.floatX)
rng = np.random.RandomState(utt.fetch_seed())
numpy_rng = np.random.RandomState(utt.fetch_seed())
# Arguments of size (3,)
rng0, val0 = f(rng, n_val, prob_val)
numpy_val0 = numpy_rng.binomial(n=n_val, p=prob_val)
assert np.all(val0 == numpy_val0)
# arguments of size (2,)
rng1, val1 = f(rng0, n_val[:-1], prob_val[:-1])
numpy_val1 = numpy_rng.binomial(n=n_val[:-1], p=prob_val[:-1])
assert np.all(val1 == numpy_val1)
# Specifying the size explicitly
g = compile.function(
[rng_R, n, prob],
binomial(rng_R, n=n, p=prob, size=(3, )),
accept_inplace=True,
)
rng2, val2 = g(rng1, n_val, prob_val)
numpy_val2 = numpy_rng.binomial(n=n_val, p=prob_val, size=(3, ))
assert np.all(val2 == numpy_val2)
with pytest.raises(ValueError):
g(rng2, n_val[:-1], prob_val[:-1])
def test_binomial(self):
# Test that raw_random.binomial generates the same results as numpy.
# Check over two calls to see if the random state is correctly updated.
rng_R = random_state_type()
# Use non-default parameters, and larger dimensions because of
# the integer nature of the result
post_r, bin = binomial(rng_R, (7, 12), 5, 0.8)
f = compile.function(
[
compile.In(
rng_R,
value=np.random.RandomState(utt.fetch_seed()),
update=post_r,
mutable=True,
)
],
[bin],
accept_inplace=True,
)
numpy_rng = np.random.RandomState(utt.fetch_seed())
val0 = f()
val1 = f()
numpy_val0 = numpy_rng.binomial(5, 0.8, size=(7, 12))
numpy_val1 = numpy_rng.binomial(5, 0.8, size=(7, 12))
assert np.all(val0 == numpy_val0)
assert np.all(val1 == numpy_val1)
def test_pkl(self):
# Test pickling of RandomFunction.
# binomial was created by calling RandomFunction on a string,
# random_integers by calling it on a function.
rng_r = random_state_type()
mode = None
if theano.config.mode in ["DEBUG_MODE", "DebugMode"]:
mode = "FAST_COMPILE"
post_bin_r, bin_sample = binomial(rng_r, (3, 5), 1, 0.3)
f = theano.function([rng_r], [post_bin_r, bin_sample], mode=mode)
pickle.dumps(f)
post_int_r, int_sample = random_integers(rng_r, (3, 5), -1, 8)
g = theano.function([rng_r], [post_int_r, int_sample], mode=mode)
pkl_g = pickle.dumps(g)
pickle.loads(pkl_g)
def test_infer_shape(self):
rng_R = random_state_type()
rng_R_val = np.random.RandomState(utt.fetch_seed())
# no shape specified, default args
post_r, out = uniform(rng_R)
self._compile_and_check([rng_R], [out], [rng_R_val], RandomFunction)
post_r, out = uniform(rng_R, size=None, ndim=2)
self._compile_and_check([rng_R], [out], [rng_R_val], RandomFunction)
"""
#infer_shape don't work for multinomial.
#The parameter ndim_added is set to 1 and in this case, the infer_shape
#inplementation don't know how to infer the shape
post_r, out = multinomial(rng_R)
self._compile_and_check([rng_R], [out], [rng_R_val],
RandomFunction)
"""
# no shape specified, args have to be broadcasted
low = tensor.TensorType(dtype="float64",
broadcastable=(False, True, True))()
high = tensor.TensorType(dtype="float64",
broadcastable=(True, True, True, False))()
post_r, out = uniform(rng_R, size=None, ndim=2, low=low, high=high)
low_val = [[[3]], [[4]], [[-5]]]
high_val = [[[[5, 8]]]]
self._compile_and_check([rng_R, low, high], [out],
[rng_R_val, low_val, high_val], RandomFunction)
# multinomial, specified shape
"""
#infer_shape don't work for multinomial
n = iscalar()
pvals = dvector()
size_val = (7, 3)
n_val = 6
pvals_val = [0.2] * 5
post_r, out = multinomial(rng_R, size=size_val, n=n, pvals=pvals,
ndim=2)
self._compile_and_check([rng_R, n, pvals], [out],
[rng_R_val, n_val, pvals_val],
RandomFunction)
"""
# uniform vector low and high
low = dvector()
high = dvector()
post_r, out = uniform(rng_R, low=low, high=1)
low_val = [-5, 0.5, 0, 1]
self._compile_and_check([rng_R, low], [out], [rng_R_val, low_val],
RandomFunction)
low_val = [0.9]
self._compile_and_check([rng_R, low], [out], [rng_R_val, low_val],
RandomFunction)
post_r, out = uniform(rng_R, low=low, high=high)
low_val = [-4.0, -2]
high_val = [-1, 0]
self._compile_and_check([rng_R, low, high], [out],
[rng_R_val, low_val, high_val], RandomFunction)
low_val = [-4.0]
high_val = [-1]
self._compile_and_check([rng_R, low, high], [out],
[rng_R_val, low_val, high_val], RandomFunction)
# uniform broadcasting low and high
low = dvector()
high = dcol()
post_r, out = uniform(rng_R, low=low, high=high)
low_val = [-5, 0.5, 0, 1]
high_val = [[1.0]]
self._compile_and_check([rng_R, low, high], [out],
[rng_R_val, low_val, high_val], RandomFunction)
low_val = [0.9]
high_val = [[1.0], [1.1], [1.5]]
self._compile_and_check([rng_R, low, high], [out],
[rng_R_val, low_val, high_val], RandomFunction)
low_val = [-5, 0.5, 0, 1]
high_val = [[1.0], [1.1], [1.5]]
self._compile_and_check([rng_R, low, high], [out],
[rng_R_val, low_val, high_val], RandomFunction)
# uniform with vector slice
low = dvector()
high = dvector()
post_r, out = uniform(rng_R, low=low, high=high)
low_val = [0.1, 0.2, 0.3]
high_val = [1.1, 2.2, 3.3]
size_val = (3, )
self._compile_and_check(
[rng_R, low, high],
[out],
[rng_R_val, low_val[:-1], high_val[:-1]],
RandomFunction,
)
# uniform with explicit size and size implicit in parameters
# NOTE 1: Would it be desirable that size could also be supplied
# as a Theano variable?
post_r, out = uniform(rng_R, size=size_val, low=low, high=high)
self._compile_and_check([rng_R, low, high], [out],
[rng_R_val, low_val, high_val], RandomFunction)
# binomial with vector slice
n = ivector()
prob = dvector()
post_r, out = binomial(rng_R, n=n, p=prob)
n_val = [1, 2, 3]
prob_val = [0.1, 0.2, 0.3]
size_val = (3, )
self._compile_and_check(
[rng_R, n, prob],
[out],
[rng_R_val, n_val[:-1], prob_val[:-1]],
RandomFunction,
)
# binomial with explicit size and size implicit in parameters
# cf. NOTE 1
post_r, out = binomial(rng_R, n=n, p=prob, size=size_val)
self._compile_and_check([rng_R, n, prob], [out],
[rng_R_val, n_val, prob_val], RandomFunction)
# normal with vector slice
avg = dvector()
std = dvector()
post_r, out = normal(rng_R, avg=avg, std=std)
avg_val = [1, 2, 3]
std_val = [0.1, 0.2, 0.3]
size_val = (3, )
self._compile_and_check(
[rng_R, avg, std],
[out],
[rng_R_val, avg_val[:-1], std_val[:-1]],
RandomFunction,
)
# normal with explicit size and size implicit in parameters
# cf. NOTE 1
post_r, out = normal(rng_R, avg=avg, std=std, size=size_val)
self._compile_and_check([rng_R, avg, std], [out],
[rng_R_val, avg_val, std_val], RandomFunction)
# multinomial with tensor-3 probabilities
"""
