def test_permutation(self):
# Test that raw_random.permutation generates the same results as numpy.
rng_R = random_state_type()
post_r, out = permutation(rng_R, size=(9,), n=6)
f = compile.function(
[compile.In(rng_R,
value=np.random.RandomState(utt.fetch_seed()),
update=post_r, mutable=True)],
[out], accept_inplace=True)
numpy_rng = np.random.RandomState(utt.fetch_seed())
# Check over two calls to see if the random state is correctly updated.
# numpy_rng.permutation outputs one vector at a time,
# so we call it iteratively to generate all the samples.
val0 = f()
val1 = f()
numpy_val0 = np.asarray([numpy_rng.permutation(6)
for i in range(9)])
numpy_val1 = np.asarray([numpy_rng.permutation(6)
for i in range(9)])
self.assertTrue(np.all(val0 == numpy_val0))
self.assertTrue(np.all(val1 == numpy_val1))
# Test that we can generate a list: have size=None or ().
for ndim in [1, None]:
post_r, out = permutation(rng_R, n=10, size=None, ndim=ndim)
inp = compile.In(rng_R,
value=np.random.RandomState(utt.fetch_seed()),
update=post_r, mutable=True)
f = theano.function([inp], out)
o = f()
assert o.shape == (10,)
assert (np.sort(o) == np.arange(10)).all()
# Wrong number of dimensions asked
self.assertRaises(TypeError, permutation, rng_R, size=None, ndim=2)
def test_permutation_helper(self):
"""Test that raw_random.permutation_helper generates the same
results as numpy,
and that the 'ndim_added' keyword behaves correctly."""
# permutation_helper needs "ndim_added=1", because its output
# is one dimension more than its "shape" argument (and there's
# no way to determine that automatically).
# Check the working case, over two calls to see if the random
# state is correctly updated.
rf = RandomFunction(permutation_helper, tensor.imatrix, 8,
ndim_added=1)
rng_R = random_state_type()
post_r, out = rf(rng_R, (7,), 8)
f = compile.function(
[compile.In(rng_R,
value=numpy.random.RandomState(utt.fetch_seed()),
update=post_r, mutable=True)],
[out], accept_inplace=True)
numpy_rng = numpy.random.RandomState(utt.fetch_seed())
val0 = f()
val1 = f()
# numpy_rng.permutation outputs one vector at a time,
# so we call it iteratively to generate all the samples.
numpy_val0 = numpy.asarray([numpy_rng.permutation(8)
for i in range(7)])
numpy_val1 = numpy.asarray([numpy_rng.permutation(8)
for i in range(7)])
print val0
print numpy_val0
print val1
print numpy_val1
self.assertTrue(numpy.all(val0 == numpy_val0))
self.assertTrue(numpy.all(val1 == numpy_val1))
# This call lacks "ndim_added=1", so ndim_added defaults to 0.
# A ValueError should be raised.
rf0 = RandomFunction(permutation_helper, tensor.imatrix, 8)
post_r0, out0 = rf0(rng_R, (7,), 8)
f0 = compile.function(
[compile.In(rng_R,
value=numpy.random.RandomState(utt.fetch_seed()),
update=post_r0, mutable=True)],
[out0], accept_inplace=True)
self.assertRaises(ValueError, f0)
# Here, ndim_added is 2 instead of 1. A ValueError should be raised.
rf2 = RandomFunction(permutation_helper, tensor.imatrix, 8,
ndim_added=2)
post_r2, out2 = rf2(rng_R, (7,), 8)
f2 = compile.function(
[compile.In(rng_R,
value=numpy.random.RandomState(utt.fetch_seed()),
update=post_r2, mutable=True)],
[out2], accept_inplace=True)
self.assertRaises(ValueError, f2)
def test_no_inplace(self):
"""Test that when not running inplace, the RandomState is
not updated"""
rf = RandomFunction('uniform', tensor.dvector)
rng_R = random_state_type()
post_r, out = rf(rng_R, (3, ), 0., 1.)
f = compile.function([rng_R], [post_r, out])
rng = numpy.random.RandomState(utt.fetch_seed())
rng0, val0 = f(rng)
rng_ = numpy.random.RandomState(utt.fetch_seed())
# rng should still be in a fresh state
self.assertTrue(rng_R.type.values_eq(rng, rng_))
# rng0 should be in an updated state
self.assertFalse(rng_R.type.values_eq(rng, rng0))
f2 = compile.function(
[compile.In(rng_R, value=rng, update=post_r, mutable=False)],
[post_r, out])
rng2, val2 = f2()
# rng should be in a fresh state
self.assertTrue(rng_R.type.values_eq(rng, rng_))
# rng2 should be in an updated state
self.assertFalse(rng_R.type.values_eq(rng, rng2))
# The updated state should be the same for both functions
self.assertTrue(rng_R.type.values_eq(rng2, rng0))
rng3, val3 = f2()
# rng2 should not have changed
self.assertTrue(rng_R.type.values_eq(rng2, rng0))
# rng3 should be an updated again version of rng2
self.assertFalse(rng_R.type.values_eq(rng3, rng2))
self.assertFalse(rng_R.type.values_eq(rng3, rng))
def test_poisson(self):
# Test that raw_random.poisson 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, out = poisson(rng_R, lam=5, size=(11, 8))
f = compile.function(
[
compile.In(
rng_R,
value=np.random.RandomState(utt.fetch_seed()),
update=post_r,
mutable=True,
)
],
[out],
accept_inplace=True,
)
numpy_rng = np.random.RandomState(utt.fetch_seed())
val0 = f()
val1 = f()
numpy_val0 = numpy_rng.poisson(5, size=(11, 8))
numpy_val1 = numpy_rng.poisson(5, size=(11, 8))
assert np.allclose(val0, numpy_val0)
assert np.allclose(val1, numpy_val1)
def test_uniform(self):
# Test that raw_random.uniform 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
post_r, out = uniform(rng_R, (4, ), -2.0, 2.0)
f = compile.function(
[
compile.In(
rng_R,
value=np.random.RandomState(utt.fetch_seed()),
update=post_r,
mutable=True,
)
],
[out],
accept_inplace=True,
)
numpy_rng = np.random.RandomState(utt.fetch_seed())
val0 = f()
val1 = f()
numpy_val0 = numpy_rng.uniform(-2.0, 2.0, size=(4, ))
numpy_val1 = numpy_rng.uniform(-2.0, 2.0, size=(4, ))
assert np.allclose(val0, numpy_val0)
assert np.allclose(val1, numpy_val1)
def test_normal(self):
"""Test that raw_random.normal 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
post_r, out = normal(rng_R, (2, 3), 4.0, 2.0)
f = compile.function([
compile.In(rng_R,
value=numpy.random.RandomState(utt.fetch_seed()),
update=post_r,
mutable=True)
], [out],
accept_inplace=True)
numpy_rng = numpy.random.RandomState(utt.fetch_seed())
val0 = f()
val1 = f()
numpy_val0 = numpy_rng.normal(4.0, 2.0, size=(2, 3))
numpy_val1 = numpy_rng.normal(4.0, 2.0, size=(2, 3))
print(val0)
print(numpy_val0)
print(val1)
print(numpy_val1)
self.assertTrue(numpy.allclose(val0, numpy_val0))
self.assertTrue(numpy.allclose(val1, numpy_val1))
def test_choice(self):
"""Test that raw_random.choice generates the same
results as numpy."""
# numpy.random.choice is only available for numpy versions >= 1.7
major, minor, _ = numpy.version.short_version.split('.')
if (int(major), int(minor)) < (1, 7):
raise utt.SkipTest('choice requires at NumPy version >= 1.7 '
'(%s)' % numpy.__version__)
# 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, out = choice(rng_R, (11, 8), 10, 1, 0)
f = compile.function([
compile.In(rng_R,
value=numpy.random.RandomState(utt.fetch_seed()),
update=post_r,
mutable=True)
], [out],
accept_inplace=True)
numpy_rng = numpy.random.RandomState(utt.fetch_seed())
val0 = f()
val1 = f()
numpy_val0 = numpy_rng.choice(10, (11, 8), True, None)
numpy_val1 = numpy_rng.choice(10, (11, 8), True, None)
print(val0)
print(numpy_val0)
print(val1)
print(numpy_val1)
self.assertTrue(numpy.allclose(val0, numpy_val0))
self.assertTrue(numpy.allclose(val1, numpy_val1))
def test_inplace_optimization(self):
"""Test that FAST_RUN includes the random_make_inplace optimization"""
#inplace = False
rf2 = RandomFunction(numpy.random.RandomState.uniform, tensor.dvector)
rng_R = random_state_type()
# If calling RandomFunction directly, all args have to be specified,
# because shape will have to be moved to the end
post_r2, out2 = rf2(rng_R, (4,), 0., 1.)
f = compile.function(
[compile.In(rng_R,
value=numpy.random.RandomState(utt.fetch_seed()),
update=post_r2,
mutable=True)],
out2,
mode='FAST_RUN') # DEBUG_MODE can't pass the id-based
# test below
# test that the RandomState object stays the same from function call to
# function call, but that the values returned change from call to call.
id0 = id(f[rng_R])
val0 = f()
assert id0 == id(f[rng_R])
val1 = f()
assert id0 == id(f[rng_R])
assert not numpy.allclose(val0, val1)
def test_multinomial(self):
"""Test that raw_random.multinomial generates the same
results as numpy."""
# Check over two calls to see if the random state is correctly updated.
rng_R = random_state_type()
post_r, out = multinomial(rng_R, (7, 3), 6, [0.2] * 5)
f = compile.function(
[compile.In(rng_R,
value=numpy.random.RandomState(utt.fetch_seed()),
update=post_r, mutable=True)],
[out], accept_inplace=True)
numpy_rng = numpy.random.RandomState(utt.fetch_seed())
val0, = f()
val1, = f()
numpy_val0 = numpy_rng.multinomial(6, [0.2] * 5, (7, 3))
numpy_val1 = numpy_rng.multinomial(6, [0.2] * 5, (7, 3))
print val0
print numpy_val0
print val1
print numpy_val1
self.assertTrue(numpy.all(val0 == numpy_val0))
self.assertTrue(numpy.all(val1 == numpy_val1))
self.assertTrue(val0.shape == (7, 3, 5))
self.assertTrue(val1.shape == (7, 3, 5))
def test_random_integers(self):
"""Test that raw_random.random_integers 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, out = random_integers(rng_R, (11, 8), -3, 16)
f = compile.function(
[compile.In(rng_R,
value=numpy.random.RandomState(utt.fetch_seed()),
update=post_r, mutable=True)],
[out], accept_inplace=True)
numpy_rng = numpy.random.RandomState(utt.fetch_seed())
val0 = f()
val1 = f()
numpy_val0 = numpy_rng.random_integers(-3, 16, size=(11, 8))
numpy_val1 = numpy_rng.random_integers(-3, 16, size=(11, 8))
print val0
print numpy_val0
print val1
print numpy_val1
self.assertTrue(numpy.allclose(val0, numpy_val0))
self.assertTrue(numpy.allclose(val1, numpy_val1))
def test_permutation(self):
"""Test that raw_random.permutation generates the same
results as numpy."""
rng_R = random_state_type()
post_r, out = permutation(rng_R, size=(9,), n=6)
print 'OUT NDIM', out.ndim
f = compile.function(
[compile.In(rng_R,
value=numpy.random.RandomState(utt.fetch_seed()),
update=post_r, mutable=True)],
[out], accept_inplace=True)
numpy_rng = numpy.random.RandomState(utt.fetch_seed())
# Check over two calls to see if the random state is correctly updated.
# numpy_rng.permutation outputs one vector at a time,
# so we call it iteratively to generate all the samples.
val0 = f()
val1 = f()
numpy_val0 = numpy.asarray([numpy_rng.permutation(6)
for i in range(9)])
numpy_val1 = numpy.asarray([numpy_rng.permutation(6)
for i in range(9)])
print val0
print numpy_val0
print val1
print numpy_val1
self.assertTrue(numpy.all(val0 == numpy_val0))
self.assertTrue(numpy.all(val1 == numpy_val1))
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=numpy.random.RandomState(utt.fetch_seed()),
update=post_r, mutable=True)],
[bin], accept_inplace=True)
numpy_rng = numpy.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))
print val0
print numpy_val0
print val1
print numpy_val1
self.assertTrue(numpy.all(val0 == numpy_val0))
self.assertTrue(numpy.all(val1 == numpy_val1))
def test_random_function_ndim(self):
"""Test that random_function helper function accepts argument ndim"""
rng_R = random_state_type()
# ndim is an optional argument indicating the length of the 'shape'
# ndim not specified, OK
post_out4, out4 = uniform(rng_R, (4,))
# ndim specified, consistent with shape, OK
post_out1_4, out1_4 = uniform(rng_R, (4, ), ndim=1)
post_out2_4_4, out2_4_4 = uniform(rng_R, (4, 4), ndim=2)
# ndim specified, but not compatible with shape
self.assertRaises(ValueError, uniform, rng_R, (4,), ndim=2)
f_ok = compile.function(
[compile.In(rng_R,
value=numpy.random.RandomState(utt.fetch_seed()),
update=post_out2_4_4,
mutable=True)],
[out4, out1_4, out2_4_4],
accept_inplace=True)
# The correct cases should execute properly
o4, o1_4, o2_4_4 = f_ok()
# Check the sanity of the answers
self.assertTrue(numpy.allclose(o4, o1_4))
self.assertTrue(numpy.allclose(o4, o2_4_4[0]))
def test_random_function_noshape_args(self):
# Test if random_function helper works with args but without shape
rng_R = random_state_type()
# No shape, default args -> OK
post_out, out = uniform(rng_R, size=None, ndim=2)
f = compile.function(
[
compile.In(
rng_R,
value=np.random.RandomState(utt.fetch_seed()),
update=post_out,
mutable=True,
)
],
[out],
accept_inplace=True,
)
(o, ) = f()
# No shape, args that have to be broadcasted -> OK
low = tensor.TensorType(dtype="float64",
broadcastable=(False, True, True))()
high = tensor.TensorType(dtype="float64",
broadcastable=(True, True, True, False))()
post_out2, out2 = uniform(rng_R, size=None, ndim=2, low=low, high=high)
assert out2.ndim == 4
assert out2.broadcastable == (True, False, True, False)
g = compile.function(
[
low,
high,
compile.In(
rng_R,
value=np.random.RandomState(utt.fetch_seed()),
update=post_out2,
mutable=True,
),
],
[out2],
accept_inplace=True,
)
low_v = [[[3]], [[4]], [[-5]]]
high_v = [[[[5, 8]]]]
(o2, ) = g(low_v, high_v)
assert o2.shape == (1, 3, 1, 2)
def test_args(self):
# Test that arguments to RandomFunction are honored
rf2 = RandomFunction(np.random.RandomState.uniform, tensor.dvector)
rf4 = RandomFunction(np.random.RandomState.uniform,
tensor.dvector,
inplace=True)
rng_R = random_state_type()
# use make_node to override some of the self.args
post_r2, out2 = rf2(rng_R, (4, ), -2, 2) # NOT INPLACE
post_r4, out4 = rf4(rng_R, (4, ), -4, 4) # INPLACE
post_r2_4, out2_4 = rf2(rng_R, (4, ), -4.0, 2) # NOT INPLACE
post_r2_4_4, out2_4_4 = rf2(rng_R, (4, ), -4.0, 4.0) # NOT INPLACE
# configure out4 to be computed inplace
# The update expression means that the random state rng_R will
# be maintained by post_r4
f = compile.function(
[
compile.In(
rng_R,
value=np.random.RandomState(utt.fetch_seed()),
update=post_r4,
mutable=True,
)
],
[out2, out4, out2_4, out2_4_4],
accept_inplace=True,
)
f2, f4, f2_4, f2_4_4 = f()
f2b, f4b, f2_4b, f2_4_4b = f()
# print f2
# print f4
# print f2_4
# print f2_4_4
# print f2b
# print f4b
# print f2_4b
# print f2_4_4b
# setting bounds is same as multiplying by 2
assert np.allclose(f2 * 2, f4), (f2, f4)
# retrieving from non-inplace generator
# is same as inplace one for first call
assert np.allclose(f2_4_4, f4), (f2_4_4, f4)
# f4 changes from call to call, that the update has worked
assert not np.allclose(f4, f4b), (f4, f4b)
def test_choice(self):
# Test that raw_random.choice 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, out = choice(rng_R, (11, 8), 10, 1, 0)
f = compile.function(
[compile.In(rng_R,
value=np.random.RandomState(utt.fetch_seed()),
update=post_r, mutable=True)],
[out], accept_inplace=True)
numpy_rng = np.random.RandomState(utt.fetch_seed())
val0 = f()
val1 = f()
numpy_val0 = numpy_rng.choice(10, (11, 8), True, None)
numpy_val1 = numpy_rng.choice(10, (11, 8), True, None)
self.assertTrue(np.allclose(val0, numpy_val0))
self.assertTrue(np.allclose(val1, numpy_val1))
def test_random_function_ndim_added(self):
"""Test that random_function helper function accepts ndim_added as
keyword argument"""
# If using numpy's uniform distribution, ndim_added should be 0,
# because the shape provided as argument is the output shape.
# Specifying a different ndim_added will change the Op's output ndim,
# so numpy.uniform will produce a result of incorrect shape,
# and a ValueError should be raised.
def ndim_added_deco(ndim_added):
def randomfunction(random_state, size=(), low=0.0, high=0.0,
ndim=None):
ndim, size, bcast = raw_random._infer_ndim_bcast(ndim, size)
if ndim_added < 0:
bcast = bcast[:ndim_added]
else:
bcast = bcast + ((False,) * ndim_added)
assert len(bcast) == ndim + ndim_added
op = RandomFunction('uniform',
tensor.TensorType(dtype='float64',
broadcastable=bcast),
ndim_added=ndim_added)
return op(random_state, size, low, high)
return randomfunction
uni_1 = ndim_added_deco(1)
uni_0 = ndim_added_deco(0)
uni_m1 = ndim_added_deco(-1)
rng_R = random_state_type()
p_uni11, uni11 = uni_1(rng_R, size=(4,))
p_uni12, uni12 = uni_1(rng_R, size=(3, 4))
p_uni01, uni01 = uni_0(rng_R, size=(4,))
p_uni02, uni02 = uni_0(rng_R, size=(3, 4))
p_unim11, unim11 = uni_m1(rng_R, size=(4,))
p_unim12, unim12 = uni_m1(rng_R, size=(3, 4))
self.assertEqual(uni11.ndim, 2)
self.assertEqual(uni12.ndim, 3)
self.assertEqual(uni01.ndim, 1)
self.assertEqual(uni02.ndim, 2)
self.assertEqual(unim11.ndim, 0)
self.assertEqual(unim12.ndim, 1)
f11 = compile.function(
[compile.In(rng_R,
value=numpy.random.RandomState(utt.fetch_seed()),
update=p_uni11, mutable=True)],
[uni11], accept_inplace=True)
f12 = compile.function(
[compile.In(rng_R,
value=numpy.random.RandomState(utt.fetch_seed()),
update=p_uni12, mutable=True)],
[uni12], accept_inplace=True)
fm11 = compile.function(
[compile.In(rng_R,
value=numpy.random.RandomState(utt.fetch_seed()),
update=p_unim11, mutable=True)],
[unim11], accept_inplace=True)
fm12 = compile.function(
[compile.In(rng_R,
value=numpy.random.RandomState(utt.fetch_seed()),
update=p_unim12, mutable=True)],
[unim12], accept_inplace=True)
f0 = compile.function(
[compile.In(rng_R,
value=numpy.random.RandomState(utt.fetch_seed()),
update=p_uni02, mutable=True)],
[uni01, uni02], accept_inplace=True)
self.assertRaises(ValueError, f11)
self.assertRaises(ValueError, f12)
self.assertRaises(ValueError, fm11)
self.assertRaises(ValueError, fm12)
u01, u02 = f0()
print u01
print u02
self.assertTrue(numpy.allclose(u01, u02[0]))
