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_infer_shape_matrix(self):
if not self.expect_success:
raise utt.SkipTest('Requires numpy >= 1.13')
# Testing the infer_shape with a matrix.
x = theano.tensor.matrix()
ops = [
self.op_class(*args, **kwargs) for args, kwargs in self.ops_pars
]
for op in ops:
if not op.return_inverse:
continue
if op.return_index:
f = op(x)[2]
else:
f = op(x)[1]
self._compile_and_check([x], [f], [
np.asarray(np.array([[2, 1], [3, 2], [2, 1]]),
dtype=config.floatX)
], self.op_class)
def test_choice(self):
"""Test that RandomStreams.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.
random = RandomStreams(utt.fetch_seed())
fn = function([], random.choice((11, 8), 10, 1, 0))
fn_val0 = fn()
fn_val1 = fn()
rng_seed = numpy.random.RandomState(utt.fetch_seed()).randint(2**30)
rng = numpy.random.RandomState(int(rng_seed)) # int() is for 32bit
numpy_val0 = rng.choice(10, (11, 8), True, None)
numpy_val1 = rng.choice(10, (11, 8), True, None)
assert numpy.all(fn_val0 == numpy_val0)
assert numpy.all(fn_val1 == numpy_val1)
def test_basic_matrix(self):
if not self.expect_success:
raise utt.SkipTest('Requires numpy >= 1.13')
# Basic test for a matrix.
# Done by using the op and checking that it returns the right
# answer.
x = theano.tensor.matrix()
ops = [
self.op_class(*args, **kwargs) for args, kwargs in self.ops_pars
]
inp = np.asarray([[2, 1], [3, 2], [2, 1]], dtype=config.floatX)
list_outs_expected = [[np.unique(inp, **kwargs)] if len(args) == 0 else
np.unique(inp, *args, **kwargs)
for args, kwargs in self.ops_pars]
for op, outs_expected in zip(ops, list_outs_expected):
f = theano.function(inputs=[x], outputs=op(x, return_list=True))
outs = f(inp)
# Compare the result computed to the expected value.
for out, out_exp in zip(outs, outs_expected):
utt.assert_allclose(out, out_exp)