def check(shape, index_ndim, mode, order):
multi_index = np.unravel_index(np.arange(np.product(shape)),
shape,
order=order)
# create some invalid indices to test the mode
if mode in ("wrap", "clip"):
multi_index = (multi_index[0] - 1, ) + multi_index[1:]
# test with scalars and higher-dimensional indices
if index_ndim == 0:
multi_index = tuple(i[-1] for i in multi_index)
elif index_ndim == 2:
multi_index = tuple(i[:, np.newaxis] for i in multi_index)
multi_index_symb = [theano.shared(i) for i in multi_index]
# reference result
ref = np.ravel_multi_index(multi_index, shape, mode, order)
def fn(mi, s):
return function([], ravel_multi_index(mi, s, mode, order))
# shape given as a tuple
f_array_tuple = fn(multi_index, shape)
f_symb_tuple = fn(multi_index_symb, shape)
np.testing.assert_equal(ref, f_array_tuple())
np.testing.assert_equal(ref, f_symb_tuple())
# shape given as an array
shape_array = np.array(shape)
f_array_array = fn(multi_index, shape_array)
np.testing.assert_equal(ref, f_array_array())
# shape given as a theano variable
shape_symb = theano.shared(shape_array)
f_array_symb = fn(multi_index, shape_symb)
np.testing.assert_equal(ref, f_array_symb())
# shape testing
self._compile_and_check(
[],
[ravel_multi_index(multi_index, shape_symb, mode, order)],
[],
RavelMultiIndex,
)
def check(shape, index_ndim, mode, order):
multi_index = np.unravel_index(np.arange(np.product(shape)), shape, order=order)
# create some invalid indices to test the mode
if mode in ('wrap', 'clip'):
multi_index = (multi_index[0] - 1,) + multi_index[1:]
# test with scalars and higher-dimensional indices
if index_ndim == 0:
multi_index = tuple(i[-1] for i in multi_index)
elif index_ndim == 2:
multi_index = tuple(i[:, np.newaxis] for i in multi_index)
multi_index_symb = [theano.shared(i) for i in multi_index]
# reference result
ref = np.ravel_multi_index(multi_index, shape, mode, order)
def fn(mi, s):
return function([], ravel_multi_index(mi, s, mode, order))
# shape given as a tuple
f_array_tuple = fn(multi_index, shape)
f_symb_tuple = fn(multi_index_symb, shape)
np.testing.assert_equal(ref, f_array_tuple())
np.testing.assert_equal(ref, f_symb_tuple())
# shape given as an array
shape_array = np.array(shape)
f_array_array = fn(multi_index, shape_array)
np.testing.assert_equal(ref, f_array_array())
# shape given as a theano variable
shape_symb = theano.shared(shape_array)
f_array_symb = fn(multi_index, shape_symb)
np.testing.assert_equal(ref, f_array_symb())
# shape testing
self._compile_and_check([],
[ravel_multi_index(multi_index, shape_symb, mode, order)],
[], RavelMultiIndex)
def fn(mi, s):
return function([], ravel_multi_index(mi, s, mode, order))
def test_ravel_multi_index(self):
def check(shape, index_ndim, mode, order):
multi_index = np.unravel_index(np.arange(np.product(shape)),
shape,
order=order)
# create some invalid indices to test the mode
if mode in ("wrap", "clip"):
multi_index = (multi_index[0] - 1, ) + multi_index[1:]
# test with scalars and higher-dimensional indices
if index_ndim == 0:
multi_index = tuple(i[-1] for i in multi_index)
elif index_ndim == 2:
multi_index = tuple(i[:, np.newaxis] for i in multi_index)
multi_index_symb = [theano.shared(i) for i in multi_index]
# reference result
ref = np.ravel_multi_index(multi_index, shape, mode, order)
def fn(mi, s):
return function([], ravel_multi_index(mi, s, mode, order))
# shape given as a tuple
f_array_tuple = fn(multi_index, shape)
f_symb_tuple = fn(multi_index_symb, shape)
np.testing.assert_equal(ref, f_array_tuple())
np.testing.assert_equal(ref, f_symb_tuple())
# shape given as an array
shape_array = np.array(shape)
f_array_array = fn(multi_index, shape_array)
np.testing.assert_equal(ref, f_array_array())
# shape given as a theano variable
shape_symb = theano.shared(shape_array)
f_array_symb = fn(multi_index, shape_symb)
np.testing.assert_equal(ref, f_array_symb())
# shape testing
self._compile_and_check(
[],
[ravel_multi_index(multi_index, shape_symb, mode, order)],
[],
RavelMultiIndex,
)
for mode in ("raise", "wrap", "clip"):
for order in ("C", "F"):
for index_ndim in (0, 1, 2):
check((3, ), index_ndim, mode, order)
check((3, 4), index_ndim, mode, order)
check((3, 4, 5), index_ndim, mode, order)
# must provide integers
with pytest.raises(TypeError):
ravel_multi_index(
(theano.tensor.fvector(), theano.tensor.ivector()), (3, 4))
with pytest.raises(TypeError):
ravel_multi_index(((3, 4), theano.tensor.ivector()), (3.4, 3.2))
# dims must be a 1D sequence
with pytest.raises(TypeError):
ravel_multi_index(((3, 4), ), ((3, 4), ))
def fn(mi, s):
return function([], ravel_multi_index(mi, s, mode, order))