def test_diag():
"""
Test that linalg.diag has the same behavior as numpy.diag.
numpy.diag has two behaviors:
(1) when given a vector, it returns a matrix with that vector as the
diagonal.
(2) when given a matrix, returns a vector which is the diagonal of the
matrix.
(1) and (2) are tested by test_alloc_diag and test_extract_diag
respectively. This test makes sure that linalg.diag instantiates
the right op based on the dimension of the input.
"""
# test that it builds a matrix with given diagonal when using vector inputs
x = theano.tensor.vector()
y = diag(x)
assert y.owner.op.__class__ == AllocDiag
# test that it extracts the diagonal when using matrix input
x = theano.tensor.matrix()
y = extract_diag(x)
assert y.owner.op.__class__ == ExtractDiag
# other types should raise error
x = theano.tensor.tensor3()
ok = False
try:
y = extract_diag(x)
except TypeError:
ok = True
assert ok
def test_extract_diag():
rng = numpy.random.RandomState(utt.fetch_seed())
x = theano.tensor.matrix()
g = extract_diag(x)
f = theano.function([x], g)
for shp in [(2, 3), (3, 2), (3, 3), (1, 1), (0, 0)]:
m = rng.rand(*shp).astype(config.floatX)
v = numpy.diag(m)
r = f(m)
# The right diagonal is extracted
assert (r == v).all()
# Test we accept only matrix
xx = theano.tensor.vector()
ok = False
try:
extract_diag(xx)
except TypeError:
ok = True
assert ok
# Test infer_shape
f = theano.function([x], g.shape)
topo = f.maker.fgraph.toposort()
if config.mode != 'FAST_COMPILE':
assert sum([node.op.__class__ == ExtractDiag for node in topo]) == 0
for shp in [(2, 3), (3, 2), (3, 3)]:
m = rng.rand(*shp).astype(config.floatX)
assert f(m) == min(shp)
def test_extract_diag():
rng = numpy.random.RandomState(utt.fetch_seed())
x = theano.tensor.matrix()
g = extract_diag(x)
f = theano.function([x], g)
for shp in [(2, 3), (3, 2), (3, 3), (1, 1), (0, 0)]:
m = rng.rand(*shp).astype(config.floatX)
v = numpy.diag(m)
r = f(m)
# The right diagonal is extracted
assert (r == v).all()
# Test we accept only matrix
xx = theano.tensor.vector()
ok = False
try:
extract_diag(xx)
except TypeError:
ok = True
assert ok
# Test infer_shape
f = theano.function([x], g.shape)
topo = f.maker.fgraph.toposort()
if config.mode != 'FAST_COMPILE':
assert sum([node.op.__class__ == ExtractDiag for node in topo]) == 0
for shp in [(2, 3), (3, 2), (3, 3)]:
m = rng.rand(*shp).astype(config.floatX)
assert f(m) == min(shp)
def test_diag():
"""
Test that linalg.diag has the same behavior as numpy.diag.
numpy.diag has two behaviors:
(1) when given a vector, it returns a matrix with that vector as the
diagonal.
(2) when given a matrix, returns a vector which is the diagonal of the
matrix.
(1) and (2) are tested by test_alloc_diag and test_extract_diag
respectively. This test makes sure that linalg.diag instantiates
the right op based on the dimension of the input.
"""
# test that it builds a matrix with given diagonal when using vector inputs
x = theano.tensor.vector()
y = diag(x)
assert y.owner.op.__class__ == AllocDiag
# test that it extracts the diagonal when using matrix input
x = theano.tensor.matrix()
y = extract_diag(x)
assert y.owner.op.__class__ == ExtractDiag
# other types should raise error
x = theano.tensor.tensor3()
ok = False
try:
y = extract_diag(x)
except TypeError:
ok = True
assert ok
def test_extract_diag_empty(self):
c = self.shared(numpy.array([[], []], self.floatX))
f = theano.function([], extract_diag(c), mode=self.mode)
assert [isinstance(node.inputs[0].type, self.type)
for node in f.maker.fgraph.toposort()
if isinstance(node.op, ExtractDiag)] == [True]
def test_extract_diag_empty(self):
c = self.shared(numpy.array([[], []], self.floatX))
f = theano.function([], extract_diag(c), mode=self.mode)
assert [isinstance(node.inputs[0].type, self.type)
for node in f.maker.fgraph.toposort()
if isinstance(node.op, ExtractDiag)] == [True]
def test_diag(self):
# test that it builds a matrix with given diagonal when using
# vector inputs
x = theano.tensor.vector()
y = diag(x)
assert y.owner.op.__class__ == AllocDiag
# test that it extracts the diagonal when using matrix input
x = theano.tensor.matrix()
y = extract_diag(x)
assert y.owner.op.__class__ == ExtractDiag
# other types should raise error
x = theano.tensor.tensor3()
ok = False
try:
y = extract_diag(x)
except TypeError:
ok = True
assert ok
def test_diag(self):
# test that it builds a matrix with given diagonal when using
# vector inputs
x = theano.tensor.vector()
y = diag(x)
assert y.owner.op.__class__ == AllocDiag
# test that it extracts the diagonal when using matrix input
x = theano.tensor.matrix()
y = extract_diag(x)
assert y.owner.op.__class__ == ExtractDiag
# other types should raise error
x = theano.tensor.tensor3()
ok = False
try:
y = extract_diag(x)
except TypeError:
ok = True
assert ok
def test_extract_diag(self):
rng = numpy.random.RandomState(utt.fetch_seed())
m = rng.rand(2, 3).astype(self.floatX)
x = self.shared(m)
g = extract_diag(x)
f = theano.function([], g)
assert [
isinstance(node.inputs[0].type, self.type)
for node in f.maker.fgraph.toposort()
if isinstance(node.op, ExtractDiag)
] == [True]
for shp in [(2, 3), (3, 2), (3, 3), (1, 1), (0, 0)]:
m = rng.rand(*shp).astype(self.floatX)
x.set_value(m)
v = numpy.diag(m)
r = f()
# The right diagonal is extracted
assert (r == v).all()
# Test we accept only matrix
xx = theano.tensor.vector()
ok = False
try:
extract_diag(xx)
except TypeError:
ok = True
assert ok
# Test infer_shape
f = theano.function([], g.shape)
topo = f.maker.fgraph.toposort()
if config.mode != 'FAST_COMPILE':
assert sum([node.op.__class__ == ExtractDiag
for node in topo]) == 0
for shp in [(2, 3), (3, 2), (3, 3)]:
m = rng.rand(*shp).astype(self.floatX)
x.set_value(m)
assert f() == min(shp)
def test_extract_diag(self):
rng = numpy.random.RandomState(utt.fetch_seed())
m = rng.rand(2, 3).astype(self.floatX)
x = self.shared(m)
g = extract_diag(x)
f = theano.function([], g)
assert [isinstance(node.inputs[0].type, self.type)
for node in f.maker.fgraph.toposort()
if isinstance(node.op, ExtractDiag)] == [True]
for shp in [(2, 3), (3, 2), (3, 3), (1, 1), (0, 0)]:
m = rng.rand(*shp).astype(self.floatX)
x.set_value(m)
v = numpy.diag(m)
r = f()
# The right diagonal is extracted
assert (r == v).all()
# Test we accept only matrix
xx = theano.tensor.vector()
ok = False
try:
extract_diag(xx)
except TypeError:
ok = True
assert ok
# Test infer_shape
f = theano.function([], g.shape)
topo = f.maker.fgraph.toposort()
if config.mode != 'FAST_COMPILE':
assert sum([node.op.__class__ == ExtractDiag
for node in topo]) == 0
for shp in [(2, 3), (3, 2), (3, 3)]:
m = rng.rand(*shp).astype(self.floatX)
x.set_value(m)
assert f() == min(shp)
def test_extract_diag_empty():
c = theano.tensor.constant(numpy.array([[], []], 'int32'))
extract_diag(c).eval()