def test_sfa2_gradient2(self):
def _alt_sfa2_grad(self, x):
"""Reference grad method based on quadratic forms."""
# note that the H and f arrays are cached in the node and remain even
# after the extension has been deactivated
if not hasattr(self, "__gradient_Hs"):
quad_forms = [self.get_quadratic_form(i)
for i in range(self.output_dim)]
self.__gradient_Hs = numx.vstack((quad_form.H[numx.newaxis]
for quad_form in quad_forms))
self.__gradient_fs = numx.vstack((quad_form.f[numx.newaxis]
for quad_form in quad_forms))
grad = (numx.dot(x, self.__gradient_Hs) +
numx.repeat(self.__gradient_fs[numx.newaxis,:,:],
len(x), axis=0))
return grad
sfa2_node = bimdp.nodes.SFA2BiNode(output_dim=3)
x = numx_rand.random((300, 6))
sfa2_node.train(x)
sfa2_node.stop_training()
x = numx_rand.random((2, 6))
mdp.activate_extension("gradient")
try:
result1 = sfa2_node.execute(x, {"method": "gradient"})
grad1 = result1[1]["grad"]
grad2 = _alt_sfa2_grad(sfa2_node, x)
assert numx.amax(abs(grad1 - grad2)) < 1E-9
finally:
mdp.deactivate_extension("gradient")
def test_sfa2_gradient2(self):
def _alt_sfa2_grad(self, x):
"""Reference grad method based on quadratic forms."""
# note that the H and f arrays are cached in the node and remain even
# after the extension has been deactivated
if not hasattr(self, "__gradient_Hs"):
quad_forms = [
self.get_quadratic_form(i) for i in range(self.output_dim)
]
self.__gradient_Hs = numx.vstack(
(quad_form.H[numx.newaxis] for quad_form in quad_forms))
self.__gradient_fs = numx.vstack(
(quad_form.f[numx.newaxis] for quad_form in quad_forms))
grad = (numx.dot(x, self.__gradient_Hs) + numx.repeat(
self.__gradient_fs[numx.newaxis, :, :], len(x), axis=0))
return grad
sfa2_node = bimdp.nodes.SFA2BiNode(output_dim=3)
x = numx_rand.random((300, 6))
sfa2_node.train(x)
sfa2_node.stop_training()
x = numx_rand.random((2, 6))
mdp.activate_extension("gradient")
try:
result1 = sfa2_node.execute(x, {"method": "gradient"})
grad1 = result1[1]["grad"]
grad2 = _alt_sfa2_grad(sfa2_node, x)
assert numx.amax(abs(grad1 - grad2)) < 1E-9
finally:
mdp.deactivate_extension("gradient")
def test_sfa2_gradient(self):
sfa2_node1 = bimdp.nodes.SFA2BiNode(output_dim=5)
sfa2_node2 = bimdp.nodes.SFA2BiNode(output_dim=3)
flow = sfa2_node1 + sfa2_node2
x = numx_rand.random((300, 6))
flow.train(x)
x = numx_rand.random((2, 6))
mdp.activate_extension("gradient")
try:
flow.execute(x, {"method": "gradient"})
finally:
mdp.deactivate_extension("gradient")
def test_mult_diag():
dim = 20
d = numx_rand.random(size=(dim,))
dd = numx.diag(d)
mtx = numx_rand.random(size=(dim, dim))
res1 = utils.mult(dd, mtx)
res2 = utils.mult_diag(d, mtx, left=True)
assert_array_almost_equal(res1, res2, 10)
res1 = utils.mult(mtx, dd)
res2 = utils.mult_diag(d, mtx, left=False)
assert_array_almost_equal(res1, res2, 10)
def test_sfa2_gradient(self):
sfa2_node1 = bimdp.nodes.SFA2BiNode(output_dim=5)
sfa2_node2 = bimdp.nodes.SFA2BiNode(output_dim=3)
flow = sfa2_node1 + sfa2_node2
x = numx_rand.random((300, 6))
flow.train(x)
x = numx_rand.random((2, 6))
mdp.activate_extension("gradient")
try:
flow.execute(x, {"method": "gradient"})
finally:
mdp.deactivate_extension("gradient")
def test_sfa_gradient(self):
"""Test gradient for combination of SFA nodes."""
sfa_node1 = bimdp.nodes.SFABiNode(output_dim=8)
sfa_node2 = bimdp.nodes.SFABiNode(output_dim=7)
sfa_node3 = bimdp.nodes.SFABiNode(output_dim=5)
flow = sfa_node1 + sfa_node2 + sfa_node3
x = numx_rand.random((300, 10))
flow.train(x)
x = numx_rand.random((2, 10))
mdp.activate_extension("gradient")
try:
flow.execute(x, {"method": "gradient"})
finally:
mdp.deactivate_extension("gradient")
def test_sfa_gradient(self):
"""Test gradient for combination of SFA nodes."""
sfa_node1 = bimdp.nodes.SFABiNode(output_dim=8)
sfa_node2 = bimdp.nodes.SFABiNode(output_dim=7)
sfa_node3 = bimdp.nodes.SFABiNode(output_dim=5)
flow = sfa_node1 + sfa_node2 + sfa_node3
x = numx_rand.random((300, 10))
flow.train(x)
x = numx_rand.random((2, 10))
mdp.activate_extension("gradient")
try:
flow.execute(x, {"method": "gradient"})
finally:
mdp.deactivate_extension("gradient")
def test_casting():
x = numx_rand.random((5,3)).astype('d')
y = 3*x
assert_type_equal(y.dtype, x.dtype)
x = numx_rand.random((5,3)).astype('f')
y = 3.*x
assert_type_equal(y.dtype, x.dtype)
x = (10*numx_rand.random((5,3))).astype('i')
y = 3.*x
assert_type_equal(y.dtype, 'd')
y = 3L*x
assert_type_equal(y.dtype, 'i')
x = numx_rand.random((5,3)).astype('f')
y = 3L*x
assert_type_equal(y.dtype, 'f')
def test_clonebilayer_gradient(self):
"""Test gradient for a simple layer."""
layer = bimdp.hinet.CloneBiLayer(
bimdp.nodes.SFA2BiNode(input_dim=5, output_dim=2),
n_nodes=3)
x = numx_rand.random((100,15))
layer.train(x)
layer.stop_training()
mdp.activate_extension("gradient")
try:
x = numx_rand.random((7,15))
result = layer._gradient(x)
grad = result[1]["grad"]
assert grad.shape == (7,6,15)
finally:
mdp.deactivate_extension("gradient")
def test_clonebilayer_gradient(self):
"""Test gradient for a simple layer."""
layer = bimdp.hinet.CloneBiLayer(bimdp.nodes.SFA2BiNode(input_dim=5,
output_dim=2),
n_nodes=3)
x = numx_rand.random((100, 15))
layer.train(x)
layer.stop_training()
mdp.activate_extension("gradient")
try:
x = numx_rand.random((7, 15))
result = layer._gradient(x)
grad = result[1]["grad"]
assert grad.shape == (7, 6, 15)
finally:
mdp.deactivate_extension("gradient")
def test_QuadraticForm_extrema():
# TODO: add some real test
# check H with negligible linear term
noise = 1e-8
tol = 1e-6
x = numx_rand.random((10,))
H = numx.outer(x, x) + numx.eye(10)*0.1
f = noise*numx_rand.random((10,))
q = utils.QuadraticForm(H, f)
xmax, xmin = q.get_extrema(utils.norm2(x), tol=tol)
assert_array_almost_equal(x, xmax, 5)
# check I + linear term
H = numx.eye(10, dtype='d')
f = x
q = utils.QuadraticForm(H, f=f)
xmax, xmin = q.get_extrema(utils.norm2(x), tol=tol)
assert_array_almost_equal(f, xmax, 5)
def test_introspection():
bogus = BogusNode()
arrays, string = utils.dig_node(bogus)
assert len(arrays.keys()) == 4, 'Not all arrays where caught'
assert sorted(arrays.keys()) == ['x', 'y.x',
'z.x', 'z.z.x'], 'Wrong names'
sizes = [x[0] for x in arrays.values()]
assert sorted(sizes) == [numx_rand.random((2,2)).itemsize*4]*4, \
'Wrong sizes'
sfa = nodes.SFANode()
sfa.train(numx_rand.random((1000, 10)))
a_sfa, string = utils.dig_node(sfa)
keys = ['_cov_mtx._avg', '_cov_mtx._cov_mtx',
'_dcov_mtx._avg', '_dcov_mtx._cov_mtx']
assert sorted(a_sfa.keys()) == keys, 'Wrong arrays in SFANode'
sfa.stop_training()
a_sfa, string = utils.dig_node(sfa)
keys = ['_bias', 'avg', 'd', 'davg', 'sf']
assert sorted(a_sfa.keys()) == keys, 'Wrong arrays in SFANode'
def test_quadexpan_gradient2(self):
"""Test gradient with multiple data points."""
node = mdp.nodes.QuadraticExpansionNode()
x = numx_rand.random((3,5))
node.execute(x)
mdp.activate_extension("gradient")
try:
result = node._gradient(x)
gradient = result[1]["grad"]
assert gradient.shape == (3,20,5)
finally:
mdp.deactivate_extension("gradient")
def test_quadexpan_gradient2(self):
"""Test gradient with multiple data points."""
node = mdp.nodes.QuadraticExpansionNode()
x = numx_rand.random((3, 5))
node.execute(x)
mdp.activate_extension("gradient")
try:
result = node._gradient(x)
gradient = result[1]["grad"]
assert gradient.shape == (3, 20, 5)
finally:
mdp.deactivate_extension("gradient")
def test_layer_gradient(self):
"""Test gradient for a simple layer."""
node1 = mdp.nodes.SFA2Node(input_dim=4, output_dim=3)
node2 = mdp.nodes.SFANode(input_dim=6, output_dim=2)
layer = mdp.hinet.Layer([node1, node2])
x = numx_rand.random((100, 10))
layer.train(x)
layer.stop_training()
mdp.activate_extension("gradient")
try:
x = numx_rand.random((7, 10))
result = layer._gradient(x)
grad = result[1]["grad"]
# get reference result
grad1 = node1._gradient(x[:, :node1.input_dim])[1]["grad"]
grad2 = node2._gradient(x[:, node1.input_dim:])[1]["grad"]
ref_grad = numx.zeros(((7, 5, 10)))
ref_grad[:, :node1.output_dim, :node1.input_dim] = grad1
ref_grad[:, node1.output_dim:, node1.input_dim:] = grad2
assert numx.all(grad == ref_grad)
finally:
mdp.deactivate_extension("gradient")
def test_symeig_fake_LAPACK_bug():
# bug. when input matrix is almost an identity matrix
# but not exactly, the lapack dgeev routine returns a
# matrix of eigenvectors which is not orthogonal.
# this bug was present when we used numx_linalg.eig
# instead of numx_linalg.eigh .
# Note: this is a LAPACK bug.
y = numx_rand.random((4,4))*1E-16
y = (y+y.T)/2
for i in xrange(4):
y[i,i]=1
val, vec = utils._symeig._symeig_fake(y)
assert_almost_equal(abs(numx_linalg.det(vec)), 1., 12)
def test_layer_gradient(self):
"""Test gradient for a simple layer."""
node1 = mdp.nodes.SFA2Node(input_dim=4, output_dim=3)
node2 = mdp.nodes.SFANode(input_dim=6, output_dim=2)
layer = mdp.hinet.Layer([node1, node2])
x = numx_rand.random((100,10))
layer.train(x)
layer.stop_training()
mdp.activate_extension("gradient")
try:
x = numx_rand.random((7,10))
result = layer._gradient(x)
grad = result[1]["grad"]
# get reference result
grad1 = node1._gradient(x[:, :node1.input_dim])[1]["grad"]
grad2 = node2._gradient(x[:, node1.input_dim:])[1]["grad"]
ref_grad = numx.zeros(((7,5,10)))
ref_grad[:, :node1.output_dim, :node1.input_dim] = grad1
ref_grad[:, node1.output_dim:, node1.input_dim:] = grad2
assert numx.all(grad == ref_grad)
finally:
mdp.deactivate_extension("gradient")
def test_switchboard_gradient1(self):
"""Test that gradient is correct for a tiny switchboard."""
sboard = mdp.hinet.Switchboard(input_dim=4, connections=[2,0])
x = numx_rand.random((2,4))
mdp.activate_extension("gradient")
try:
result = sboard._gradient(x)
grad = result[1]["grad"]
ref_grad = numx.array([[[0,0,1,0], [1,0,0,0]],
[[0,0,1,0], [1,0,0,0]]], dtype=grad.dtype)
assert numx.all(grad == ref_grad)
finally:
mdp.deactivate_extension("gradient")
def test_network_gradient(self):
"""Test gradient for a small SFA network."""
sfa_node = bimdp.nodes.SFABiNode(input_dim=4 * 4, output_dim=5)
switchboard = bimdp.hinet.Rectangular2dBiSwitchboard(
in_channels_xy=8, field_channels_xy=4, field_spacing_xy=2)
flownode = bimdp.hinet.BiFlowNode(bimdp.BiFlow([sfa_node]))
sfa_layer = bimdp.hinet.CloneBiLayer(flownode,
switchboard.output_channels)
flow = bimdp.BiFlow([switchboard, sfa_layer])
train_gen = [
numx_rand.random((10, switchboard.input_dim)) for _ in range(3)
]
flow.train([None, train_gen])
# now can test the gradient
mdp.activate_extension("gradient")
try:
x = numx_rand.random((3, switchboard.input_dim))
result = flow(x, {"method": "gradient"})
grad = result[1]["grad"]
assert grad.shape == (3, sfa_layer.output_dim,
switchboard.input_dim)
finally:
mdp.deactivate_extension("gradient")
def test_switchboard_gradient1(self):
"""Test that gradient is correct for a tiny switchboard."""
sboard = mdp.hinet.Switchboard(input_dim=4, connections=[2, 0])
x = numx_rand.random((2, 4))
mdp.activate_extension("gradient")
try:
result = sboard._gradient(x)
grad = result[1]["grad"]
ref_grad = numx.array(
[[[0, 0, 1, 0], [1, 0, 0, 0]], [[0, 0, 1, 0], [1, 0, 0, 0]]],
dtype=grad.dtype)
assert numx.all(grad == ref_grad)
finally:
mdp.deactivate_extension("gradient")
def test_gradient_product(self):
"""Test that the product of gradients is calculated correctly."""
sfa_node1 = bimdp.nodes.SFABiNode(output_dim=5)
sfa_node2 = bimdp.nodes.SFABiNode(output_dim=3)
flow = sfa_node1 + sfa_node2
x = numx_rand.random((300, 10))
flow.train(x)
mdp.activate_extension("gradient")
try:
x1 = numx_rand.random((2, 10))
x2, msg = sfa_node1.execute(x1, {"method": "gradient"})
grad1 = msg["grad"]
_, msg = sfa_node2.execute(x2, {"method": "gradient"})
grad2 = msg["grad"]
grad12 = flow.execute(x1, {"method": "gradient"})[1]["grad"]
# use a different way to calculate the product of the gradients,
# this method is too memory intensive for large data
ref_grad = numx.sum(grad2[:,:,numx.newaxis,:] *
numx.transpose(grad1[:,numx.newaxis,:,:], (0,1,3,2)),
axis=3)
assert numx.amax(abs(ref_grad - grad12)) < 1E-9
finally:
mdp.deactivate_extension("gradient")
def test_gradient_product(self):
"""Test that the product of gradients is calculated correctly."""
sfa_node1 = bimdp.nodes.SFABiNode(output_dim=5)
sfa_node2 = bimdp.nodes.SFABiNode(output_dim=3)
flow = sfa_node1 + sfa_node2
x = numx_rand.random((300, 10))
flow.train(x)
mdp.activate_extension("gradient")
try:
x1 = numx_rand.random((2, 10))
x2, msg = sfa_node1.execute(x1, {"method": "gradient"})
grad1 = msg["grad"]
_, msg = sfa_node2.execute(x2, {"method": "gradient"})
grad2 = msg["grad"]
grad12 = flow.execute(x1, {"method": "gradient"})[1]["grad"]
# use a different way to calculate the product of the gradients,
# this method is too memory intensive for large data
ref_grad = numx.sum(grad2[:, :, numx.newaxis, :] *
numx.transpose(grad1[:, numx.newaxis, :, :],
(0, 1, 3, 2)),
axis=3)
assert numx.amax(abs(ref_grad - grad12)) < 1E-9
finally:
mdp.deactivate_extension("gradient")
def test_network_gradient(self):
"""Test gradient for a small SFA network."""
sfa_node = bimdp.nodes.SFABiNode(input_dim=4*4, output_dim=5)
switchboard = bimdp.hinet.Rectangular2dBiSwitchboard(
in_channels_xy=8,
field_channels_xy=4,
field_spacing_xy=2)
flownode = bimdp.hinet.BiFlowNode(bimdp.BiFlow([sfa_node]))
sfa_layer = bimdp.hinet.CloneBiLayer(flownode,
switchboard.output_channels)
flow = bimdp.BiFlow([switchboard, sfa_layer])
train_gen = [numx_rand.random((10, switchboard.input_dim))
for _ in range(3)]
flow.train([None, train_gen])
# now can test the gradient
mdp.activate_extension("gradient")
try:
x = numx_rand.random((3, switchboard.input_dim))
result = flow(x, {"method": "gradient"})
grad = result[1]["grad"]
assert grad.shape == (3, sfa_layer.output_dim,
switchboard.input_dim)
finally:
mdp.deactivate_extension("gradient")
def test_QuadraticForm_non_symmetric_raises():
"""Test the detection of non symmetric H!
"""
H = numx_rand.random((10,10))
py.test.raises(mdp.utils.QuadraticFormException,
utils.QuadraticForm, H)
def __init__(self):
self.x = numx_rand.random((2,2))