def alternating_alignments_nonlinear(X,Y,proj_align,corr_align,threshold,max_iters):
corr = Correspondence(pairs=np.array(((0,0),(X.shape[0]-1,Y.shape[0]-1))))
X_proj,Y_proj = proj_align(X,Y,corr)
for it in xrange(max_iters):
new_corr = corr_align(X_proj,Y_proj)
if corr.dist_from(new_corr) < threshold:
return new_corr, X_proj, Y_proj
corr = new_corr
X_proj,Y_proj = proj_align(X_proj,Y_proj,corr)
return corr, X_proj, Y_proj
def alternating_alignments(X,Y,proj_align,corr_align,threshold,max_iters):
corr = Correspondence(pairs=np.array(((0,0),(X.shape[0]-1,Y.shape[0]-1))))
aln = TrivialAlignment(X,Y)
X_proj,Y_proj = X.copy(), Y.copy() # same as aln.project(X,Y)
for it in xrange(max_iters):
aln.apply_transform(proj_align(X_proj,Y_proj,corr))
X_proj,Y_proj = aln.project(X,Y)
new_corr = corr_align(X_proj,Y_proj)
if corr.dist_from(new_corr) < threshold:
return new_corr, aln
corr = new_corr
return corr, aln
def dtw(X, Y, metric=SquaredL2, debug=False):
'''Dynamic Time Warping'''
dist = metric.between(X, Y)
if debug:
path = _python_dtw_path(dist)
else:
path = _dtw_path(dist)
return Correspondence(pairs=path)
def proposeCorrespondence(self, initialObject, targetObject, conceptMappings, flipTargetObject, oldCodelet):
from correspondence import Correspondence
correspondence = Correspondence(initialObject, targetObject, conceptMappings, flipTargetObject)
for mapping in conceptMappings:
mapping.initialDescriptionType.buffer = 100.0
mapping.initialDescriptor.buffer = 100.0
mapping.targetDescriptionType.buffer = 100.0
mapping.targetDescriptor.buffer = 100.0
mappings = correspondence.distinguishingConceptMappings()
urgency = sum([mapping.strength() for mapping in mappings])
numberOfMappings = len(mappings)
if urgency:
urgency /= numberOfMappings
bin = self.getUrgencyBin(urgency)
logging.info('urgency: %s, number: %d, bin: %d' % (urgency, numberOfMappings, bin))
self.newCodelet('correspondence-strength-tester', oldCodelet, urgency, correspondence)
def proposeCorrespondence(self, initialObject, targetObject,
conceptMappings, flipTargetObject, oldCodelet):
from correspondence import Correspondence
correspondence = Correspondence(initialObject, targetObject,
conceptMappings, flipTargetObject)
for mapping in conceptMappings:
mapping.initialDescriptionType.buffer = 100.0
mapping.initialDescriptor.buffer = 100.0
mapping.targetDescriptionType.buffer = 100.0
mapping.targetDescriptor.buffer = 100.0
mappings = correspondence.distinguishingConceptMappings()
urgency = sum([mapping.strength() for mapping in mappings])
numberOfMappings = len(mappings)
if urgency:
urgency /= numberOfMappings
binn = getUrgencyBin(urgency)
logging.info('urgency: %s, number: %d, bin: %d', urgency,
numberOfMappings, binn)
self.newCodelet('correspondence-strength-tester', oldCodelet, urgency,
correspondence)
if __name__ == '__main__':
# simple usage example / visual test case
from matplotlib import pyplot
from viz import show_neighbor_graph
from util import Timer
from correspondence import Correspondence
from synthetic_data import cylinder
n = 300
knn = 5
out_dim = 2
X = cylinder(np.linspace(0, 4, n))
W = neighbor_graph(X=X, k=knn)
corr = Correspondence(matrix=W)
with Timer('LapEig'):
le_embed = lapeig(W=W, num_vecs=out_dim)
with Timer('Linear LapEig'):
# lapeig_linear returns a projector, not an embedding
lel_embed = np.dot(X, lapeig_linear(X=X, W=W, num_vecs=out_dim, k=knn))
with Timer('Isomap'):
im_embed = isomap(X=X, num_vecs=out_dim, k=knn)
with Timer('LLE'):
lle_embed = lle(X=X, num_vecs=out_dim, k=knn)
with Timer('SFA'):
sfa_embed = np.dot(X, slow_features(X=X, num_vecs=out_dim))
show_neighbor_graph(X, corr, 'Original space')
def gen_data(n, three_d=False):
t = np.linspace(0, 5, n)
if three_d:
X = swiss_roll(t, lambda A: np.sin(A)**2)
Y = np.vstack((np.sin(t)**2, t, np.zeros(n))).T
else:
X = spiral(t)
Y = X[:, (1, 0)] # swap x and y axes
return add_noise(X, 0.05), add_noise(Y, 0.05)
if __name__ == '__main__':
n = 500
d = 3
X, Y = gen_data(n, d == 3)
corr = Correspondence(matrix=np.eye(n))
Wx = neighbor_graph(X, k=5)
Wy = neighbor_graph(Y, k=5)
lin_aligners = (
('no alignment', lambda: TrivialAlignment(X, Y)),
('affine', lambda: Affine(X, Y, corr, d)),
('procrustes', lambda: Procrustes(X, Y, corr, d)),
('cca', lambda: CCA(X, Y, corr, d)),
('cca_v2', lambda: CCAv2(X, Y, d)),
('linear manifold', lambda: ManifoldLinear(X, Y, corr, d, Wx, Wy)),
('ctw', lambda: ctw(X, Y, d)[1]),
('manifold warping',
lambda: manifold_warping_linear(X, Y, d, Wx, Wy)[1]),
)
file = "expr.RData"
rf = r['load'](file)
dayExpr = pandas2ri.ri2py_dataframe(r['dayExpr'])
nightExpr = pandas2ri.ri2py_dataframe(r['nightExpr'])
X = dayExpr.as_matrix() # datWorm.iloc[:, 0:datWorm.shape[1]].as_matrix()
Y = nightExpr.as_matrix() # datFly.iloc[:, 0:datFly.shape[1]].as_matrix()
n = 17695
d = 3
X_normalized = preprocessing.normalize(X, norm='l2').T
Y_normalized = preprocessing.normalize(Y, norm='l2')[0:13, :].T
corr = Correspondence(
matrix=np.eye(n)) # Correspondence(matrix=corr.as_matrix())
Wx = neighbor_graph(X_normalized, k=5)
Wy = neighbor_graph(Y_normalized, k=5)
lin_aligners = (
('no alignment', lambda: TrivialAlignment(X_normalized, Y_normalized, d)),
# ('affine', lambda: Affine(X,Y,corr,d)),
# ('procrustes', lambda: Procrustes(X,Y,corr,d)),
('cca', lambda: CCA(X_normalized, Y_normalized, corr, d)),
# ('cca_v2', lambda: CCAv2(X,Y,d)),
('linear manifold',
lambda: ManifoldLinear(X_normalized, Y_normalized, corr, d, Wx, Wy)),
('ctw', lambda: ctw(X_normalized, Y_normalized, d)[1]),
('manifold warping',
lambda: manifold_warping_linear(X_normalized, Y_normalized, d, Wx, Wy)[1]
),
