def test1():
# test
C = np.matrix('1 -1 1 1; 1 1 1 0; 0 1 1 1 ;1 -3 -2 1')
(cost, pi, edge_cost) = approxMatch(C)
(cy_cost, cy_pi, cy_edge_cost) = fast_approxMatch(C)
print C
print "cost:", cost, cy_cost-cost
print pi, pi-cy_pi
print edge_cost, cy_edge_cost - edge_cost
D = 2000
np.random.seed(1)
C = common.randn((D, D))
import cProfile
cProfile.runctx('(cost, pi, edge_cost) = approxMatch(C)', globals(), locals())
cProfile.runctx('(cy_cost0, cy_pi0, cy_edge_cost0) = cy_ApproxMatch(C)', globals(), locals())
(cost, pi, edge_cost) = approxMatch(C)
(cy_cost0, cy_pi0, cy_edge_cost0) = cy_ApproxMatch(C)
assert np.linalg.norm(cy_cost0-cost) == 0
assert np.linalg.norm(pi-cy_pi0) == 0
assert np.linalg.norm(cy_edge_cost0 - edge_cost) == 0
X = np.array([[ 0.17034787, -1.11852005, 2.3723583 ],
[ 0.40587496, -0.71610151, 0.24853477],
[ 0.28721089, -1.62157422, 0.33806607],
[ 0.88027416, 0.30368357, -1.15908568],
[-0.50893908, -0.61650787, -1.10849268]])
Y = np.array([[ 0.49316125, -1.6459511 , 0.03514289],
[ 0.16211477, 0.41796482, -0.19066103],
[-0.68095143, 0.48752118, 1.08161025],
[-0.44147856, 0.43943179, 1.05625251]])
GX = np.array([[0, 1, 1, 0, 1],
[0, 1, 1, 1, 0],
[0, 1, 0, 1, 1],
[0, 1, 1, 0, 0],
[1, 0, 0, 0, 1]])
GY = np.array([[0, 1, 1, 0],
[0, 1, 0, 0],
[1, 0, 1, 1],
[0, 0, 0, 1]])
options = common.Struct()
options.weight_type = 'dist'
options.K = 1 # K is not really 1 here, but a non-zero value is required.
options.alpha = 0.3
options.normalize_projections = 1
W, U, Z = makeWeights(options, X, Y, GX, GY)
print 'W shape: ', W.shape
print W
def fast_approxMatch(C):
if isinstance(C, np.matrix):
C = np.array(C, dtype=np.double)
return cy_ApproxMatch(C)
