def testTucker():
s = 8
r = 5
re = 3
size = [s,s,s]
rank = [r,r,r]
rank_estimate = [re,re,re]
L = createTestLaplacian(s)
alpha = 0.01
while True:
#X = alg.randomTensorOfNorm(size,rank,0)
dat = benchmark.ThreeDNoseData()
X = dat["X"]
L = dat["L"]
X = X / norm(X)
print "start estimation for tensor size of", X.shape
(G,As) = alg.HOOI(X,rank_estimate,L,alpha)
print "finished"
Result = alg.expand(G,As)
#print "original \n", X
#print "estimated \n",Result
print "error \n", norm(X - Result)
raw_input()
def testTucker():
size = [3, 3, 3]
rank = [3, 3, 3]
rank_estimate = [1, 1, 1]
X = randomTensorOfNorm(size, rank)
(G, As) = alg.HOOI(X, rank_estimate)
print "finished"
Result = alg.expand(G, As)
print "original \n", X
print "estimated \n", Result
print "error \n", norm(X - Result)
def approximate(Xin):
(G,As) = alg.HOOI(Xin,rank_estimate,alpha,L)
Xs = alg.expand(G,As)
return Xs