def approximateCP(Xin):
print re
print alpha
print Ls
print beta
As = alg.RRMFCP(Xin,re,beta,Ls,alpha)
Xs = alg.expand(I,As)
return Xs
def testL():
"""
過去の遺産
"""
s = 100
size = [s, s, s]
rank = 4
rank_estimate = 15
alpha = 0.1
beta = 0.00
while True:
#X = alg.randomTensorOfNorm(size,rank,0.05)
dat = benchmark.Bonnie()
X = dat["X"]
L = dat["L"]
#X = X - mean(X)
#X = X + min(X.reshape(prod(X.shape)))
print "norm:", norm(X)
originalNorm = norm(X)
X = X / norm(X)
#X = X+0.1
print[det(l) for l in L]
return
print X.shape
#As = alg.RRMFCP(X,rank_estimate,alpha,[L,None,None],beta)
As = alg.RRMFCP(X, rank_estimate, beta, L, alpha)
#As = cpold.RRMFCP(X,rank_estimate,alpha)
print "finished"
I = alg.createUnitTensor(len(size), rank_estimate)
Result = alg.expand(I, As)
#vint = vectorize(int)
#Result = vint(Result*originalNorm+0.5) / originalNorm
#Result = sign(vint(Result))
#benchmark.SaveImage(X.reshape(151*2,151*19),"Sugar")
#benchmark.SaveImage(Result.reshape(151*2,151*19),"Est_Sugar")
#benchmark.SaveImage(abs(X-Result).reshape(151*2,151*19),"Diff_Sugar")
#Result = Result / norm(Result)
#print "original \n", X
print "estimated \n", abs(X - Result)
print "error \n", norm(X - Result)
#print As[0]
raw_input()
def testCP():
s = 100
size = [s,s,s]
rank = 4
rank_estimate = 5
alpha = 0.1
beta = 0.0001
while True:
#X = alg.randomTensorOfNorm(size,rank,0.05)
#dat = benchmark.ThreeDNoseData()
#dat = benchmark.RandomSmallTensor()
dat = benchmark.Flow_Injection()
#dat = benchmark.Bonnie()
#dat = benchmark.Artificial()
X = dat["X"]
L = dat["L"]
#X = X - mean(X)
#X = X + min(X.reshape(prod(X.shape)))
print "norm:",norm(X)
X = X / norm(X)
#X = X+0.1
L = [None,None,None]
print X.shape
#As = alg.RRMFCP(X,rank_estimate,alpha,[L,None,None],beta)
As = alg.RRMFCP(X,rank_estimate,beta,L,alpha)
#As = cpold.RRMFCP(X,rank_estimate,alpha)
print "finished"
I = alg.createUnitTensor(len(size),rank_estimate)
Result = alg.expand(I,As)
benchmark.SaveImage(X,"Flow")
benchmark.SaveImage(Result,"Est_Flow")
#Result = Result / norm(Result)
#print "original \n", X
#print "estimated \n",Result
print "error \n", norm(X - Result)
#print As[0]
raw_input()
def approximate(Xin):
As = alg.RRMFCP(Xin,rank_estimate,beta,Ls,alpha,Ps,Ds)
Xs = alg.expand(I,As)
return Xs
def testCP():
"""
CP分解テスト
"""
s = 100
size = [s, s, s]
rank = 10
rank_estimate = 15
alpha = 0.1
beta = 0.00
#X = alg.randomTensorOfNorm(size,rank,0.05)
#dat = benchmark.ThreeDNoseData()
dat = benchmark.RandomSmallTensor()
#dat = benchmark.Flow_Injection()
#dat = benchmark.Bonnie()
#dat = benchmark.Enron()
#dat = benchmark.Sugar()
#dat = benchmark.Artificial()
while True:
X = dat["X"]
L = dat["L"]
#X = X - mean(X)
#X = X + min(X.reshape(prod(X.shape)))
print "norm:", norm(X)
originalNorm = norm(X)
X = X / norm(X)
#X = X+0.1
L = [None, None, None]
print X.shape
#As = alg.RRMFCP(X,rank_estimate,alpha,[L,None,None],beta)
start = datetime.datetime.today()
As = alg.RRMFCP(X, rank_estimate, beta, L, alpha)
end = datetime.datetime.today()
#As = cpold.RRMFCP(X,rank_estimate,alpha)
print "finished"
print end - start
I = alg.createUnitTensor(len(size), rank_estimate)
Result = alg.expand(I, As)
#vint = vectorize(int)
#Result = vint(Result*originalNorm+0.5) / originalNorm
#Result = sign(vint(Result))
#benchmark.SaveImage(X.reshape(151*2,151*19),"Sugar")
#benchmark.SaveImage(Result.reshape(151*2,151*19),"Est_Sugar")
#benchmark.SaveImage(abs(X-Result).reshape(151*2,151*19),"Diff_Sugar")
#Result = Result / norm(Result)
#print "original \n", X
#print "estimated \n",abs(X-Result)
print "error \n", norm(X - Result)
#print As[0]
raw_input()
def approximate(Xin):
As = alg.RRMFCP(Xin, rank_estimate, alpha)
Xs = alg.expand(I, As)
return Xs