def Evaluation_CP():
"""
CP分解のクロスバリデーションによる評価
"""
logger = Logger("CPEvaluation")
s = 20
r = 3
re = 2
size = [s, s, s]
rank = r
rank_estimate = re
alpha = 0.001
logger.WriteLine("TensorSize=" + str(size))
logger.WriteLine("TensorRank=" + str(rank))
logger.WriteLine("EstimationRank" + str(rank_estimate))
Y = alg.randomTensorOfNorm(size, rank, 0.05) * 100
I = alg.createUnitTensor(len(size), rank)
while True:
testrates = [0.1 * (i + 1) for i in xrange(9)]
for zerorate in testrates:
W = createMask(size, zerorate)
def approximate(Xin):
As = alg.RRMFCP(Xin, rank_estimate, alpha)
Xs = alg.expand(I, As)
return Xs
X = comp.Completion(Y, W, approximate)
diff = norm(Y - X)
logger.WriteLine(str(zerorate) + " " + str(diff))
print "rate:", zerorate, "error:", norm(Y - X)
def Evaluation_Tucker():
"""
Tucker分解のクロスバリデーションによる評価
"""
logger = Logger("TuckerEvaluation")
s = 20
r = 3
re = 2
size = [s, s, s]
rank = [r, r, r]
rank_estimate = [re, re, re]
logger.WriteLine("TensorSize=" + str(size))
logger.WriteLine("TensorRank=" + str(rank))
logger.WriteLine("EstimationRank" + str(rank_estimate))
Y = alg.randomTensorOfNorm(size, rank, 0.05) * 100
while True:
testrates = [0.02 * (i + 1) for i in xrange(49)]
for zerorate in testrates:
W = createMask(size, zerorate)
def approximate(Xin):
(G, As) = alg.HOOI(Xin, rank_estimate)
Xs = alg.expand(G, As)
return Xs
X = comp.Completion(Y, W, approximate)
diff = norm(Y - X)
logger.WriteLine(str(zerorate) + " " + str(diff))
print "rate:", zerorate, "error:", norm(Y - X)
def Evaluation_Tucker():
"""
Tucker分解のクロスバリデーションによる評価
"""
logger = Logger("TuckerEvaluation")
s = 20
r = 3
re = 2
size = [s,s,s]
rank = [r,r,r]
rank_estimate = [re,re,re]
logger.WriteLine("TensorSize="+str(size))
logger.WriteLine("TensorRank="+str(rank))
logger.WriteLine("EstimationRank"+str(rank_estimate))
Y = alg.randomTensorOfNorm(size,rank,0.05) * 100
while True:
testrates = [0.02 * (i+1) for i in xrange(49)]
for zerorate in testrates:
W = createMask(size,zerorate)
def approximate(Xin):
(G,As) = alg.HOOI(Xin,rank_estimate)
Xs = alg.expand(G,As)
return Xs
X = comp.Completion(Y,W,approximate)
diff = norm(Y-X)
logger.WriteLine(str(zerorate)+ " " + str(diff))
print "rate:", zerorate, "error:", norm(Y-X)
def Evaluation_CP():
"""
CP分解のクロスバリデーションによる評価
"""
logger = Logger("CPEvaluation")
s = 20
r = 3
re = 2
size = [s,s,s]
rank = r
rank_estimate = re
alpha = 0.001
logger.WriteLine("TensorSize="+str(size))
logger.WriteLine("TensorRank="+str(rank))
logger.WriteLine("EstimationRank"+str(rank_estimate))
Y = alg.randomTensorOfNorm(size,rank,0.05) * 100
I = alg.createUnitTensor(len(size),rank)
while True:
testrates = [0.1 * (i+1) for i in xrange(9)]
for zerorate in testrates:
W = createMask(size,zerorate)
def approximate(Xin):
As = alg.RRMFCP(Xin,rank_estimate,alpha)
Xs = alg.expand(I,As)
return Xs
X = comp.Completion(Y,W,approximate)
diff = norm(Y-X)
logger.WriteLine(str(zerorate)+ " " + str(diff))
print "rate:", zerorate, "error:", norm(Y-X)
def getTensor(mat):
size = 30
rank = 20
sl = [100,100,500]
rl = [rank,rank,rank]
return algorithm.randomTensorOfNorm(sl,rl,0.01)
def getTensor(mat):
size = 30
rank = 20
sl = [100, 100, 500]
rl = [rank, rank, rank]
return algorithm.randomTensorOfNorm(sl, rl, 0.01)