def computeDecomp(self, gamma=None, gradual=True):
## random initialize if not existing
if self.M[REG_LOCATION] == None and self.M[AUG_LOCATION] == None:
self.initialize()
## Kkeep track of the iteration information
iterInfo = OrderedDict(sorted({}.items(), key=lambda t:t[1]))
lastLL = tensorTools.loglikelihood(self.X, self.M)
## projection factor starts at 0 (unless there's no gradual)
xi = 0 if gradual else 1
## if nothing is set, we're just not going to do any hard-thresholding
if gamma == None:
gamma = list(np.repeat(0, self.N))
## for outer iterations
for iteration in range(self.maxIters):
startIter = time.time()
for n in range(self.N):
startMode = time.time()
## first we calculate the "augmented" tensor matricization
self.M[AUG_LOCATION].redistribute(n)
xsubs = self.X.subs[:,n]
B, Pi, inI1, kktModeViolation1 = self.__solveSignalTensor(xsubs, self.M[AUG_LOCATION].U[n], n)
## hard threshold based on the xi and gamma
thr = xi * gamma[n]
if (thr > 0):
self.M[REG_LOCATION].U[n] = tensorTools.hardThresholdMatrix(self.M[REG_LOCATION].U[n], thr)
# renormalize the mode
self.M[REG_LOCATION].normalize_mode(n, 1)
## recalculate B using the new matrix
B = np.dot(self.M[REG_LOCATION].U[n], np.diag(self.M[REG_LOCATION].lmbda))
elapsed1 = time.time() - startMode
# now that we are done, we can calculate the new 'unaugmented matricization'
inI2, kktModeViolation2 = self.__solveAugmentedTensor(xsubs, B, Pi, n)
elapsed2 = time.time() - startMode
ll = tensorTools.loglikelihood(self.X, self.M)
iterInfo[str((iteration, n))] = { "Time": [elapsed1, elapsed2],
"KKTViolation": [kktModeViolation1, kktModeViolation2],
"Iterations": [inI1, inI2],
"LL": ll}
if gradual:
xiTemp = 1-np.min([1, (np.absolute(lastLL - ll) / np.max(np.absolute([lastLL,ll])))])
if xiTemp > xi:
## take the mean of the two
xi = (xi + xiTemp) / 2
print("Iteration {0}: Xi = {1}, dll = {2}, time = {3}".format(iteration, xi, np.abs(lastLL - ll), time.time() - startIter))
if np.abs(lastLL - ll) < self.dlTol and xi >= 0.99:
break;
lastLL = ll
return iterInfo
maxiters=MAX_ITER)
Yinfo = spntf.computeDecomp(gamma=gamma)
## calculate all the request entries
marbleElapse = time.time() - startTime
marbleFMS, marbleFOS, marbleNNZ = calculateValues(
TM, spntf.M[SP_NTF.REG_LOCATION])
np.random.seed(seed)
startTime = time.time()
YCP, ycpstats, mstats = CP_APR.cp_apr(X,
R=R,
maxinner=INNER_ITER,
maxiters=MAX_ITER)
cpaprElapse = time.time() - startTime
cpaprFMS, cpaprFOS, cpaprNNZ = calculateValues(TM, YCP)
for n in range(YCP.ndims()):
YCP.U[n] = tensorTools.hardThresholdMatrix(YCP.U[n], gamma[n])
limestoneFMS, limestoneFOS, limestoneNNZ = calculateValues(TM, YCP)
sampleResult = {
"Order": ["Marble", "CPAPR", "Limestone"],
"FMS": [marbleFMS, cpaprFMS, limestoneFMS],
"FOS": [marbleFOS, cpaprFOS, limestoneFOS],
"CompTime": [marbleElapse, cpaprElapse, cpaprElapse],
"NNZ": [marbleNNZ, cpaprNNZ, limestoneNNZ]
}
data[str(sample)] = sampleResult
with open('results/simulation-{0}.json'.format(exptID), 'w') as outfile:
json.dump(data, outfile)
seed = sample*1000
np.random.seed(seed)
## solve the solution
startTime = time.time()
spntf = SP_NTF.SP_NTF(X, R=R, alpha=alpha, maxinner=INNER_ITER, maxiters=MAX_ITER)
Yinfo = spntf.computeDecomp(gamma=gamma)
## calculate all the request entries
marbleElapse = time.time() - startTime
marbleFMS, marbleFOS, marbleNNZ = calculateValues(TM, spntf.M[SP_NTF.REG_LOCATION])
np.random.seed(seed)
startTime = time.time()
YCP, ycpstats, mstats = CP_APR.cp_apr(X, R=R, maxinner=INNER_ITER, maxiters=MAX_ITER)
cpaprElapse = time.time() - startTime
cpaprFMS, cpaprFOS, cpaprNNZ = calculateValues(TM, YCP)
for n in range(YCP.ndims()):
YCP.U[n] = tensorTools.hardThresholdMatrix(YCP.U[n], gamma[n])
limestoneFMS, limestoneFOS, limestoneNNZ = calculateValues(TM, YCP)
sampleResult = {
"Order": ["Marble", "CPAPR", "Limestone"],
"FMS":[marbleFMS, cpaprFMS, limestoneFMS],
"FOS":[marbleFOS, cpaprFOS, limestoneFOS],
"CompTime": [marbleElapse, cpaprElapse, cpaprElapse],
"NNZ": [marbleNNZ, cpaprNNZ, limestoneNNZ]
}
data[str(sample)] = sampleResult
with open('results/simulation-{0}.json'.format(exptID), 'w') as outfile:
json.dump(data, outfile)
