MSize = args.MS
gamma = args.gamma
AFill = args.fill
startSeed = args.seed
### Generate the solution for comparison purposes ###
print "Generating simulation data with known decomposition"
TM, TMHat = simultTools.generateSolution(MSize, R, AFill, alpha)
TMFull = TM.toTensor() + TMHat.toTensor()
np.random.seed(startSeed)
outfile = open('results/iteration-{0}.json'.format(exptID), 'w')
for sample in range(10):
## generate a random problem
X = simultTools.generateRandomProblem(TMFull) #sparse tensor
data = {
'exptID': exptID,
'size': MSize,
'sparsity': AFill,
'sample': sample,
"rank": R,
"alpha": alpha,
"gamma": gamma,
"seed": startSeed
}
seed = sample * 1000
for innerIt in [1, 2, 5, 10]:
## set seed for consistency
np.random.seed(seed)
## solve the solution
R = 3
alpha = 1
MSize = [2,2,2]
gamma = None
AFill = [2,2,2]
startSeed = 1
print "Generating simulation data with known decomposition"
TM, TMHat = simultTools.generateSolution(MSize, R, AFill, alpha)
TMFull = TM.toTensor() + TMHat.toTensor()
np.random.seed(startSeed)
#generate random problem
sample = 0
X = simultTools.generateRandomProblem(TMFull)
data = {'exptID': exptID, 'size': MSize, 'sparsity': AFill, 'sample': sample,
"rank": R, "alpha": alpha, "gamma": gamma, "seed": startSeed}
## solve the solution
innerIt = 1
startTime = time.time()
spntf = SP_NTF.SP_NTF(X, R=R, alpha=alpha, maxinner = innerIt)
Yinfo = spntf.computeDecomp(gamma=gamma)
totalTime = time.time() - startTime
sampleResult = {
"compTime": totalTime,
"iterInfo": Yinfo,
"fms": TM.greedy_fms(spntf.M[SP_NTF.REG_LOCATION])
}
data[str(innerIt)] = sampleResult
exptID = args.expt
R = args.r
alpha = args.alpha
MSize = args.MS
gamma = args.gamma
AFill = args.fill
INNER_ITER = 5
MAX_ITER = 500
print "Generating simulation data with known decomposition"
## generate the solution
TM, TMHat = simultTools.generateSolution(MSize, R, AFill, alpha)
TMFull = TM.toTensor() + TMHat.toTensor()
## generate an observation from the known solution
X = simultTools.generateRandomProblem(TMFull)
data = {
'exptID': exptID,
'size': MSize,
'sparsity': AFill,
"rank": R,
"alpha": alpha,
"gamma": gamma
}
def calculateValues(TM, M):
fms = TM.greedy_fms(M)
fos = TM.greedy_fos(M)
nnz = tensorTools.countTensorNNZ(M)
MSize = args.MS
gamma = args.gamma
AFill = args.fill
startSeed = args.seed
### Generate the solution for comparison purposes ###
print "Generating simulation data with known decomposition"
TM, TMHat = simultTools.generateSolution(MSize, R, AFill, alpha)
TMFull = TM.toTensor() + TMHat.toTensor()
np.random.seed(startSeed)
outfile = open('results/iteration-{0}.json'.format(exptID), 'w')
for sample in range(10):
## generate a random problem
X = simultTools.generateRandomProblem(TMFull) #sparse tensor
data = {'exptID': exptID, 'size': MSize, 'sparsity': AFill, 'sample': sample,
"rank": R, "alpha": alpha, "gamma": gamma, "seed": startSeed}
seed = sample*1000
for innerIt in [1, 2, 5, 10]:
## set seed for consistency
np.random.seed(seed)
## solve the solution
startTime = time.time()
spntf = SP_NTF.SP_NTF(X, R=R, alpha=alpha, maxinner = innerIt)
Yinfo = spntf.computeDecomp(gamma=gamma)
totalTime = time.time() - startTime
sampleResult = {
"compTime": totalTime,
"iterInfo": Yinfo,
"fms": TM.greedy_fms(spntf.M[SP_NTF.REG_LOCATION])
