def next(self):
X = self.XIterator.next()
#Return the matrices P, Q as the learnt model
if self.ZListSGD == None:
# assumption : training matrix centered by row and column
self.ZListSGD = self.baseLearner.learnModel(X, storeAll=False)
else:
#In the case the matrix size changes, we alter P and Q to fit the new data
P, Q = self.ZListSGD[0]
if X.shape[0] > P.shape[0]:
P = Util.extendArray(P, (X.shape[0], P.shape[1]))
elif X.shape[0] < P.shape[0]:
P = P[0:X.shape[0], :]
if X.shape[1] > Q.shape[0]:
Q = Util.extendArray(Q, (X.shape[1], Q.shape[1]))
elif X.shape[1] < Q.shape[0]:
Q = Q[0:X.shape[1], :]
self.ZListSGD = [(P, Q)]
try:
self.ZListSGD = self.baseLearner.learnModel(X, Z=self.ZListSGD, storeAll=False)
except FloatingPointError:
logging.warning("FloatingPointError encountered, reinitialise the matrix decomposition")
self.ZListSGD = self.baseLearner.learnModel(X, storeAll=False)
except ValueError:
logging.warning("ValueError encountered, reinitialise the matrix decomposition")
self.ZListSGD = self.baseLearner.learnModel(X, storeAll=False)
except SGDNorm2Reg.ArithmeticError:
logging.warning("ArithmeticError encountered, reinitialise the matrix decomposition")
self.ZListSGD = self.baseLearner.learnModel(X, storeAll=False)
return self.ZListSGD
def next(self):
X = self.XIterator.next()
#Return the matrices P, Q as the learnt model
if self.ZListSGD == None:
# assumption : training matrix centered by row and column
self.ZListSGD = self.baseLearner.learnModel(X,
storeAll=False)
else:
#In the case the matrix size changes, we alter P and Q to fit the new data
P, Q = self.ZListSGD[0]
if X.shape[0] > P.shape[0]:
P = Util.extendArray(P, (X.shape[0], P.shape[1]))
elif X.shape[0] < P.shape[0]:
P = P[0:X.shape[0], :]
if X.shape[1] > Q.shape[0]:
Q = Util.extendArray(Q, (X.shape[1], Q.shape[1]))
elif X.shape[1] < Q.shape[0]:
Q = Q[0:X.shape[1], :]
self.ZListSGD = [(P, Q)]
try:
self.ZListSGD = self.baseLearner.learnModel(
X, Z=self.ZListSGD, storeAll=False)
except FloatingPointError:
logging.warning(
"FloatingPointError encountered, reinitialise the matrix decomposition"
)
self.ZListSGD = self.baseLearner.learnModel(
X, storeAll=False)
except ValueError:
logging.warning(
"ValueError encountered, reinitialise the matrix decomposition"
)
self.ZListSGD = self.baseLearner.learnModel(
X, storeAll=False)
except SGDNorm2Reg.ArithmeticError:
logging.warning(
"ArithmeticError encountered, reinitialise the matrix decomposition"
)
self.ZListSGD = self.baseLearner.learnModel(
X, storeAll=False)
return self.ZListSGD
