def copy(self, new_obj):
nkernel = len(self.XKernel)
ndata = len(self.xdata_rel)
new_obj.xdata_rel = [None] * ndata
for i in range(ndata):
new_obj.xdata_rel[i] = self.xdata_rel[i][self.itrain]
for ikernel in range(nkernel):
new_obj.XKernel[ikernel] = self.XKernel[ikernel].copy()
new_obj.YKernel = self.YKernel.copy()
new_obj.set_validation()
new_obj.penalty = base.cls_penalty()
new_obj.penalty.c = self.penalty.c
new_obj.penalty.d = self.penalty.d
new_obj.penalty.crossval = self.penalty.crossval
new_obj.glm_model = self.glm_model
new_obj.nrow = self.nrow
new_obj.ncol = self.ncol
new_obj.itestmode = self.itestmode
new_obj.kmode = self.kmode
new_obj.xbias = self.xbias
new_obj.ieval_type = self.ieval_type
new_obj.ibinary = self.ibinary
new_obj.categorymax = self.categorymax
new_obj.Y0 = self.Y0
new_obj.rowcol = self.rowcol
def copy(self,new_obj):
nkernel=len(self.XKernel)
ndata=len(self.xdata_rel)
new_obj.xdata_rel=[None]*ndata
for i in range(ndata):
new_obj.xdata_rel[i]=self.xdata_rel[i][self.itrain]
for ikernel in range(nkernel):
new_obj.XKernel[ikernel]=self.XKernel[ikernel].copy()
new_obj.YKernel=self.YKernel.copy()
new_obj.set_validation()
new_obj.penalty=base.cls_penalty()
new_obj.penalty.c=self.penalty.c
new_obj.penalty.d=self.penalty.d
new_obj.penalty.crossval=self.penalty.crossval
new_obj.glm_model=self.glm_model
new_obj.nrow=self.nrow
new_obj.ncol=self.ncol
new_obj.itestmode=self.itestmode
new_obj.kmode=self.kmode
new_obj.xbias=self.xbias
new_obj.ieval_type=self.ieval_type
new_obj.ibinary=self.ibinary
new_obj.categorymax=self.categorymax
new_obj.Y0=self.Y0
new_obj.rowcol=self.rowcol
def __init__(self, ninputview):
base.cls_data.__init__(self, ninputview)
self.XKernel = [None] * ninputview
self.YKernel = None
self.KX = None
self.KXCross = None
self.KY = None
self.d1x = None
self.d2x = None
self.d1x = None
self.penalty = base.cls_penalty(
) ## setting C,D penelty term paramters
self.penalty.set_crossval()
## perceptron
self.iperceptron = 0
self.perceptron = base.cls_perceptron_param()
## other classes
self.dual = None
## self.xbias=-0.6
self.xbias = 0.0
self.kmode = 1 ## =0 additive (feature concatenation)
## =1 multiplicative (feature tensor product)
self.ifixtrain = None
self.ifixtest = None
self.crossval_mode = 0 ## =0 random cross folds =1 fixtraining
## itestmode can be 2 if YKernel is linear !!!
self.itestmode = 0 ## 2 against the training with knn, 10 vectorwise
## 20 Y0 is available
## ##################################################
## !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
self.nrepeat = 10000
self.nfold = 5
## ##################################################
self.testknn = 5
self.ieval_type = 0
self.mdata = 0
def __init__(self,ninputview):
base.cls_data.__init__(self,ninputview)
self.XKernel=[None]*ninputview
self.YKernel=None
self.KX=None
self.KXCross=None
self.KY=None
self.d1x=None
self.d2x=None
self.d1x=None
self.penalty=base.cls_penalty() ## setting C,D penelty term paramters
self.penalty.set_crossval()
## perceptron
self.iperceptron=0
self.perceptron=base.cls_perceptron_param()
## other classes
self.dual=None
## self.xbias=-0.6
self.xbias=0.0
self.kmode=1 ## =0 additive (feature concatenation)
## =1 multiplicative (feature tensor product)
self.ifixtrain=None
self.ifixtest=None
self.crossval_mode=0 ## =0 random cross folds =1 fixtraining
## itestmode can be 2 if YKernel is linear !!!
self.itestmode=0 ## 2 against the training with knn, 10 vectorwise
## 20 Y0 is available
## ##################################################
## !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!11
self.nrepeat=5000
self.nfold=5
## ##################################################
self.testknn=5
self.ieval_type=0
self.mdata=0
def copy(self,new_obj,itrain):
nkernel=len(self.XKernel)
for ikernel in range(nkernel):
xdata=self.XKernel[ikernel].get_train(self.itrain)
new_obj.XKernel[ikernel]=self.XKernel[ikernel].copy(xdata)
xdata=self.YKernel.get_train(self.itrain)
new_obj.YKernel=self.YKernel.copy(xdata)
new_obj.set_validation()
new_obj.penalty=base.cls_penalty()
new_obj.penalty.c=self.penalty.c
new_obj.penalty.d=self.penalty.d
new_obj.penalty.crossval=self.penalty.crossval
new_obj.mdata=len(itrain)
new_obj.itestmode=self.itestmode
new_obj.testknn=self.testknn
new_obj.kmode=self.kmode
new_obj.xbias=self.xbias
def copy(self, new_obj, itrain):
nkernel = len(self.XKernel)
for ikernel in range(nkernel):
xdata = self.XKernel[ikernel].get_train(self.itrain)
new_obj.XKernel[ikernel] = self.XKernel[ikernel].copy(xdata)
xdata = self.YKernel.get_train(self.itrain)
new_obj.YKernel = self.YKernel.copy(xdata)
new_obj.set_validation()
new_obj.penalty = base.cls_penalty()
new_obj.penalty.c = self.penalty.c
new_obj.penalty.d = self.penalty.d
new_obj.penalty.crossval = self.penalty.crossval
new_obj.mdata = len(itrain)
new_obj.itestmode = self.itestmode
new_obj.testknn = self.testknn
new_obj.kmode = self.kmode
new_obj.xbias = self.xbias
def __init__(self, ninputview=1):
base.cls_data.__init__(self, ninputview)
self.XKernel = [None] * ninputview ## list of input kernel objects
self.YKernel = None ## ouput kernel object
self.KX = None ## compound input kernel
self.KY = None ## output kernel
## mvm specific
self.xdata_rel = None ## all data tuples (irow,icol,value)
self.xdata_tra = None ## training tuples
self.xdata_tes = None ## test tuples
self.xranges_rel = None ## training ranges, start position, and length
## of the known items in each row in
## the sparse representation
self.xranges_rel_test = None ## ranges for the test
self.KXvar = None
self.glm_model = None ## the parameters, means of the GLM model:
## total mean, row means, column means
self.largest_class = None ## row wise largest classes if values are class
## indexes
self.penalty = base.cls_penalty(
) ## setting C,D penelty term paramters
## other classes
self.dual = None ## the vector of the dual variables computed
## by the solver
self.xbias = 0 ## penalty term for projective bias, it can be =0
self.kmode = 0 ## =0 additive (feature concatenation)
## =1 multiplicative (fetaure tensor product)
self.ifixtrain = None ## xdata_rel relative indexes of the fix training
self.ifixtest = None ## xdata_rel relative indexes of the fix test
self.crossval_mode = 0 ## =0 random cross folds =1 fixtraining
self.itestmode = 3 ## 0 active learning 1,2 random subsets,
## 3 fix training test
self.ibootstrap = 2 ## =0 random =1 worst case =2 best case
## =3 alternate between worst case and random
self.nrepeat = 1 ## number of repetation of the folding
self.nfold = 2 ## number of folds
self.nrepeat0 = 1 ## number of effective repetation of the folding
self.nfold0 = 2 ## number of effective folds
self.ieval_type = 0 ## =0 category, =1 RMSE , =2 MAE
self.ibinary = 1 ## =1 Y0=[-1,+1], =0 [0,1,...,categorymax-1]
## mvm specific
self.category = 0 ## =0 rank cells =1 category cells =2 {-1,0,+1}^n
## =3 joint table on all categories
self.categorymax = 0
self.ndata = 0 ## all non-missing example in the relation table
self.ncol = 0 ## number of rows in the relation table
self.nrow = 0 ## number of column in the relation table
## test
self.verbose = 0
## row-column exchange
self.rowcol = 0 ## =0 row-col order =1 col-row order
## for n-fold cross validation
self.xselector = None ## a 1d array randomly loaded with
## the indexes of the folds to select the training
## and test in the cross-validation
## active learning pointers
self.icandidate_w = -1 ## the test relative index of
## the worst case in prediction by confidence
self.icandidate_b = -1 ## the test relative index of
## the best case in prediction by confidence
self.testontrain = 0 ## =0 test on test, =1 test on train
self.knowntest = 1 ## =0 test items are unknown, =1 known
self.confidence_scale = 2 ## scale paramter, e.g. standard deviation,
## of the distribution
## used in the confidence estimation
self.confidence_local = 0 ## localization paramter, e.g. mean,
def __init__(self,ninputview=1):
base.cls_data.__init__(self,ninputview)
self.XKernel=[ None ]*ninputview ## list of input kernel objects
self.YKernel=None ## ouput kernel object
self.KX=None ## compound input kernel
self.KY=None ## output kernel
## mvm specific
self.xdata_rel=None ## all data tuples (irow,icol,value)
self.xdata_tra=None ## training tuples
self.xdata_tes=None ## test tuples
self.xranges_rel=None ## training ranges, start position, and length
## of the known items in each row in
## the sparse representation
self.xranges_rel_test=None ## ranges for the test
self.KXvar=None
self.glm_model=None ## the parameters, means of the GLM model:
## total mean, row means, column means
self.largest_class=None ## row wise largest classes if values are class
## indexes
self.penalty=base.cls_penalty() ## setting C,D penelty term paramters
## other classes
self.dual=None ## the vector of the dual variables computed
## by the solver
self.xbias=0 ## penalty term for projective bias, it can be =0
self.kmode=0 ## =0 additive (feature concatenation)
## =1 multiplicative (fetaure tensor product)
self.ifixtrain=None ## xdata_rel relative indexes of the fix training
self.ifixtest=None ## xdata_rel relative indexes of the fix test
self.crossval_mode=0 ## =0 random cross folds =1 fixtraining
self.itestmode=3 ## 0 active learning 1,2 random subsets,
## 3 fix training test
self.ibootstrap=2 ## =0 random =1 worst case =2 best case
## =3 alternate between worst case and random
self.nrepeat=1 ## number of repetation of the folding
self.nfold=2 ## number of folds
self.nrepeat0=1 ## number of effective repetation of the folding
self.nfold0=2 ## number of effective folds
self.ieval_type=0 ## =0 category, =1 RMSE , =2 MAE
self.ibinary=1 ## =1 Y0=[-1,+1], =0 [0,1,...,categorymax-1]
## mvm specific
self.category=0 ## =0 rank cells =1 category cells =2 {-1,0,+1}^n
## =3 joint table on all categories
self.categorymax=0
self.ndata=0 ## all non-missing example in the relation table
self.ncol=0 ## number of rows in the relation table
self.nrow=0 ## number of column in the relation table
## test
self.verbose=0
## row-column exchange
self.rowcol=0 ## =0 row-col order =1 col-row order
## for n-fold cross validation
self.xselector=None ## a 1d array randomly loaded with
## the indexes of the folds to select the training
## and test in the cross-validation
## active learning pointers
self.icandidate_w=-1 ## the test relative index of
## the worst case in prediction by confidence
self.icandidate_b=-1 ## the test relative index of
## the best case in prediction by confidence
self.testontrain=0 ## =0 test on test, =1 test on train
self.knowntest=1 ## =0 test items are unknown, =1 known
self.confidence_scale=2 ## scale paramter, e.g. standard deviation,
## of the distribution
## used in the confidence estimation
self.confidence_local=0 ## localization paramter, e.g. mean,