def mmr_train(self):
if self.iperceptron == 0:
mtra = self.mtrain
KX=mmr_kernel(self,self.itrain,self.itrain,ioutput=0, \
itraintest=0,itraindata=0,itensor=self.kmode)[0]
KY=mmr_kernel(self,self.itrain,self.itrain,ioutput=1, \
itraintest=0,itraindata=0)[0]
cOptDual = base.cls_dual(None, None)
self.dual = cOptDual
self.csolver = mmr_solver_cls.cls_mmr_solver()
self.dual.alpha=self.csolver.mmr_solver(KX,KY,self.penalty.c, \
self.penalty.d)
## estimate the bias for linear output kernel
if self.YKernel.ifeature == 0:
YTrain = self.YKernel.get_train_norm(self.itrain)
ZW=np.dot(YTrain.T, \
(np.outer(self.dual.alpha,np.ones(mtra))*KX))
xbias = np.median(YTrain - ZW.T, axis=0)
else:
KY = self.YKernel.Kcross
ZW = np.dot(KY.T,
(np.outer(self.dual.alpha, np.ones(mtra)) * KX))
xbias = np.zeros(mtra)
self.dual.bias = xbias
elif self.iperceptron == 1:
cOptDual = base.cls_dual(None, None)
XTrainNorm0 = self.XKernel[0].get_train_norm()
YTrainNorm = self.YKernel.get_train_norm(self.itrain)
cOptDual.W=mmr_perceptron_primal(XTrainNorm0,YTrainNorm, \
self.perceptron.margin, \
self.perceptron.stepsize, \
self.perceptron.nrepeat)
elif self.iperceptron == 2:
cOptDual = base.cls_dual(None, None)
XTrainNorm0 = self.XKernel[0].get_train_norm()
YTrainNorm = self.YKernel.get_train_norm(self.itrain)
cOptDual.alpha=mmr_perceptron_dual(XTrainNorm0,YTrainNorm, \
self.XKernel[0].kernel_params.ipar1, \
self.XKernel[0].kernel_params.ipar2, \
self.XKernel[0].kernel_type, \
self.perceptron.margin, \
self.perceptron.stepsize, \
self.perceptron.nrepeat)
return (cOptDual)
def mmr_train(self):
if self.iperceptron==0:
mtra=self.mtrain
KX=mmr_kernel(self,self.itrain,self.itrain,ioutput=0, \
itraintest=0,itraindata=0,itensor=self.kmode)[0]
KY=mmr_kernel(self,self.itrain,self.itrain,ioutput=1, \
itraintest=0,itraindata=0)[0]
cOptDual=base.cls_dual(None,None)
self.dual=cOptDual
self.csolver=mmr_solver_cls.cls_mmr_solver()
self.dual.alpha=self.csolver.mmr_solver(KX,KY,self.penalty.c, \
self.penalty.d)
## estimate the bias for linear output kernel
if self.YKernel.ifeature==0:
YTrain=self.YKernel.get_train_norm(self.itrain)
ZW=np.dot(YTrain.T, \
(np.outer(self.dual.alpha,np.ones(mtra))*KX))
xbias=np.median(YTrain-ZW.T,axis=0)
else:
KY=self.YKernel.Kcross
ZW=np.dot(KY.T,(np.outer(self.dual.alpha,np.ones(mtra))*KX))
xbias=np.zeros(mtra)
self.dual.bias=xbias
elif self.iperceptron==1:
cOptDual=base.cls_dual(None,None)
XTrainNorm0=self.XKernel[0].get_train_norm()
YTrainNorm=self.YKernel.get_train_norm(self.itrain)
cOptDual.W=mmr_perceptron_primal(XTrainNorm0,YTrainNorm, \
self.perceptron.margin, \
self.perceptron.stepsize, \
self.perceptron.nrepeat)
elif self.iperceptron==2:
cOptDual=base.cls_dual(None,None)
XTrainNorm0=self.XKernel[0].get_train_norm()
YTrainNorm=self.YKernel.get_train_norm(self.itrain)
cOptDual.alpha=mmr_perceptron_dual(XTrainNorm0,YTrainNorm, \
self.XKernel[0].kernel_params.ipar1, \
self.XKernel[0].kernel_params.ipar2, \
self.XKernel[0].kernel_type, \
self.perceptron.margin, \
self.perceptron.stepsize, \
self.perceptron.nrepeat)
return(cOptDual)
def edge_prekernel(self, X, params_spec):
(m, n) = X.shape ## xdataraw[0]
dalpha = {}
dxy = {}
ialpha = 1
ny = 2
nitem = 3
self.ddiag = np.zeros(m)
csolver = mmr_solver_cls.cls_mmr_solver()
## read the edges
for iview in range(m):
xy = X[iview]
nxy = len(xy) // nitem
xy = xy[:nxy * nitem]
xy = xy.reshape((nxy, nitem))
ixy = np.where(xy[:, 0] > 0)[0]
xy = xy[ixy]
x = xy[:, :2] ## edge position
ya = xy[:, 2] ## edge angle
y = np.vstack((np.cos(ya), np.sin(ya))).T
## normalize the locations
ilocal = self.norm.ilocal
iscale = self.norm.iscale
(xnorm, xdummy, opar) = mmr_normalization(ilocal, iscale, x, None,
0)
## xnorm=x
self.dxy[iview] = [xnorm, y]
if ialpha == 1:
Ky = np.dot(y, y.T)
xdata = [xnorm, None]
(Kx, dx1, dx2) = kernel_eval_kernel(xdata, None, None,
params_spec)
C = 1
D = 0
xalpha = csolver.mmr_solver(Kx, Ky, C, D, 1, 1, 1, 1)
self.dalpha[iview] = xalpha
else:
xalpha = np.ones(len(y)) / len(y)
self.dalpha[iview] = xalpha
self.ddiag[iview] = np.dot(xalpha, np.dot(Kx * Ky, xalpha))
return
def edge_prekernel(self,X,params_spec):
(m,n)=X.shape ## xdataraw[0]
dalpha={}
dxy={}
ialpha=1
ny=2
nitem=3
self.ddiag=np.zeros(m)
csolver=mmr_solver_cls.cls_mmr_solver()
## read the edges
for iview in range(m):
xy=X[iview]
nxy=len(xy)//nitem
xy=xy[:nxy*nitem]
xy=xy.reshape((nxy,nitem))
ixy=np.where(xy[:,0]>0)[0]
xy=xy[ixy]
x=xy[:,:2] ## edge position
ya=xy[:,2] ## edge angle
y=np.vstack((np.cos(ya),np.sin(ya))).T
## normalize the locations
ilocal=self.norm.ilocal
iscale=self.norm.iscale
(xnorm,xdummy,opar)=mmr_normalization(ilocal,iscale,x,None,0)
## xnorm=x
self.dxy[iview]=[xnorm,y]
if ialpha==1:
Ky=np.dot(y,y.T)
xdata=[xnorm,None]
(Kx,dx1,dx2)=kernel_eval_kernel(xdata,None,None,params_spec)
C=1
D=0
xalpha=csolver.mmr_solver(Kx,Ky,C,D,1,1,1,1)
self.dalpha[iview]=xalpha
else:
xalpha=np.ones(len(y))/len(y)
self.dalpha[iview]=xalpha
self.ddiag[iview]=np.dot(xalpha,np.dot(Kx*Ky,xalpha))
return
def loadData(trainOutput, trainInput, dualParams, kernelParams):
global cMMR
params = mmr_setparams.cls_params()
nview = 1
params.ninputview = nview
cMMR = mmr_mmr_cls.cls_mmr(params.ninputview)
nfold = cMMR.nfold
nrepeat = cMMR.nrepeat
## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
cdata_store=armar_load_data.cls_label_files(trainOutput,trainInput, \
dualParams,kernelParams)
cdata_store.load_mmr(cMMR)
mdata = cMMR.mdata
## ############################################################
xselector = np.ones(mdata)
xselector[-1] = 0
ifold = 0
cMMR.split_train_test(xselector, ifold)
cMMR.compute_kernels()
## cMMR.Y0=cMMR.YKernel.get_train(cMMR.itrain) ## candidates
cMMR.csolver = mmr_solver_cls.cls_mmr_solver()
def loadData(trainOutput,trainInput,dualParams,kernelParams):
global cMMR
params=mmr_setparams.cls_params()
nview=1
params.ninputview=nview
cMMR=mmr_mmr_cls.cls_mmr(params.ninputview)
nfold=cMMR.nfold
nrepeat=cMMR.nrepeat
## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
cdata_store=armar_load_data.cls_label_files(trainOutput,trainInput, \
dualParams,kernelParams)
cdata_store.load_mmr(cMMR)
mdata=cMMR.mdata
## ############################################################
xselector=np.ones(mdata)
xselector[-1]=0
ifold=0
cMMR.split_train_test(xselector,ifold)
cMMR.compute_kernels()
## cMMR.Y0=cMMR.YKernel.get_train(cMMR.itrain) ## candidates
cMMR.csolver=mmr_solver_cls.cls_mmr_solver()
