def load_mmr(self,cData):
X_in=np.loadtxt(self.training_input)
X_out=np.loadtxt(self.training_output)
alpha=np.loadtxt(self.dualParams)
kernelParams=np.loadtxt(self.kernelParams)
(m,nx)=X_in.shape
ny=X_out.shape[1]
cData.training_center=np.mean(X_in,axis=0)
## loading the offline training parameters
cData.dual=base.cls_dual(alpha,np.zeros(ny)+0.1)
## add an empty test row to the trainig,
## it will be replaced with the real test inputs
X_in=[np.vstack((X_in,np.zeros((1,nx))))] ## list of views
X_out=np.vstack((X_out,np.zeros((1,ny))))
## subspace output kernel
cData.YKernel=mmr_kernel_explicit.cls_feature(ifeature=0)
cData.YKernel.load_data(X_out,ifeature=0)
cData.YKernel.ifeature=0
cData.YKernel.title='output'
## setting output parameters
cparams=cls_initial_params()
cData.YKernel.kernel_params.set(cparams.get_yparams('kernel',0))
cData.YKernel.crossval.set(cparams.get_yparams('cross',0))
cData.YKernel.norm.set(cparams.get_yparams('norm',0))
idata=0
nview=1
for iview in range(nview):
cData.XKernel[idata]=mmr_kernel_explicit.cls_feature(ifeature=0)
cData.XKernel[idata].load_data(X_in[iview],ifeature=0)
cData.XKernel[idata].title='input_'+str(iview)
## setting input parameters
cData.XKernel[idata].kernel_params.set(cparams. \
get_xparams('kernel',idata))
## kernel parameters
cData.XKernel[idata].kernel_params.ipar1=kernelParams[0]
cData.XKernel[idata].kernel_params.ipar2=kernelParams[1]
cData.XKernel[idata].crossval.set(cparams.get_xparams('cross',idata))
cData.XKernel[idata].norm.set(cparams.get_xparams('norm',idata))
idata+=1
cData.ninputview=idata ## set active views
cData.mdata=cData.YKernel.dataraw.shape[0]
cData.nfold=1
cData.nrepeat=1
cData.kmode=1 ## =0 additive (feature concatenation)
## =1 multiplicative (fetaure tensor product)
return
def load_mmr(self, cData, lviews):
dmat = scipy.io.loadmat(self.sbasedir + self.datafile)
ytrain = dmat['labels']
xtrain = dmat['his']
ytrain = ytrain[0, :]
(m, n) = xtrain.shape
nmax = ytrain.max()
nmin = ytrain.min()
ny = nmax - nmin + 1
ncut = 10000
if ncut < m:
isample = np.random.permutation(m)
isample = isample[:ncut]
m = ncut
else:
isample = np.arange(m)
ytrain = ytrain[isample]
xtrain = xtrain[isample]
X_in = [xtrain]
X_out = np.zeros((m, ny))
for i in range(m):
X_out[i, ytrain[i] - nmin] = 1
## subspace output kernel
cData.YKernel = mmr_kernel_explicit.cls_feature(ifeature=0)
cData.YKernel.load_data(X_out, ifeature=0)
cData.YKernel.ifeature = 0
cData.YKernel.title = 'output'
## setting output parameters
cparams = cls_initial_params()
cData.YKernel.kernel_params.set(cparams.get_yparams('kernel', 0))
## cData.YKernel.prekernel_params.set(cparams.get_yinparams('kernel',0))
cData.YKernel.crossval.set(cparams.get_yparams('cross', 0))
cData.YKernel.norm.set(cparams.get_yparams('norm', 0))
idata = 0
nview = 1
for iview in range(nview):
if iview in lviews:
cData.XKernel[idata] = mmr_kernel_explicit.cls_feature(
ifeature=0)
cData.XKernel[idata].load_data(X_in[iview], ifeature=0)
cData.XKernel[idata].title = 'input_' + str(iview)
## setting input parameters
cData.XKernel[idata].kernel_params.set(cparams. \
get_xparams('kernel',idata))
## cData.XKernel[idata].prekernel_params.set(cparams. \
## get_xinparams('kernel',idata))
cData.XKernel[idata].crossval.set(
cparams.get_xparams('cross', idata))
cData.XKernel[idata].norm.set(
cparams.get_xparams('norm', idata))
idata += 1
cData.ninputview = idata ## set active views
cData.mdata = cData.YKernel.dataraw.shape[0]
cData.nfold = 2
cData.nrepeat = 2
cData.kmode = 1 ## =0 additive (feature concatenation)
## =1 multiplicative (fetaure tensor product)
return
def load_data(self,cData,idata,lfeatures):
"""
cData refers to an mmr class object, created by mmr_mmr_cls
idata index of the chosen database from self.datadirs
lfeatures list of indexes of the selected features,
the 'annot' the labels always implicitly selected
the data files assumed to be matlab files
"""
## read the outputs, list_feature[0]=annot'
dmat=scipy.io.loadmat(self.basedir+self.datadirs[idata]+'/' \
+self.list_features[0]+'.mat')
ytrain=dmat['xtrain']
ytest=dmat['xtest']
print('ytrain',ytrain.shape)
print('ytest',ytest.shape)
ytrain=1*(ytrain>0) ## label indicators are 1 and 0
ytest=1*(ytest>0)
mtrain=ytrain.shape[0]
mtest=ytest.shape[0]
## select random subsample of training
if self.ncut<mtrain:
itrainsample=np.random.permutation(mtrain)
itrainsample=itrainsample[:self.ncut]
else:
itrainsample=np.arange(mtrain)
## select random subsample of test
if self.ncut<mtest:
itestsample=np.random.permutation(mtest)
itestsample=itestsample[:self.ncut]
else:
itestsample=np.arange(mtest)
## store the indexes of the subtraining and subtest in the MMR object
cData.ifixtrain=np.arange(len(itrainsample))
cData.ifixtest=np.arange(len(itestsample))
## we can concatenate now the training and test since ifixtrain
## and ifixtest will select them
ydata=np.vstack((ytrain[itrainsample],ytest[itestsample]))
## subspace output kernel
cData.YKernel=mmr_kernel_explicit.cls_feature(ifeature=0)
cData.YKernel.load_data(ydata,ifeature=0)
cData.YKernel.ifeature=0
cData.YKernel.title='output'
## setting output parameters
cparams=mmr_initial_params.cls_initial_params()
## kernel parameters
cData.YKernel.kernel_params.set(cparams.get_yparams('kernel',0))
## cross validation ranges
cData.YKernel.crossval.set(cparams.get_yparams('cross',0))
## normalization, localization and scaling type
cData.YKernel.norm.set(cparams.get_yparams('norm',0))
## process the input features
iview=0
for ifeature in lfeatures:
if ifeature==0: ## if annot(labels) included drop it!
continue
dmat=scipy.io.loadmat(self.basedir+self.datadirs[idata]+'/' \
+self.list_features[ifeature]+'.mat')
xtrain=dmat['xtrain']
xtest=dmat['xtest']
print('xtrain',xtrain.shape)
print('xtest',xtest.shape)
xdata=np.vstack((xtrain[itrainsample], \
xtest[itestsample])).astype(np.double)
print('>>>>>>>',self.datadirs[idata],'ifeature:', \
self.list_features[ifeature], \
len(itrainsample),len(itestsample))
cData.XKernel[iview]=mmr_kernel_explicit.cls_feature(ifeature=0)
cData.XKernel[iview].load_data(xdata,ifeature=0)
cData.XKernel[iview].title='input_'+str(ifeature)
## setting input kernel parameters
cData.XKernel[iview].kernel_params.set(cparams. \
get_xparams('kernel',iview))
## cross validation ranges
cData.XKernel[iview].crossval.set(cparams.get_xparams('cross',iview))
## normalization, localization and scaling type
cData.XKernel[iview].norm.set(cparams.get_xparams('norm',iview))
iview+=1
cData.ninputview=iview ## set the number of active views
cData.mdata=cData.YKernel.dataraw.shape[0]
return
def load_mmr(self,cData,lviews):
dmat=scipy.io.loadmat(self.sbasedir+self.datafile)
ytrain=dmat['labels']
xtrain=dmat['his']
ytrain=ytrain[0,:]
(m,n)=xtrain.shape
nmax=ytrain.max()
nmin=ytrain.min()
ny=nmax-nmin+1
ncut=10000
if ncut<m:
isample=np.random.permutation(m)
isample=isample[:ncut]
m=ncut
else:
isample=np.arange(m)
ytrain=ytrain[isample]
xtrain=xtrain[isample]
X_in=[xtrain]
X_out=np.zeros((m,ny))
for i in range(m):
X_out[i,ytrain[i]-nmin]=1
## subspace output kernel
cData.YKernel=mmr_kernel_explicit.cls_feature(ifeature=0)
cData.YKernel.load_data(X_out,ifeature=0)
cData.YKernel.ifeature=0
cData.YKernel.title='output'
## setting output parameters
cparams=cls_initial_params()
cData.YKernel.kernel_params.set(cparams.get_yparams('kernel',0))
## cData.YKernel.prekernel_params.set(cparams.get_yinparams('kernel',0))
cData.YKernel.crossval.set(cparams.get_yparams('cross',0))
cData.YKernel.norm.set(cparams.get_yparams('norm',0))
idata=0
nview=1
for iview in range(nview):
if iview in lviews:
cData.XKernel[idata]=mmr_kernel_explicit.cls_feature(ifeature=0)
cData.XKernel[idata].load_data(X_in[iview],ifeature=0)
cData.XKernel[idata].title='input_'+str(iview)
## setting input parameters
cData.XKernel[idata].kernel_params.set(cparams. \
get_xparams('kernel',idata))
## cData.XKernel[idata].prekernel_params.set(cparams. \
## get_xinparams('kernel',idata))
cData.XKernel[idata].crossval.set(cparams.get_xparams('cross',idata))
cData.XKernel[idata].norm.set(cparams.get_xparams('norm',idata))
idata+=1
cData.ninputview=idata ## set active views
cData.mdata=cData.YKernel.dataraw.shape[0]
cData.nfold=2
cData.nrepeat=2
cData.kmode=1 ## =0 additive (feature concatenation)
## =1 multiplicative (fetaure tensor product)
return
def load_mmr(self, cData, lviews):
ldata_labels = self.read_raw_txt(self.sbasedir, self.labelfile)
if self.loadFurther == 1:
ldata_labels.append([self.evaldir, 1])
mdata = len(ldata_labels)
## !!!!!!!!!!!!!!!!!!!!!!!!!!
## fixed number of categories od sides
ncategory = 4
## ncategory=3
category_recode = [0, 1, 2, 3]
## ####################################
X_out = np.zeros((mdata, ncategory))
for i in range(mdata):
## X_out[i,int(ldata_labels[i][1])-1]=1
icategory = int(ldata_labels[i][1]) - 1
if icategory == 0:
X_out[i, category_recode[icategory]] = 1
else:
if ncategory == 3:
X_out[i, category_recode[icategory]] = 1
else:
X_out[i, category_recode[icategory]] = 1
## number of views: joint force, cart force, cart torque
nview = len(self.linputmask)
## find common minimum length of trajectories
xlength = np.zeros(mdata)
for i in range(mdata):
X = np.loadtxt(ldata_labels[i][0], skiprows=1)
xlength[i] = X.shape[0]
ixin_type = 0 ## =0 simple vectorized =1 angles
ixin_decompose = 0 ## =0 no, =1 tensor decomposition
icumulant = 0 ## =0 no cumulants, =1 cumulants are the feature components
nmin = xlength.min()
## !!!!!!!!!!!!!!!!!!!!!!
if nmin > 240:
if ixin_decompose == 1:
nmin = 240 ## 8*6*5
tfactors = (30, 8, 1, 1)
nfactor = len(tfactors)
nlayer = 1
ncumulant = 5
if ncumulant > tfactors[0]:
ncumulant = tfactors[0]
if icumulant == 0:
nitem = tfactors[0]
else:
nitem = ncumulant
if ixin_type == 0:
nboot = 1
if ixin_decompose == 0:
X_in = []
for i in range(nview):
nslice = self.linputmask[i][1] - self.linputmask[i][0]
X_in.append(np.zeros((mdata, nboot * nmin * nslice)))
elif ixin_decompose == 1:
X_in = []
for i in range(nview):
nslice = self.linputmask[i][1] - self.linputmask[i][0]
X_in.append(np.zeros((mdata, nlayer * nitem * nslice)))
for i in range(mdata):
## if i==18:
## print(i)
X = np.loadtxt(ldata_labels[i][0], skiprows=1)
X = X[:nmin, :]
if ixin_decompose == 0:
for j in range(nview):
i0 = self.linputmask[j][0]
ik = self.linputmask[j][1]
## fX=X[:,i0:ik].ravel()
## fX=np.sort(fX)
## X_in[j][i]=fX
X_in[j][i] = X[:, i0:ik].ravel()
elif ixin_decompose == 1:
for j in range(nview):
i0 = self.linputmask[j][0]
ik = self.linputmask[j][1]
xmatrix = X[:, i0:ik]
## xamtrix=spsignal.savgol_filter(xmatrix,7,5,deriv=0,axis=0, \
## mode='interp')
## xfactors assume 240 rows
xfactors=np.array([[tfactors[0],ik-i0],[tfactors[1],1], \
[tfactors[2],1],[tfactors[3],1]])
ctensor = tensor_decomp.cls_tensor_decomp()
ctensor.load(xmatrix, xfactors)
niter = 10
ctensor.decompose2(niter)
nslice = self.linputmask[j][1] - self.linputmask[j][0]
fX = np.zeros(nlayer * nitem * nslice)
ipos = 0
for ilayer in range(nlayer):
for ifactor in range(1):
zfact = ctensor.lcomponents[ilayer][ifactor]
(mzfact, nzfact) = zfact.shape
for icol in range(nzfact):
if np.mean(zfact[:, icol]) < 0:
zfact[:, icol] = -zfact[:, icol]
if icumulant == 1:
for icol in range(nzfact):
xcumulants = cumulants(zfact[:, icol],
ncumulant,
icentral=1)
fX[ipos:ipos + nitem] = xcumulants
ipos += nitem
else:
## (u,s,v)=np.linalg.svd(zfact)
## zfact=np.dot(u[:,:nzfact],np.diag(s[:nzfact]))
for icol in range(nzfact):
fX[ipos:ipos + mzfact] = zfact[:, icol]
ipos += mzfact
## fX=np.sort(fX)
X_in[j][i] = fX
elif ixin_type == 1:
X_in = [np.zeros((mdata, nmin - 1)) for i in range(nview)]
for i in range(mdata):
X = np.loadtxt(self.sbasedir + ldata_labels[i][0], skiprows=1)
X = X[:nmin, :]
for j in range(nview):
i0 = self.linputmask[j][0]
ik = self.linputmask[j][1]
X_in[j][i] = angle_moment(X[:, i0:ik])
## subspace output kernel
cData.YKernel = mmr_kernel_explicit.cls_feature(ifeature=0)
cData.YKernel.load_data(X_out, ifeature=0)
cData.YKernel.ifeature = 0
cData.YKernel.title = 'output'
## setting output parameters
cparams = cls_initial_params()
cData.YKernel.kernel_params.set(cparams.get_yparams('kernel', 0))
## cData.YKernel.prekernel_params.set(cparams.get_yinparams('kernel',0))
cData.YKernel.crossval.set(cparams.get_yparams('cross', 0))
cData.YKernel.norm.set(cparams.get_yparams('norm', 0))
idata = 0
for iview in range(nview):
if iview in lviews:
cData.XKernel[idata] = mmr_kernel_explicit.cls_feature(
ifeature=0)
cData.XKernel[idata].load_data(X_in[iview], ifeature=0)
cData.XKernel[idata].title = 'input_' + str(iview)
## setting input parameters
cData.XKernel[idata].kernel_params.set(cparams. \
get_xparams('kernel',idata))
## cData.XKernel[idata].prekernel_params.set(cparams. \
## get_xinparams('kernel',idata))
cData.XKernel[idata].crossval.set(
cparams.get_xparams('cross', idata))
cData.XKernel[idata].norm.set(
cparams.get_xparams('norm', idata))
idata += 1
cData.ninputview = idata ## set active views
cData.mdata = cData.YKernel.dataraw.shape[0]
cData.nfold = 2
cData.nrepeat = 2
cData.kmode = 1 ## =0 additive (feature concatenation)
## =1 multiplicative (fetaure tensor product)
return
def load_mmr(self, cData, lviews):
ldata_labels = self.read_raw_txt(self.sbasedir,self.labelfile)
if self.loadFurther==1:
ldata_labels.append([self.evaldir, 1])
mdata=len(ldata_labels)
## !!!!!!!!!!!!!!!!!!!!!!!!!!
## fixed number of categories od sides
ncategory=4
## ncategory=3
category_recode=[0,1,2,3]
## ####################################
X_out=np.zeros((mdata,ncategory))
for i in range(mdata):
## X_out[i,int(ldata_labels[i][1])-1]=1
icategory=int(ldata_labels[i][1])-1
if icategory==0:
X_out[i,category_recode[icategory]]=1
else:
if ncategory==3:
X_out[i,category_recode[icategory]]=1
else:
X_out[i,category_recode[icategory]]=1
## number of views: joint force, cart force, cart torque
nview=len(self.linputmask)
## find common minimum length of trajectories
xlength=np.zeros(mdata)
for i in range(mdata):
X=np.loadtxt(ldata_labels[i][0],skiprows=1)
xlength[i]=X.shape[0]
ixin_type=0 ## =0 simple vectorized =1 angles
ixin_decompose=0 ## =0 no, =1 tensor decomposition
icumulant=0 ## =0 no cumulants, =1 cumulants are the feature components
nmin=xlength.min()
## !!!!!!!!!!!!!!!!!!!!!!
if nmin>240:
if ixin_decompose==1:
nmin=240 ## 8*6*5
tfactors=(30,8,1,1)
nfactor=len(tfactors)
nlayer=1
ncumulant=5
if ncumulant>tfactors[0]:
ncumulant=tfactors[0]
if icumulant==0:
nitem=tfactors[0]
else:
nitem=ncumulant
if ixin_type==0:
nboot=1
if ixin_decompose==0:
X_in=[]
for i in range(nview):
nslice=self.linputmask[i][1]-self.linputmask[i][0]
X_in.append(np.zeros((mdata,nboot*nmin*nslice)))
elif ixin_decompose==1:
X_in=[]
for i in range(nview):
nslice=self.linputmask[i][1]-self.linputmask[i][0]
X_in.append(np.zeros((mdata,nlayer*nitem*nslice)))
for i in range(mdata):
## if i==18:
## print(i)
X=np.loadtxt(ldata_labels[i][0],skiprows=1)
X=X[:nmin,:]
if ixin_decompose==0:
for j in range(nview):
i0=self.linputmask[j][0]
ik=self.linputmask[j][1]
## fX=X[:,i0:ik].ravel()
## fX=np.sort(fX)
## X_in[j][i]=fX
X_in[j][i]=X[:,i0:ik].ravel()
elif ixin_decompose==1:
for j in range(nview):
i0=self.linputmask[j][0]
ik=self.linputmask[j][1]
xmatrix=X[:,i0:ik]
## xamtrix=spsignal.savgol_filter(xmatrix,7,5,deriv=0,axis=0, \
## mode='interp')
## xfactors assume 240 rows
xfactors=np.array([[tfactors[0],ik-i0],[tfactors[1],1], \
[tfactors[2],1],[tfactors[3],1]])
ctensor=tensor_decomp.cls_tensor_decomp()
ctensor.load(xmatrix,xfactors)
niter=10
ctensor.decompose2(niter)
nslice=self.linputmask[j][1]-self.linputmask[j][0]
fX=np.zeros(nlayer*nitem*nslice)
ipos=0
for ilayer in range(nlayer):
for ifactor in range(1):
zfact=ctensor.lcomponents[ilayer][ifactor]
(mzfact,nzfact)=zfact.shape
for icol in range(nzfact):
if np.mean(zfact[:,icol])<0:
zfact[:,icol]=-zfact[:,icol]
if icumulant==1:
for icol in range(nzfact):
xcumulants=cumulants(zfact[:,icol],ncumulant,icentral=1)
fX[ipos:ipos+nitem]=xcumulants
ipos+=nitem
else:
## (u,s,v)=np.linalg.svd(zfact)
## zfact=np.dot(u[:,:nzfact],np.diag(s[:nzfact]))
for icol in range(nzfact):
fX[ipos:ipos+mzfact]=zfact[:,icol]
ipos+=mzfact
## fX=np.sort(fX)
X_in[j][i]=fX
elif ixin_type==1:
X_in=[ np.zeros((mdata,nmin-1)) for i in range(nview)]
for i in range(mdata):
X=np.loadtxt(self.sbasedir+ldata_labels[i][0],skiprows=1)
X=X[:nmin,:]
for j in range(nview):
i0=self.linputmask[j][0]
ik=self.linputmask[j][1]
X_in[j][i]=angle_moment(X[:,i0:ik])
## subspace output kernel
cData.YKernel=mmr_kernel_explicit.cls_feature(ifeature=0)
cData.YKernel.load_data(X_out,ifeature=0)
cData.YKernel.ifeature=0
cData.YKernel.title='output'
## setting output parameters
cparams=cls_initial_params()
cData.YKernel.kernel_params.set(cparams.get_yparams('kernel',0))
## cData.YKernel.prekernel_params.set(cparams.get_yinparams('kernel',0))
cData.YKernel.crossval.set(cparams.get_yparams('cross',0))
cData.YKernel.norm.set(cparams.get_yparams('norm',0))
idata=0
for iview in range(nview):
if iview in lviews:
cData.XKernel[idata]=mmr_kernel_explicit.cls_feature(ifeature=0)
cData.XKernel[idata].load_data(X_in[iview],ifeature=0)
cData.XKernel[idata].title='input_'+str(iview)
## setting input parameters
cData.XKernel[idata].kernel_params.set(cparams. \
get_xparams('kernel',idata))
## cData.XKernel[idata].prekernel_params.set(cparams. \
## get_xinparams('kernel',idata))
cData.XKernel[idata].crossval.set(cparams.get_xparams('cross',idata))
cData.XKernel[idata].norm.set(cparams.get_xparams('norm',idata))
idata+=1
cData.ninputview=idata ## set active views
cData.mdata=cData.YKernel.dataraw.shape[0]
cData.nfold=2
cData.nrepeat=2
cData.kmode=1 ## =0 additive (feature concatenation)
## =1 multiplicative (fetaure tensor product)
return
def load_data(self, cData, idata, lfeatures):
"""
cData refers to an mmr class object, created by mmr_mmr_cls
idata index of the chosen database from self.datadirs
lfeatures list of indexes of the selected features,
the 'annot' the labels always implicitly selected
the data files assumed to be matlab files
"""
## read the outputs, list_feature[0]=annot'
dmat=scipy.io.loadmat(self.basedir+self.datadirs[idata]+'/' \
+self.list_features[0]+'.mat')
ytrain = dmat['xtrain']
ytest = dmat['xtest']
print('ytrain', ytrain.shape)
print('ytest', ytest.shape)
ytrain = 1 * (ytrain > 0) ## label indicators are 1 and 0
ytest = 1 * (ytest > 0)
mtrain = ytrain.shape[0]
mtest = ytest.shape[0]
## select random subsample of training
if self.ncut < mtrain:
itrainsample = np.random.permutation(mtrain)
itrainsample = itrainsample[:self.ncut]
else:
itrainsample = np.arange(mtrain)
## select random subsample of test
if self.ncut < mtest:
itestsample = np.random.permutation(mtest)
itestsample = itestsample[:self.ncut]
else:
itestsample = np.arange(mtest)
## store the indexes of the subtraining and subtest in the MMR object
cData.ifixtrain = np.arange(len(itrainsample))
cData.ifixtest = np.arange(len(itestsample))
## we can concatenate now the training and test since ifixtrain
## and ifixtest will select them
ydata = np.vstack((ytrain[itrainsample], ytest[itestsample]))
## subspace output kernel
cData.YKernel = mmr_kernel_explicit.cls_feature(ifeature=0)
cData.YKernel.load_data(ydata, ifeature=0)
cData.YKernel.ifeature = 0
cData.YKernel.title = 'output'
## setting output parameters
cparams = mmr_initial_params.cls_initial_params()
## kernel parameters
cData.YKernel.kernel_params.set(cparams.get_yparams('kernel', 0))
## cross validation ranges
cData.YKernel.crossval.set(cparams.get_yparams('cross', 0))
## normalization, localization and scaling type
cData.YKernel.norm.set(cparams.get_yparams('norm', 0))
## process the input features
iview = 0
for ifeature in lfeatures:
if ifeature == 0: ## if annot(labels) included drop it!
continue
dmat=scipy.io.loadmat(self.basedir+self.datadirs[idata]+'/' \
+self.list_features[ifeature]+'.mat')
xtrain = dmat['xtrain']
xtest = dmat['xtest']
print('xtrain', xtrain.shape)
print('xtest', xtest.shape)
xdata=np.vstack((xtrain[itrainsample], \
xtest[itestsample])).astype(np.double)
print('>>>>>>>',self.datadirs[idata],'ifeature:', \
self.list_features[ifeature], \
len(itrainsample),len(itestsample))
cData.XKernel[iview] = mmr_kernel_explicit.cls_feature(ifeature=0)
cData.XKernel[iview].load_data(xdata, ifeature=0)
cData.XKernel[iview].title = 'input_' + str(ifeature)
## setting input kernel parameters
cData.XKernel[iview].kernel_params.set(cparams. \
get_xparams('kernel',iview))
## cross validation ranges
cData.XKernel[iview].crossval.set(
cparams.get_xparams('cross', iview))
## normalization, localization and scaling type
cData.XKernel[iview].norm.set(cparams.get_xparams('norm', iview))
iview += 1
cData.ninputview = iview ## set the number of active views
cData.mdata = cData.YKernel.dataraw.shape[0]
return