def mvm_form_prod(self,X,pselect=1.0):
xdata=[[],[],[]] ## row index, column index , value
(m,n)=X.shape
X=X.reshape((1*m,n/1))
## X=np.dot(X,X.T)
# n=m
k=0
icount=0
for i in range(m):
px=np.random.rand(m)
for j in range(m):
if px[j]<pselect:
v=np.dot(X[i],X[j])
ij=np.ravel_multi_index(([i],[j]),(m,m))[0]
tij=np.unravel_index([ij],(m*1,m/1))
xdata[0].append(tij[0][0])
xdata[1].append(tij[1][0])
xdata[2].append(v)
k+=1
icount+=1
xdata=mvm_prepare.sort_table(xdata,ifloat=1)
return(xdata)
def mvm_form_prod_reorder(self,X,xfactors):
xdata=[[],[],[]] ## row index, column index , value
(m,n)=X.shape
X2=np.dot(X,X.T)
if m==400:
print(m,n)
ctensor=tensor_decomp.cls_tensor_decomp()
ctensor.load(X2,xfactors)
X2=ctensor.reorder_in_x(X2)
(m2,n2)=X2.shape
xmean=np.mean(X2)
xstd=np.std(X2)
lbound=xmean-5*xstd
ubound=xmean+5*xstd
k=0
icount=0
for i in range(m2):
for j in range(n2):
v=X2[i,j]
if v>=lbound and v<=ubound:
xdata[0].append(i)
xdata[1].append(j)
xdata[2].append(v)
k+=1
icount+=1
xdata=mvm_prepare.sort_table(xdata,ifloat=1)
return(xdata,m2,n2)
def load(self,ifile,pselect,itrain=1):
xdata=[[],[],[]] ## row index, column index , value
ddata=scipy.io.loadmat(self.sbasedir+self.ldirs[ifile]+'/'+self.slabels)
if itrain==1:
xtrain=ddata['xtrain']
else:
xtrain=ddata['xtest']
m=xtrain.shape[0]
## antimatroid(xtrain[:5000])
iselect=np.where(np.random.rand(m)<pselect)[0]
xtrain0=xtrain[iselect]
m=xtrain0.shape[0];
X=np.dot(xtrain0,xtrain.T)
for i in range(m):
for j in range(m):
v=X[i,j]
if v!=0:
xdata[0].append(i)
xdata[1].append(j)
xdata[2].append(v)
xdata=mvm_prepare.sort_table(xdata,ifloat=1)
return(xdata,m,m)
def load_objobj_act(self, lfiles, ifeature):
"""
load collected known entities (object, action,feature)
into the relation table.
"""
## first phase collect object labels
for ifile in lfiles:
self.collect_entities(ifile, ifeature)
## second phase load the relation table of known examples
xdata = [[], [], []] ## row index, column index , value
idata = 0
ifixtrain = []
ifixtest = []
for iobject1 in range(self.nobject):
for iaction in self.ddata[iobject1].keys():
for iobject2 in range(self.nobject):
if iaction in self.ddata[iobject2]:
lvalue1 = self.ddata[iobject1][iaction]
lvalue2 = self.ddata[iobject2][iaction]
if len(lvalue1) > 0 and len(lvalue2) > 0:
vvalue=np.hstack((np.array(list(lvalue1)), \
np.array(list(lvalue2))))
## if len(vvalue)>1:
## print(vvalue)
vvalue = np.mean(vvalue.astype(np.double))
if self.irowcol == 0:
xdata[0].append(iobject1 * self.nobject +
iobject2)
xdata[1].append(iaction)
xdata[2].append(vvalue)
elif self.irowcol == 1:
xdata[0].append(iaction)
xdata[1].append(iobject1 * self.nobject +
iobject2)
xdata[2].append(vvalue)
ifixtrain.append(idata)
idata += 1
if self.irowcol == 0:
self.nrow = self.nobject**2
self.ncol = self.naction
elif self.irowcol == 1:
self.nrow = self.naction
self.ncol = self.nobject**2
xdata = mvm_prepare.sort_table(xdata, ifloat=1)
ifixtrain = np.array(ifixtrain)
ifixtest = ifixtrain
return (xdata, self.nrow, self.ncol, ifixtrain, ifixtest)
def load_objobj_act(self,lfiles,ifeature):
"""
load collected known entities (object, action,feature)
into the relation table.
"""
## first phase collect object labels
for ifile in lfiles:
self.collect_entities(ifile,ifeature)
## second phase load the relation table of known examples
xdata=[[],[],[]] ## row index, column index , value
idata=0
ifixtrain=[]
ifixtest=[]
for iobject1 in range(self.nobject):
for iaction in self.ddata[iobject1].keys():
for iobject2 in range(self.nobject):
if iaction in self.ddata[iobject2]:
lvalue1=self.ddata[iobject1][iaction]
lvalue2=self.ddata[iobject2][iaction]
if len(lvalue1)>0 and len(lvalue2)>0:
vvalue=np.hstack((np.array(list(lvalue1)), \
np.array(list(lvalue2))))
## if len(vvalue)>1:
## print(vvalue)
vvalue=np.mean(vvalue.astype(np.double))
if self.irowcol==0:
xdata[0].append(iobject1*self.nobject+iobject2)
xdata[1].append(iaction)
xdata[2].append(vvalue)
elif self.irowcol==1:
xdata[0].append(iaction)
xdata[1].append(iobject1*self.nobject+iobject2)
xdata[2].append(vvalue)
ifixtrain.append(idata)
idata+=1
if self.irowcol==0:
self.nrow=self.nobject**2
self.ncol=self.naction
elif self.irowcol==1:
self.nrow=self.naction
self.ncol=self.nobject**2
xdata=mvm_prepare.sort_table(xdata,ifloat=1)
ifixtrain=np.array(ifixtrain)
ifixtest=ifixtrain
return(xdata,self.nrow,self.ncol,ifixtrain,ifixtest)
def load_onefile(self, iknown, ifile):
"""
- load collected known entities (object, action,feature)
into the relation table.
- one file is loaded and cut into training and test
- it is to find the best parameters for the general case
"""
## first phase collect object labels
if iknown == 0:
sfile = self.listfull[ifile]
else:
sfile = self.listknown[ifile]
self.collect_objects(sfile)
self.collect_relations(sfile, self.ddata_tra)
print('<<<' + sfile)
## second phase load the relation table of known examples
xdata = [[], [], []] ## row index, column index , value
for iobject1, dobject2 in self.ddata_tra.items():
for iobject2, dvalues in dobject2.items():
for ifeature in range(self.nfeature):
if ifeature in dvalues:
svalue = dvalues[ifeature]
else:
svalue = ''
if len(
svalue
) > 0: ## if value is not given it is assumed as missing
if self.irowcol == 0:
xdata[0].append(iobject1)
xdata[1].append(ifeature * self.nobject + iobject2)
xdata[2].append(float(svalue))
elif self.irowcol == 1:
xdata[0].append(ifeature * self.nobject + iobject1)
xdata[1].append(iobject2)
xdata[2].append(float(svalue))
if self.irowcol == 0:
self.nrow = self.nobject
self.ncol = self.nobject * self.nfeature
elif self.irowcol == 1:
self.nrow = self.nobject * self.nfeature
self.ncol = self.nobject
xdata = mvm_prepare.sort_table(xdata, ifloat=1)
return (xdata, self.nrow, self.ncol)
def load_onefile(self,iknown,ifile):
"""
- load collected known entities (object, action,feature)
into the relation table.
- one file is loaded and cut into training and test
- it is to find the best parameters for the general case
"""
## first phase collect object labels
if iknown==0:
sfile=self.listfull[ifile]
else:
sfile=self.listknown[ifile]
self.collect_objects(sfile)
self.collect_relations(sfile,self.ddata_tra)
print('<<<'+sfile)
## second phase load the relation table of known examples
xdata=[[],[],[]] ## row index, column index , value
for iobject1,dobject2 in self.ddata_tra.items():
for iobject2,dvalues in dobject2.items():
for ifeature in range(self.nfeature):
if ifeature in dvalues:
svalue=dvalues[ifeature]
else:
svalue=''
if len(svalue)>0: ## if value is not given it is assumed as missing
if self.irowcol==0:
xdata[0].append(iobject1)
xdata[1].append(ifeature*self.nobject+iobject2)
xdata[2].append(float(svalue))
elif self.irowcol==1:
xdata[0].append(ifeature*self.nobject+iobject1)
xdata[1].append(iobject2)
xdata[2].append(float(svalue))
if self.irowcol==0:
self.nrow=self.nobject
self.ncol=self.nobject*self.nfeature
elif self.irowcol==1:
self.nrow=self.nobject*self.nfeature
self.ncol=self.nobject
xdata=mvm_prepare.sort_table(xdata,ifloat=1)
return(xdata,self.nrow,self.ncol)
def mvm_form(self,pselect=1.0):
xdata=[[],[],[]] ## row index, column index , value
(m,n)=self.X.shape
k=0
for i in range(m):
px=np.random.rand(n)
for j in range(n):
if px[j]<pselect:
xdata[0].append(i)
xdata[1].append(j)
xdata[2].append(self.X[i,j])
k+=1
xdata=mvm_prepare.sort_table(xdata,ifloat=1)
return(xdata)
def load(self,ifile):
if ifile==0:
sdir='/home/szedmak/data/image_text/labeled_images/'
sfile='/cambridge/Image007.png'
xmatpil=pilimg.open(sdir+sfile)
xmatrix=np.array(xmatpil.resize((768,512))) ## 504,756 -> 512,768
self.xfactors=np.array([[32,48,1],[16,16,1],[1,1,3]])
elif ifile==1:
sdir='/home/szedmak/src/python/algebra/tensor_decomp/'
sfile='handwritten.png'
xmatpil=pilimg.open(sdir+sfile)
xmatrix=np.array(xmatpil.resize((1024,1024))) ## 504,756 -> 512,768
self.xfactors=np.array([[32,32],[32,32]])
elif ifile==2:
sdir='/home/szedmak/src/python/algebra/tensor_decomp/'
sfile='tilling.png'
xmatpil=pilimg.open(sdir+sfile)
xmatrix=np.array(xmatpil.resize((576,512))) ## 504,756 -> 512,768
## xmatrix=np.mean(xmatrix,axis=2)
self.xfactors=np.array([[32,36,1],[16,16,1],[1,1,3]]) ## tilling
elif ifile==3:
sdir='/home/szedmak/src/python/algebra/tensor_decomp/'
sfile='poligon.png'
xmatpil=pilimg.open(sdir+sfile)
xmatrix=np.array(xmatpil.resize((512,512))) ## 504,756 -> 512,768
self.xfactors=np.array([[32,32,1],[16,16,1],[1,1,3]]) ## poligon
elif ifile==4:
sdir='/home/szedmak/src/python/algebra/tensor_decomp/'
sfile='2008toyroom.png'
xmatpil=pilimg.open(sdir+sfile)
xmatrix=np.array(xmatpil.resize((768,512))) ## 504,756 -> 512,768
## xmatrix=np.mean(xmatrix,axis=2)
self.xfactors=np.array([[32,48,1],[16,16,1],[1,1,3]])
elif ifile==5:
sdir='/home/szedmak/src/python/algebra/tensor_decomp/'
sfile='PLayroom_How_to_Organize_Kids_Room_2013.png'
xmatpil=pilimg.open(sdir+sfile)
xmatrix=np.array(xmatpil.resize((768,512))) ## 504,756 -> 512,768
self.xfactors=np.array([[32,48,1],[16,16,1],[1,1,3]])
print(sdir,sfile)
print(self.xfactors)
self.baseline(xmatrix)
ctensor=tensor_decomp.cls_tensor_decomp()
ctensor.load(xmatrix,self.xfactors)
X=ctensor.reorder_in_x(xmatrix)
## X=X.T
tshape=X.shape
mi=tshape[0]
mj=tshape[1]
## if len(tshape)>2:
## mk=tshape[2]
## X=X/self.image_scale
xdata=[[],[],[]] ## row index, column index , value
for i in range(mi):
for j in range(mj):
xdata[0].append(i)
xdata[1].append(int(np.mean(X[i,j])))
xdata[2].append(X[i,j]/self.image_scale)
xdata=mvm_prepare.sort_table(xdata,ifloat=1)
m1=mi
## m2=mj
m2=256
return(xdata,m1,m2)
def load_twofiles(self,iknown1,iknown2,ifile1,ifile2):
"""
load collected known entities (object, action,feature)
+ the unknown object, action pairs without value into the relation table.
!!! here we assume that all files contains the same set of object pairs.
!!! it can be changed later!!!
iknown1,ifile1 give the training
iknown2,ifile2 give the test
"""
## first phase collect object labels
if iknown1==0:
sfile1=self.listfull[ifile1]
else:
sfile1=self.listknown[ifile1]
if iknown2==0:
sfile2=self.listfull[ifile2]
else:
sfile2=self.listknown[ifile2]
self.collect_objects(sfile1)
self.collect_relations(sfile1,self.ddata_tra)
self.collect_relations(sfile2,self.ddata_tes)
## second phase load the relation table of known examples
xdata=[[],[],[]] ## row index, column index , value
## ndata=self.nobject**2
idata=0
ifixtrain=[]
ifixtest=[]
for iobject1,dobject2 in self.ddata_tra.items():
for iobject2,dvalues in dobject2.items():
for ifeature,svalue in dvalues.items():
if len(svalue)>0:
if self.irowcol==0:
xdata[0].append(iobject1)
## xdata[1].append(ifeature*self.nobject+iobject2)
xdata[1].append(ifeature+self.nfeature*iobject2)
xdata[2].append(float(svalue))
elif self.irowcol==1:
## xdata[0].append(ifeature*self.nobject+iobject1)
xdata[0].append(ifeature+self.nfeature*iobject1)
xdata[1].append(iobject2)
xdata[2].append(float(svalue))
ifixtrain.append(idata)
idata+=1
else:
svalue=self.ddata_tes[iobject1][iobject2][ifeature]
if len(svalue)>0:
fvalue=float(svalue)
else:
fvalue=0
if self.irowcol==0:
xdata[0].append(iobject1)
## xdata[1].append(ifeature*self.nobject+iobject2)
xdata[1].append(ifeature+self.nfeature*iobject2)
xdata[2].append(fvalue)
elif self.irowcol==1:
## xdata[0].append(ifeature*self.nobject+iobject1)
xdata[0].append(ifeature+self.nfeature*iobject1)
xdata[1].append(iobject2)
xdata[2].append(fvalue)
ifixtest.append(idata)
idata+=1
if self.irowcol==0:
self.nrow=self.nobject
self.ncol=self.nobject*self.nfeature
elif self.irowcol==1:
self.nrow=self.nobject*self.nfeature
self.ncol=self.nobject
xdata=mvm_prepare.sort_table(xdata,ifloat=1)
ifixtrain=np.array(ifixtrain)
ifixtest=np.array(ifixtest)
return(xdata,self.nrow,self.ncol,ifixtrain,ifixtest)
def create_diag_kernel(self,
xdatacls,
params_spec,
norm_spec,
itraintest=1):
xdata_1 = xdatacls.xdata_tra
nval = xdata_1[2].shape[1]
xranges_1 = xdatacls.xranges_rel
if itraintest != 1:
xdata_2 = xdatacls.xdata_tes
xranges_2 = xdatacls.xranges_rel_test
else:
xdata_2 = xdata_1
xranges_2 = xranges_1
nrow = xdatacls.nrow
ncol = xdatacls.ncol
## create column, row order
xdata_1_inv = [xdata_1[1], xdata_1[0]]
xdata_1_inv = mvm_prepare.sort_table(xdata_1_inv, idata=0)
xranges_1_inv = mvm_prepare.mvm_ranges(xdata_1_inv, ncol, None)
if itraintest != 1:
xdata_2_inv = [xdata_2[1], xdata_2[0]]
xdata_2_inv = mvm_prepare.sort_table(xdata_2_inv, idata=0)
xranges_2_inv = mvm_prepare.mvm_ranges(xdata_2_inv, ncol, None)
else:
xdata_2_inv = xdata_1_inv
xranges_2_inv = xranges_1_inv
cxdata = cls_dataitem()
npart = 3
cxdata.xdata_tra = [None] * npart
cxdata.xdata_tes = [None] * npart
for ilearner in range(nrow):
(istart, nlength) = xranges_1[ilearner]
nlength_1 = 0
for icol in range(nlength):
inboro = xdata_1[1][istart + icol]
nlength_1 += xranges_1_inv[inboro][1]
cxdata.xdata_tra=[np.zeros(nlength_1,dtype=int), \
np.zeros(nlength_1,dtype=int), \
np.zeros((nlength_1,nval))]
if itraintest != 1:
(istart, nlength) = xranges_2[ilearner]
nlength_2 = 0
for icol in range(nlength):
inboro = xdata_2[1][istart + icol]
nlength_2 += xranges_2_inv[inboro][1]
cxdata.xdata_tes=[np.zeros(nlength_1,dtype=int), \
np.zeros(nlength_1,dtype=int), \
np.zeros((nlength_1,nval))]
else:
nlength_2 = nlength_1
for ilearner in range(nrow):
(istart, nlength_tra) = xranges_1[ilearner]
for icol in range(nlength_tra):
inboro = xdata_1[1][istart + icol]
(istartr, nlengthr) = xranges_1_inv[inboro]
xilearners = xdata_1_inv[istartr, istartr:nlengthr]
ipos = 0
for ilearnerc in xilearners:
(istartc, nlengthc) = xranges_1[ilearnerc]
for ipart in range(npart):
cxdata.xdata_tra[ipart][ipos:ipos+nlengthc] \
=xdata_1[ipart][istartc,istartc+nlengthc]
ipos += nlengthc
if itraintest != 1:
(istart, nlength_tes) = xranges_2[ilearner]
for icol in range(nlength_tes):
inboro = xdata_2[1][istart + icol]
(istartr, nlengthr) = xranges_2_inv[inboro]
xilearners = xdata_2_inv[istartr, istartr:nlengthr]
ipos = 0
for ilearnerc in xilearners:
(istartc, nlengthc) = xranges_2[ilearnerc]
for ipart in range(npart):
cxdata.xdata_tes[ipart][ipos:ipos+nlengthc] \
=xdata_2[ipart][istartc,istartc+nlengthc]
ipos += nlengthc
cxdata.nrow = nrow
if itraintest == 1:
cxdata.nncol = nlength_tra
self.dkernel[ilearner]=mvm_kernel_eval.mvm_kernel_sparse(cxdata,itraintest, \
params_spec,norm_spec)
else:
cxdata.nncol = nlength_tes
self.dkernel_test[ilearner]=mvm_kernel_eval.mvm_kernel_sparse(cxdata,itraintest, \
params_spec,norm_spec)
return
def create_diag_kernel(self,xdatacls,params_spec,norm_spec,itraintest=1):
xdata_1=xdatacls.xdata_tra
nval=xdata_1[2].shape[1]
xranges_1=xdatacls.xranges_rel
if itraintest!=1:
xdata_2=xdatacls.xdata_tes
xranges_2=xdatacls.xranges_rel_test
else:
xdata_2=xdata_1
xranges_2=xranges_1
nrow=xdatacls.nrow
ncol=xdatacls.ncol
## create column, row order
xdata_1_inv=[xdata_1[1],xdata_1[0]]
xdata_1_inv=mvm_prepare.sort_table(xdata_1_inv,idata=0)
xranges_1_inv=mvm_prepare.mvm_ranges(xdata_1_inv,ncol,None)
if itraintest!=1:
xdata_2_inv=[xdata_2[1],xdata_2[0]]
xdata_2_inv=mvm_prepare.sort_table(xdata_2_inv,idata=0)
xranges_2_inv=mvm_prepare.mvm_ranges(xdata_2_inv,ncol,None)
else:
xdata_2_inv=xdata_1_inv
xranges_2_inv=xranges_1_inv
cxdata=cls_dataitem()
npart=3
cxdata.xdata_tra=[None]*npart
cxdata.xdata_tes=[None]*npart
for ilearner in range(nrow):
(istart,nlength)=xranges_1[ilearner]
nlength_1=0
for icol in range(nlength):
inboro=xdata_1[1][istart+icol]
nlength_1+=xranges_1_inv[inboro][1]
cxdata.xdata_tra=[np.zeros(nlength_1,dtype=int), \
np.zeros(nlength_1,dtype=int), \
np.zeros((nlength_1,nval))]
if itraintest!=1:
(istart,nlength)=xranges_2[ilearner]
nlength_2=0
for icol in range(nlength):
inboro=xdata_2[1][istart+icol]
nlength_2+=xranges_2_inv[inboro][1]
cxdata.xdata_tes=[np.zeros(nlength_1,dtype=int), \
np.zeros(nlength_1,dtype=int), \
np.zeros((nlength_1,nval))]
else:
nlength_2=nlength_1
for ilearner in range(nrow):
(istart,nlength_tra)=xranges_1[ilearner]
for icol in range(nlength_tra):
inboro=xdata_1[1][istart+icol]
(istartr,nlengthr)=xranges_1_inv[inboro]
xilearners=xdata_1_inv[istartr,istartr:nlengthr]
ipos=0
for ilearnerc in xilearners:
(istartc,nlengthc)=xranges_1[ilearnerc]
for ipart in range(npart):
cxdata.xdata_tra[ipart][ipos:ipos+nlengthc] \
=xdata_1[ipart][istartc,istartc+nlengthc]
ipos+=nlengthc
if itraintest!=1:
(istart,nlength_tes)=xranges_2[ilearner]
for icol in range(nlength_tes):
inboro=xdata_2[1][istart+icol]
(istartr,nlengthr)=xranges_2_inv[inboro]
xilearners=xdata_2_inv[istartr,istartr:nlengthr]
ipos=0
for ilearnerc in xilearners:
(istartc,nlengthc)=xranges_2[ilearnerc]
for ipart in range(npart):
cxdata.xdata_tes[ipart][ipos:ipos+nlengthc] \
=xdata_2[ipart][istartc,istartc+nlengthc]
ipos+=nlengthc
cxdata.nrow=nrow
if itraintest==1:
cxdata.nncol=nlength_tra
self.dkernel[ilearner]=mvm_kernel_eval.mvm_kernel_sparse(cxdata,itraintest, \
params_spec,norm_spec)
else:
cxdata.nncol=nlength_tes
self.dkernel_test[ilearner]=mvm_kernel_eval.mvm_kernel_sparse(cxdata,itraintest, \
params_spec,norm_spec)
return
def load_twofiles(self, iknown1, iknown2, ifile1, ifile2):
"""
load collected known entities (object, action,feature)
+ the unknown object, action pairs without value into the relation table.
!!! here we assume that all files contains the same set of object pairs.
!!! it can be changed later!!!
iknown1,ifile1 give the training
iknown2,ifile2 give the test
"""
## first phase collect object labels
if iknown1 == 0:
sfile1 = self.listfull[ifile1]
else:
sfile1 = self.listknown[ifile1]
if iknown2 == 0:
sfile2 = self.listfull[ifile2]
else:
sfile2 = self.listknown[ifile2]
self.collect_objects(sfile1)
self.collect_relations(sfile1, self.ddata_tra)
self.collect_relations(sfile2, self.ddata_tes)
## second phase load the relation table of known examples
xdata = [[], [], []] ## row index, column index , value
## ndata=self.nobject**2
idata = 0
ifixtrain = []
ifixtest = []
for iobject1, dobject2 in self.ddata_tra.items():
for iobject2, dvalues in dobject2.items():
for ifeature, svalue in dvalues.items():
if len(svalue) > 0:
if self.irowcol == 0:
xdata[0].append(iobject1)
## xdata[1].append(ifeature*self.nobject+iobject2)
xdata[1].append(ifeature +
self.nfeature * iobject2)
xdata[2].append(float(svalue))
elif self.irowcol == 1:
## xdata[0].append(ifeature*self.nobject+iobject1)
xdata[0].append(ifeature +
self.nfeature * iobject1)
xdata[1].append(iobject2)
xdata[2].append(float(svalue))
ifixtrain.append(idata)
idata += 1
else:
svalue = self.ddata_tes[iobject1][iobject2][ifeature]
if len(svalue) > 0:
fvalue = float(svalue)
else:
fvalue = 0
if self.irowcol == 0:
xdata[0].append(iobject1)
## xdata[1].append(ifeature*self.nobject+iobject2)
xdata[1].append(ifeature +
self.nfeature * iobject2)
xdata[2].append(fvalue)
elif self.irowcol == 1:
## xdata[0].append(ifeature*self.nobject+iobject1)
xdata[0].append(ifeature +
self.nfeature * iobject1)
xdata[1].append(iobject2)
xdata[2].append(fvalue)
ifixtest.append(idata)
idata += 1
if self.irowcol == 0:
self.nrow = self.nobject
self.ncol = self.nobject * self.nfeature
elif self.irowcol == 1:
self.nrow = self.nobject * self.nfeature
self.ncol = self.nobject
xdata = mvm_prepare.sort_table(xdata, ifloat=1)
ifixtrain = np.array(ifixtrain)
ifixtest = np.array(ifixtest)
return (xdata, self.nrow, self.ncol, ifixtrain, ifixtest)
