def compute_kernel(self, xdatacls):
if xdatacls.category == 0 or xdatacls.category == 3:
## self.K=mvm_kernel_eval.mvm_kernel_sparse(xdatacls,isymmetric, \
## self.kernel_params, \
## self.norm)
isymmetric = 1
self.K=mvm_kernel_eval.mvm_kernel_sparse(xdatacls,isymmetric, \
self.kernel_params, \
self.norm)
elif xdatacls.category == 1:
self.K=mvm_kernel_eval.kernel_category_2d(xdatacls, \
self.kernel_params, \
self.norm)
elif xdatacls.category == 2:
self.K=mvm_kernel_eval.kernel_categoryvec_2d(xdatacls, \
self.kernel_params, \
self.norm)
self.K += self.xbias
return
def compute_kernel(self,xdatacls):
if xdatacls.category==0 or xdatacls.category==3:
## self.K=mvm_kernel_eval.mvm_kernel_sparse(xdatacls,isymmetric, \
## self.kernel_params, \
## self.norm)
isymmetric=1
self.K=mvm_kernel_eval.mvm_kernel_sparse(xdatacls,isymmetric, \
self.kernel_params, \
self.norm)
elif xdatacls.category==1:
self.K=mvm_kernel_eval.kernel_category_2d(xdatacls, \
self.kernel_params, \
self.norm)
elif xdatacls.category==2:
self.K=mvm_kernel_eval.kernel_categoryvec_2d(xdatacls, \
self.kernel_params, \
self.norm)
self.K+=self.xbias
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 create_monolith(self, xdatacls, isymmetric, params_spec, norm_spec):
self.Kmonolith=mvm_kernel_eval.mvm_kernel_sparse(xdatacls,isymmetric, \
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 create_monolith(self,xdatacls,isymmetric,params_spec,norm_spec):
self.Kmonolith=mvm_kernel_eval.mvm_kernel_sparse(xdatacls,isymmetric, \
params_spec,norm_spec)
return