def compute_kernel_row(self):
K = np.zeros((self.nrow, self.nrow))
drow = {}
for i in range(self.nrow):
iA = np.where(self.xdata[i] > 0)[0]
nsub = len(iA)
if nsub > 0:
A = self.base_vectors[iA]
AAT = self.kernel_col[iA.reshape((nsub, 1)), iA]
AATI = sp_lin.pinvh(AAT)
drow[i] = (np.dot(AATI, A), iA)
else:
drow[i] = (None, np.array([]))
for i in range(self.nrow):
(BBTI, iB) = drow[i]
if len(iB) > 0:
for j in range(i + 1):
(AATI, iA) = drow[j]
## = trace(Vhi.T,Vhj)=\braket{Vhi,Vhj}_{Frobenius}
## (BB^T)^{-1}BA^T(AA^T)^{-1}
if len(iA) > 0:
BAT = self.kernel_col[iB.reshape((len(iB), 1)), iA]
## xP=np.diag(np.dot(BBTI.T,np.dot(BAT,AATI)))
xP = np.dot(BBTI, AATI.T) * BAT
K[i, j] = np.sum(xP)
K[j, i] = K[i, j]
## if i%1000==0:
## print(i)
d1 = np.diag(K)
d2 = d1
self.kernel_row = kernel_eval_nl(K, d1, d2, self.param_row)
print('Modular kernel done')
return
def compute_kernel_row(self):
K=np.zeros((self.nrow,self.nrow))
drow={}
for i in range(self.nrow):
iA=np.where(self.xdata[i]>0)[0]
nsub=len(iA)
if nsub>0:
A=self.base_vectors[iA]
AAT=self.kernel_col[iA.reshape((nsub,1)),iA]
AATI=sp_lin.pinvh(AAT)
drow[i]=(np.dot(AATI,A),iA)
else:
drow[i]=(None,np.array([]))
for i in range(self.nrow):
(BBTI,iB)=drow[i]
if len(iB)>0:
for j in range(i+1):
(AATI,iA)=drow[j]
## = trace(Vhi.T,Vhj)=\braket{Vhi,Vhj}_{Frobenius}
## (BB^T)^{-1}BA^T(AA^T)^{-1}
if len(iA)>0:
BAT=self.kernel_col[iB.reshape((len(iB),1)),iA]
## xP=np.diag(np.dot(BBTI.T,np.dot(BAT,AATI)))
xP=np.dot(BBTI,AATI.T)*BAT
K[i,j]=np.sum(xP)
K[j,i]=K[i,j]
## if i%1000==0:
## print(i)
d1=np.diag(K)
d2=d1
self.kernel_row=kernel_eval_nl(K,d1,d2,self.param_row)
print('Modular kernel done')
return
def compute_kernel_kernel_row(self, itrates=0):
K = np.zeros((self.nrow, self.nrow_test))
drow = {}
for i in range(self.nrow):
iA = np.where(self.xdata[i] > 0)[0]
nsub = len(iA)
if nsub > 0:
AAT = self.kernel_col[iA.reshape((nsub, 1)), iA]
AATI = sp_lin.pinvh(AAT)
drow[i] = (AATI, iA)
else:
drow[i] = (None, None)
drow_test = {}
if itrates == 1:
for i in range(self.nrow_test):
iA = np.where(self.xdata_test[i] > 0)[0]
nsub = len(iA)
if nsub > 0:
AAT = self.kernel_col[iA.reshape((nsub, 1)), iA]
AATI = sp_lin.pinvh(AAT)
drow_test[i] = (AATI, iA)
else:
drow_test[i] = (None, None)
else:
drow_test = drow
for i in range(self.nrow):
(BBTI, iB) = drow[i]
if iB is not None:
if itrates == 0: ## to avoid double computation of symmetric kernel
for j in range(i + 1):
(AATI, iA) = drow[j]
if iA is not None:
KBA = self.kernel_col[iB.reshape((len(iB), 1)), iA]
xP = np.dot(KBA.T, np.dot(BBTI, np.dot(KBA, AATI)))
K[i, j] = np.sum(xP)
K[j, i] = K[i, j]
elif itrates == 1:
for j in range(self.nrow_test):
(AATI, iA) = drow_test[j]
if iA is not None:
KBA = self.kernel_col[iB.reshape((len(iB), 1)), iA]
xP = np.dot(KBA.T, np.dot(BBTI, np.dot(KBA, AATI)))
K[i, j] = np.sum(xP)
## if i%1000==0:
## print(i)
dK = np.sqrt(np.diag(K))
idK = np.where(dK == 0)[0]
for i in idK:
K[i, i] = 1.0
dK[i] = 1.0
K = K / np.outer(dK, dK)
d1 = np.diag(K)
if itrates == 0:
d2 = d1
elif itrates == 1:
d2 = np.zeros(self.nrow_test)
for i in range(self.nrow_test):
(AATI, iA) = drow_test[i]
if iA is not None:
KBA = self.kernel_col[iA.reshape((len(iA), 1)), iA]
xP = np.dot(KBA.T, np.dot(AATI, np.dot(KBA, AATI)))
d2[i] = np.sum(xP)
if itrates == 0:
self.kernel_row = kernel_eval_nl(K, d1, d2, self.param_row)
elif itrates == 1:
self.kernel_row_test=kernel_eval_nl(K,d1,d2, \
self.param_row)
self.kernel_row = kernel_center(self.kernel_row)
dK = np.diag(self.kernel_row)
idK = np.where(dK < 0)[0]
for i in idK:
self.kernel_row[i, i] = 0
dK = np.sqrt(np.diag(self.kernel_row))
idK = np.where(dK == 0)[0]
for i in idK:
self.kernel_row[i, i] = 1.0
dK[i] = 1.0
self.kernel_row = self.kernel_row / np.outer(dK, dK)
## print('Modular kernel done')
return
def compute_kernel_kernel_row(self,itrates=0):
K=np.zeros((self.nrow,self.nrow_test))
drow={}
for i in range(self.nrow):
iA=np.where(self.xdata[i]>0)[0]
nsub=len(iA)
if nsub>0:
AAT=self.kernel_col[iA.reshape((nsub,1)),iA]
AATI=sp_lin.pinvh(AAT)
drow[i]=(AATI,iA)
else:
drow[i]=(None,None)
drow_test={}
if itrates==1:
for i in range(self.nrow_test):
iA=np.where(self.xdata_test[i]>0)[0]
nsub=len(iA)
if nsub>0:
AAT=self.kernel_col[iA.reshape((nsub,1)),iA]
AATI=sp_lin.pinvh(AAT)
drow_test[i]=(AATI,iA)
else:
drow_test[i]=(None,None)
else:
drow_test=drow
for i in range(self.nrow):
(BBTI,iB)=drow[i]
if iB is not None:
if itrates==0: ## to avoid double computation of symmetric kernel
for j in range(i+1):
(AATI,iA)=drow[j]
if iA is not None:
KBA=self.kernel_col[iB.reshape((len(iB),1)),iA]
xP=np.dot(KBA.T,np.dot(BBTI,np.dot(KBA,AATI)))
K[i,j]=np.sum(xP)
K[j,i]=K[i,j]
elif itrates==1:
for j in range(self.nrow_test):
(AATI,iA)=drow_test[j]
if iA is not None:
KBA=self.kernel_col[iB.reshape((len(iB),1)),iA]
xP=np.dot(KBA.T,np.dot(BBTI,np.dot(KBA,AATI)))
K[i,j]=np.sum(xP)
## if i%1000==0:
## print(i)
dK=np.sqrt(np.diag(K))
idK=np.where(dK==0)[0]
for i in idK:
K[i,i]=1.0
dK[i]=1.0
K=K/np.outer(dK,dK)
d1=np.diag(K)
if itrates==0:
d2=d1
elif itrates==1:
d2=np.zeros(self.nrow_test)
for i in range(self.nrow_test):
(AATI,iA)=drow_test[i]
if iA is not None:
KBA=self.kernel_col[iA.reshape((len(iA),1)),iA]
xP=np.dot(KBA.T,np.dot(AATI,np.dot(KBA,AATI)))
d2[i]=np.sum(xP)
if itrates==0:
self.kernel_row=kernel_eval_nl(K,d1,d2,self.param_row)
elif itrates==1:
self.kernel_row_test=kernel_eval_nl(K,d1,d2, \
self.param_row)
self.kernel_row=kernel_center(self.kernel_row)
dK=np.diag(self.kernel_row)
idK=np.where(dK<0)[0]
for i in idK:
self.kernel_row[i,i]=0
dK=np.sqrt(np.diag(self.kernel_row))
idK=np.where(dK==0)[0]
for i in idK:
self.kernel_row[i,i]=1.0
dK[i]=1.0
self.kernel_row=self.kernel_row/np.outer(dK,dK)
## print('Modular kernel done')
return