def mvm_train(self):
"""
execute the trianing procedure
Inputs:
"""
## print('Generate kernels')
time0 = time.time()
self.compute_kernels()
if self.verbose == 1:
print('Kernel computation:', time.time() - time0)
## print('Solve optimization problem')
time0 = time.time()
self.KX=mmr_kernel(self,self.itrain,self.itrain,ioutput=0, \
itraintest=0, itensor=self.kmode)[0]
self.KY = mmr_kernel(self, self.itrain, self.itrain, ioutput=1)[0]
if self.verbose == 1:
print('Kernel merge computation:', time.time() - time0)
## self.solvertime=time.time()-time0
## t0=time.clock()
cOptDual = base.cls_dual(None, None)
self.dual = cOptDual
time0 = time.time()
cmvm_solver = mvm_solver_cls.cls_mvm_solver()
self.dual.alpha = cmvm_solver.mvm_solver(self)
self.solvertime = time.time() - time0
return (cOptDual)
def mvm_train(self):
"""
execute the trianing procedure
Inputs:
"""
## print('Generate kernels')
time0=time.time()
self.compute_kernels()
if self.verbose==1:
print('Kernel computation:',time.time()-time0)
## print('Solve optimization problem')
time0=time.time()
self.KX=mmr_kernel(self,self.itrain,self.itrain,ioutput=0, \
itraintest=0, itensor=self.kmode)[0]
self.KY=mmr_kernel(self,self.itrain,self.itrain,ioutput=1)[0]
if self.verbose==1:
print('Kernel merge computation:',time.time()-time0)
## self.solvertime=time.time()-time0
## t0=time.clock()
cOptDual=base.cls_dual(None,None)
self.dual=cOptDual
time0=time.time()
cmvm_solver=mvm_solver_cls.cls_mvm_solver()
self.dual.alpha=cmvm_solver.mvm_solver(self)
self.solvertime=time.time()-time0
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 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_test(self,cOptDual,itraindata=1):
if self.YKernel.kernel_params.kernel_type>0:
self.itestmode=0
if itraindata==0:
itest=self.itrain
else:
itest=self.itest
if self.iperceptron in (0,2):
KXcross=mmr_kernel(self,self.itrain,itest,ioutput=0, \
itraintest=1, itraindata=itraindata, \
itensor=self.kmode)[0]
KYcross=mmr_kernel(self,self.itrain,self.itrain,ioutput=1, \
itraintest=1,itraindata=itraindata)[0]
mtest=KXcross.shape[1]
cPredict=base.cls_predict()
if self.YKernel.ifeature==0:
if self.YKernel.kernel_params.kernel_type==0:
YTrain=self.YKernel.get_train_norm(self.itrain)
Zw0=np.dot(YTrain.T,(np.outer(cOptDual.alpha, \
np.ones(mtest))*KXcross))
if self.csolver.ibias_estim==1:
Zw0=Zw0+np.outer(cOptDual.bias,np.ones(mtest))
xnorm=np.sqrt(np.sum(Zw0**2,axis=0))
xnorm=xnorm+(xnorm==0)
Zw=Zw0/np.outer(np.ones(Zw0.shape[0]),xnorm)
Y0=self.YKernel.get_Y0_norm(self.itrain)
cPredict.ZTest=np.dot(Y0,Zw)
else:
Zw0=np.dot(KYcross.T, \
(np.outer(cOptDual.alpha,np.ones(mtest))*KXcross))
cPredict.ZTest=Zw0
if self.csolver.ibias_estim==1:
cPredict.bias=cOptDual.bias
else:
cPredict.bias=0
Zw=Zw0
cPredict.Zw0=Zw0.T
cPredict.Zw=Zw.T
if self.itestmode in (0,2,3,4):
Y0=self.YKernel.get_Y0(self.itrain)
cPredict.zPremax=cPredict.ZTest.max(0)
cPredict.iPredCat=cPredict.ZTest.argmax(0)
cPredict.zPred=Y0[cPredict.iPredCat]
if self.itestmode==2 and self.YKernel.kernel_params.kernel_type==0:
cPredict.knnPredCat=np.argsort \
(-cPredict.ZTest,axis=0)[:self.testknn,:]
elif self.itestmode==3:
n=YTrain.shape[1]
nsort=5
for i in range(mtest):
cPredict.zPred[i,:]=np.zeros(n)
iones=np.argsort(-Zw[:,i])[:nsort]
for j in range(nsort-1):
if Zw[iones[j],i]<0.04 or \
Zw[iones[j],i]>10.0*Zw[iones[j+1],i]:
iones=iones[:(j+1)]
break
cPredict.zPred[i,iones]=1
elif self.itestmode==1:
YTrain=self.YKernel.get_train(self.itrain)
ndim=YTrain.shape[1]
i2boro=np.argsort(-Zw0,axis=0)[0:self.testknn,:].T
cPredict.zPred=np.zeros((mtest,ndim))
for i in range(mtest):
zsum=np.zeros(ndim)
for j in range(self.testknn):
zsum+=YTrain[i2boro[i,j]]
cPredict.zPred[i,:]=1*(zsum>=self.testknn/2)
elif self.YKernel.ifeature==1:
Y0=self.YKernel.get_Y0(self.itrain)
cPredict.ZTest=np.dot(KYcross.T,(np.outer(cOptDual.alpha, \
np.ones(mtest))*KXcross))
cPredict.zPremax=cPredict.ZTest.max(0)
cPredict.iPredCat=cPredict.ZTest.argmax(0)
cPredict.zPred=Y0[cPredict.iPredCat]
if self.itestmode==2:
cPredict.knnPredCat=np.argsort \
(-cPredict.ZTest,axis=0)[:self.testknn,:]
elif self.iperceptron==1:
if itraindata==0:
X=self.XKernel[0].get_train_norm(self.itrain)
for i in range(1,len(self.XKernel)):
X=np.hstack((X,self.XKernel[i].get_train_norm(self.itrain)))
else:
X=self.XKernel[0].get_test_norm(itest)
for i in range(1,len(self.XKernel)):
X=np.hstack((X,self.XKernel[i].get_test_norm(itest)))
cPredict=base.cls_predict()
xmean=np.mean(X)
mtest=X.shape[0]
Zw=np.dot(cOptDual.W, \
np.hstack((X,np.ones((mtest,1))*xmean)).T)
cPredict.ZTest=np.dot(YTrain,Zw)
cPredict.zPremax=cPredict.ZTest.max(0)
cPredict.iPredCat=cPredict.ZTest.argmax(0)
cPredict.zPred=YTrain[cPredict.iPredCat]
if self.itestmode==2:
cPredict.knnPredCat=np.argsort(-cPredict.ZTest, \
axis=0)[:self.testknn,:]
return(cPredict)
def mmr_test(self, cOptDual, itraindata=1):
if self.YKernel.kernel_params.kernel_type > 0:
self.itestmode = 0
if itraindata == 0:
itest = self.itrain
else:
itest = self.itest
if self.iperceptron in (0, 2):
KXcross=mmr_kernel(self,self.itrain,itest,ioutput=0, \
itraintest=1, itraindata=itraindata, \
itensor=self.kmode)[0]
KYcross=mmr_kernel(self,self.itrain,self.itrain,ioutput=1, \
itraintest=1,itraindata=itraindata)[0]
mtest = KXcross.shape[1]
cPredict = base.cls_predict()
if self.YKernel.ifeature == 0:
if self.YKernel.kernel_params.kernel_type == 0:
YTrain = self.YKernel.get_train_norm(self.itrain)
Zw0=np.dot(YTrain.T,(np.outer(cOptDual.alpha, \
np.ones(mtest))*KXcross))
if self.csolver.ibias_estim == 1:
Zw0 = Zw0 + np.outer(cOptDual.bias, np.ones(mtest))
xnorm = np.sqrt(np.sum(Zw0**2, axis=0))
xnorm = xnorm + (xnorm == 0)
Zw = Zw0 / np.outer(np.ones(Zw0.shape[0]), xnorm)
Y0 = self.YKernel.get_Y0_norm(self.itrain)
cPredict.ZTest = np.dot(Y0, Zw)
else:
Zw0=np.dot(KYcross.T, \
(np.outer(cOptDual.alpha,np.ones(mtest))*KXcross))
cPredict.ZTest = Zw0
if self.csolver.ibias_estim == 1:
cPredict.bias = cOptDual.bias
else:
cPredict.bias = 0
Zw = Zw0
cPredict.Zw0 = Zw0.T
cPredict.Zw = Zw.T
if self.itestmode in (0, 2, 3, 4):
Y0 = self.YKernel.get_Y0(self.itrain)
cPredict.zPremax = cPredict.ZTest.max(0)
cPredict.iPredCat = cPredict.ZTest.argmax(0)
cPredict.zPred = Y0[cPredict.iPredCat]
if self.itestmode == 2 and self.YKernel.kernel_params.kernel_type == 0:
cPredict.knnPredCat=np.argsort \
(-cPredict.ZTest,axis=0)[:self.testknn,:]
elif self.itestmode == 3:
n = YTrain.shape[1]
nsort = 5
for i in range(mtest):
cPredict.zPred[i, :] = np.zeros(n)
iones = np.argsort(-Zw[:, i])[:nsort]
for j in range(nsort - 1):
if Zw[iones[j],i]<0.04 or \
Zw[iones[j],i]>10.0*Zw[iones[j+1],i]:
iones = iones[:(j + 1)]
break
cPredict.zPred[i, iones] = 1
elif self.itestmode == 1:
YTrain = self.YKernel.get_train(self.itrain)
ndim = YTrain.shape[1]
i2boro = np.argsort(-Zw0, axis=0)[0:self.testknn, :].T
cPredict.zPred = np.zeros((mtest, ndim))
for i in range(mtest):
zsum = np.zeros(ndim)
for j in range(self.testknn):
zsum += YTrain[i2boro[i, j]]
cPredict.zPred[i, :] = 1 * (zsum >= self.testknn / 2)
elif self.YKernel.ifeature == 1:
Y0 = self.YKernel.get_Y0(self.itrain)
cPredict.ZTest=np.dot(KYcross.T,(np.outer(cOptDual.alpha, \
np.ones(mtest))*KXcross))
cPredict.zPremax = cPredict.ZTest.max(0)
cPredict.iPredCat = cPredict.ZTest.argmax(0)
cPredict.zPred = Y0[cPredict.iPredCat]
if self.itestmode == 2:
cPredict.knnPredCat=np.argsort \
(-cPredict.ZTest,axis=0)[:self.testknn,:]
elif self.iperceptron == 1:
if itraindata == 0:
X = self.XKernel[0].get_train_norm(self.itrain)
for i in range(1, len(self.XKernel)):
X = np.hstack(
(X, self.XKernel[i].get_train_norm(self.itrain)))
else:
X = self.XKernel[0].get_test_norm(itest)
for i in range(1, len(self.XKernel)):
X = np.hstack((X, self.XKernel[i].get_test_norm(itest)))
cPredict = base.cls_predict()
xmean = np.mean(X)
mtest = X.shape[0]
Zw=np.dot(cOptDual.W, \
np.hstack((X,np.ones((mtest,1))*xmean)).T)
cPredict.ZTest = np.dot(YTrain, Zw)
cPredict.zPremax = cPredict.ZTest.max(0)
cPredict.iPredCat = cPredict.ZTest.argmax(0)
cPredict.zPred = YTrain[cPredict.iPredCat]
if self.itestmode == 2:
cPredict.knnPredCat=np.argsort(-cPredict.ZTest, \
axis=0)[:self.testknn,:]
return (cPredict)
