return wfinal,yfinal,final[1]
if __name__ == '__main__':
f="../le_ficheiro/meta+palavra1"
print "FICHEIRO:",f,"\n"
dictionary,total,y=cria_dados.read_output(f+"train.txt")
#X,Y,mediaY,stdY,mediaX=cria_dados.criaXY(dictionary,total,y,True)
X,Y=cria_dados.criaXY(dictionary,total,y,False)
pdb.set_trace()
X,total=cria_dados.delstopword(X,total,True)
dictionary,temp,y=cria_dados.read_output(f+"test.txt")
Xteste,Yteste=cria_dados.criaXY(dictionary,total,y,False)
dictionary,temp,y=cria_dados.read_output(f+"dev.txt")
Xdev,Ydev=cria_dados.criaXY(dictionary,total,y,False)
#Y_train=Y.shape[0]
#Y_test=Yteste.shape[0]
#Y_dev=Ydev.shape[0]
#Ytotal=np.vstack((Y.toarray(),Yteste.toarray(),Ydev.toarray()))
#maxi=max(np.abs(Ytotal))
#Ytotal=Ytotal-np.ones_like(Ytotal)*maxi
#Y=sparse.csr_matrix(Ytotal)[0:Y_train,0]
#Yteste=sparse.csr_matrix(Ytotal)[Y_train:Y_test+Y_train,0]
#Ydev=sparse.csr_matrix(Ytotal)[Y_test+Y_train:,0]
Ytotal=pickle.load(file)
file=open("../dados_priberam/dados_indice_corrigido.pkl","rb")
indices=pickle.load(file)
trainsize=int(Xtotal.shape[0]*0.8)
devsize=int(Xtotal.shape[0]*0.1)+trainsize
#Xtrain,Ytrain,Xtest,Ytest,Xdev,Ydev=PB.separaXY(Xtotal,Ytotal)
#train_index=xrange(trainsize)
#dev_index=xrange(trainsize,devsize)
#test_index=xrange(devsize,Xtotal.shape[0])
Xtotal=Xtotal.tocsc()
Ytotal=Ytotal.tocsc()
Xtotal,indices=cria_dados.delstopword(Xtotal,indices,False)
#Xtotal,indices=PB.tira_meta(Xtotal,indices)
#vec=sparse.csr_matrix([0 for i in xrange(Xtrain.shape[1])])
vec=sparse.csr_matrix([0 for i in xrange(Xtotal[:trainsize,:].shape[1])])
vec=vec.transpose()
print "-----------------------------"
for alpha in [0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1]:
print "------------------------------"
W,F,lamb=elastic_net.elastic_net(Xtotal[:trainsize,:],Ytotal[0:trainsize,0],vec,Xtotal[devsize:,:],Ytotal[devsize:,0],Xtotal[trainsize:devsize,:],Ytotal[trainsize:devsize,0],alpha)
print "ALPHA", alpha
print "LAMBDA",lamb
print "ERRO:",Rfechado.erro(Xtotal[trainsize:devsize,:],Ytotal[trainsize:devsize,:],W) #W=elastic_net.elastic_net(Xtrain,Ytrain.transpose(),vec,Xdev,Ydev.transpose(),Xtest,Ytest.transpose(),0.5)
print "ERRO_DEV:",Rfechado.erro(Xtotal[trainsize:devsize,:],Ytotal[trainsize:devsize,:],W) #W=elastic_net.elastic_net(Xtrain,Ytrain.transpose(),vec,Xdev,Ydev.transpose(),Xtest,Ytest.transpose(),0.5)
print "ERRO_TEST:",Rfechado.erro(Xtotal[trainsize:devsize,:],Ytotal[trainsize:devsize,:],W)
