if alpha==0:
print "**ERRO**"
print "aplha=0, impossivel continuar o algorimto"
break
step_size=sigma/alpha
error=(X*w_old)-Y
grad1=it.GetGradL(error,X)
U=it.getU(w_old,step_size,grad1).todense()
w_new=list(itertools.imap(soft,U.tolist(),[step_size*lamb for x in xrange(len(U.tolist()))]))
w_new=np.matrix(w_new)
w_new=w_new.transpose()
dif=w_new-w_old
dif=dif.transpose()*dif
w_new=sparse.csr_matrix(w_new)
error=(X*w_new)-Y
y_new=it.get_func(error,w_new,lamb) #funcao de erro
count=0
if i!=0:
while y_new>=y_old-sigma2*alpha*dif[0,0]:
print "A diminuir step:",i
step_size=step_size/10
U=it.getU(w_old,step_size,grad1).todense()
w_new=list(itertools.imap(soft,U.tolist(),[step_size*lamb for x in xrange(len(U.tolist()))]))
w_new=np.matrix(w_new)
w_new=w_new.transpose()
error=(X*w_new)-Y
dif=w_new-w_old
dif=dif.transpose()*dif
y_new=it.get_func(error,w_new,lamb) #funcao de erro
count=count+1
if count==40:
y=[]
#X,total=cria_dados.delcomun(X,total)
##Y=np.log2(Y)
#dictionaryDev,totalDev,yDev=read_output("../le_ficheiro/dev.txt")
#XDev,YDev=criaXY(dictionaryDev,total,yDev)
#YDev=YDev/maxi
#YDev=np.log10(YDev)
W,erro_dev,lamb=calculaW(X,Y,Xteste,Yteste,Xdev,Ydev)
error=(X*W-Y)
print "OBJECTIVO:", it.get_func(error,W,lamb)
'''
print "___________________________________________________"
print "___________________________________________________\n\n"
print "lambda:",lamb
print "erro do teste-----------------------\n", erro(f+"test.txt",W,total)
#print "erro do treino-----------------------\n", erro("../le_ficheiro/train.txt",W,total)
#print "erro do dev-----------------------\n", erro("../le_ficheiro/dev.txt",W,total)
print "__________________________"
print "W:"
print W
error=(X*W)-Y
print "----------------------------------"
print "funcao de custo:"
print it.get_func(error,W,lamb)
#print "----------------------------------"
Ytotal=pickle.load(file)
file=open("../dados_priberam/dados_W_random.pkl","rb")
W=pickle.load(file)
trainsize=int(Xtotal.shape[0]*0.8)
devsize=int(Xtotal.shape[0]*0.1)+trainsize
lamb=10
max_iter=10000
vec=sparse.csr_matrix([0 for i in xrange(Xtotal.shape[1])])
vec=vec.transpose()
#pdb.set_trace()
error=Xtotal[:trainsize,:]*W - Ytotal[:trainsize,:]
custo=it.get_func(error,W,lamb)
print custo
#w_estimado=grad.grad(Xtotal[:trainsize,:],Ytotal[0:trainsize,0],vec,Xtotal[devsize:,:],Ytotal[devsize:,0],Xtotal[trainsize:devsize,:],Ytotal[trainsize:devsize,0],custo,lamb)
#w_estimado,yy1,xx1=grad_old.grad(Xtotal,Ytotal,vec,Xtotal,Ytotal,Xtotal,Ytotal,lamb,max_iter,True)
#w_estimado,yy2,xx2=grad_old.grad(Xtotal,Ytotal,vec,Xtotal,Ytotal,Xtotal,Ytotal,lamb,max_iter,False)
#w_estimado,yy3,xx3=grad.grad(Xtotal,Ytotal,vec,Xtotal,Ytotal,Xtotal,Ytotal,max_iter)
w0=[]
w1=[]
w2=[]
w3=[]
w4=[]
lambs=[0.1,1,10,10e2,10e3,10e4,10e5,10e6,10e7,10e8,10e9,10e10,10e11]
xg=[np.log10(x) for x in lambs]
'''
for lamb in lambs:
w_estimado,yy3,xx3=grad.grad(Xtotal,Ytotal,vec,Xtotal,Ytotal,Xtotal,Ytotal,max_iter,lamb)
def lasso(X,Y,w0,total):
import matplotlib.pyplot as plt
print "A fazer calculos..."
w_new=np.zeros(w0.shape)
Y=sparse.csr_matrix(np.log2(Y.todense()))
final=[0,0]
#Use a fixed small step size
step_size = 0.00000002
#max iterations
max_iter = 1000
yyy=[]
xxx=[]
it.soft=soft
for lamb in [10**x for x in xrange(-12,-9)]:
#for lamb in [0.000001]:
w_old=w0
yy=[]
xx=[]
print "calculos terminados, a fazer iterecoes"
for i in xrange(max_iter):
error=(X*w_old)-Y
grad1=it.GetGradL(error,X)
U=it.getU(w_old,step_size,grad1).todense()
w_new=list(itertools.imap(soft,U.tolist(),[step_size*lamb for x in xrange(len(U.tolist()))]))
w_new=np.matrix(w_new)
w_new=w_new.transpose()
error=(X*w_new)-Y
y_new=it.get_func(error,w_old,lamb) #funcao de erro
y_old=y_new
w_old = sparse.csr_matrix(w_new)
yy.append(y_new)
xx.append(i)
print "exceeded maximum number of iterations, leaving"
media=RRegression_beta.erro("../le_ficheiro/dev.txt",w_new,total)
print lamb,media
if final[0]>media or final[0]==0:
final[0]=media
final[1]=lamb
graphFinal=yy
zero=0.0
for J in xrange(w_new.shape[0]):
if w_new[J,0]==0:
zero=zero+1.0
#print "zero->",zero
sp=(zero/w_new.shape[0])*100
print "percentagem:",sp
yyy.append(sp)
xxx.append(lamb)
plt.figure(1)
plt.subplot(211)
plt.title("Funcao de custo")
plt.plot(xx,graphFinal,"r")
plt.subplot(212)
plt.title("Percentagem de W com valor =0")
import pylab
print yyy
#pylab.ylim([0,100])
plt.plot(yyy,"b",yyy,"ro")
plt.show()
return w_new,y_new