for lamb in [10**x for x in xrange(-3,6)]:
#for lamb in [10**5]:
w_old_old=w0
w_old=w0
yy=[]
xx=[]
print "calculos terminados, a fazer iterecoes"
for i in xrange(max_iter):
error=(X*w_old)-Y
#print i
if i==0:
step_size=0.001
else:
error_old=error=(X*w_old)-Y
error_old_old=(X*w_old_old)-Y
alpha=it.get_step(w_old,w_old_old,X,error_old,error_old_old,lamb)
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
def lasso(X,Y,w0,Xteste,Yteste,Xdev,Ydev):
import matplotlib.pyplot as plt
w_new=np.zeros(w0.shape)
final=[0,0,0]
sigma=1
sigma2=0.0000001
max_iter =2000
yyy=[]
xxx=[]
gmedia=[]
gmediatest=[]
w0=w0.todense()
soft=it.soft
lambs=[10**x for x in xrange(0,8)]
xg=map(lambda x: np.log10(x),lambs)
#lambdas=100*X.shape[0]
#lambs=[lambdas]
#xg=[2]
for lamb in lambs:
print "A iniciar Iteracoes para lambda=",lamb
w_old_old=w0
w_old=w0
yy=[]
xx=[]
i=0
while i<max_iter:
#print "iteracao",i
if i==0:
step_size=0.0000001
else:
error_old=(X*w_old)-Y
error_old_old=(X*w_old_old)-Y
alpha=it.get_step(w_old,w_old_old,X,error_old,error_old_old,lamb)
if alpha!=0:
step_size=sigma/alpha
else:
i=max_iter
error=(X*w_old)-Y
grad1=it.GetGradL(error,X)
U=it.getU(w_old,step_size,grad1)
#pdb.set_trace()
#w_new=[soft(x,step_size*lamb) for x in U.transpose().tolist()[0]]
#w_new=sparse.csr_matrix(w_new)
#w_new=w_new.transpose()
w_new=it.softt(U,step_size*lamb)
#pdb.set_trace()
dif=w_new-w_old
dif=dif.transpose()*dif
error=(X*w_new)-Y
y_new=it.get_func_lasso(error,np.matrix(w_new).transpose(),lamb) #funcao de erro
count=0
if i!=0:
w_temp=w_new
while y_new>=y_old-sigma2*alpha*dif[0,0] and i<max_iter:
#while y_new>=y_old:
#print "A diminuir step:",i
step_size=step_size/2
U=it.getU(w_old,step_size,grad1)
#w_new=[soft(x,step_size*lamb) for x in U.transpose().tolist()[0]]
#w_new=sparse.csr_matrix(w_new)
#w_new=w_new.transpose().transpose())
w_new=it.softt(U,step_size*lamb)
error=(X*w_new)-Y
dif=w_new-w_old
dif=dif.transpose()*dif
y_new=it.get_func_lasso(error,w_new,lamb) #funcao de custo
count=count+1
i=i+1
if count==10:
break
else:
if i==max_iter:
w_new=w_temp
#if count ==5000:
#print "****A SAIR****\nProvavelmente o sparsa chegou ao minimo antes de terminar o numero de iteracoes"
#break
#elif i==max_iter:
#pass
#print "Fim das interacoes"
i=i+1
y_old=y_new
w_old_old=w_old
w_old=w_new
yy.append(y_new)
xx.append(i)
errod=RRegression_beta.erro(Xdev,Ydev,w_new)
gmedia.append(errod)
errot=RRegression_beta.erro(Xteste,Yteste,w_new)
gmediatest.append(errot)
if final[0]>errod or final[0]==0:
final[0]=errod
final[1]=lamb
final[2]=errot
graphFinal=deepcopy(yy)
wfinal=deepcopy(w_new)
yfinal=deepcopy(y_new)
finalxx=deepcopy(xx)
zero=0.0
for J in xrange(w_new.shape[0]):
if w_new[J,0]==0:
zero=zero+1.0
sp=(zero/w_new.shape[0])*100
print "percentagem:",sp
yyy.append(sp)
xxx.append(lamb)
plt.figure(1)
plt.subplot(221)
plt.title("Funcao de custo")
plt.plot(finalxx,graphFinal,"r",finalxx,[1.1959e15 for lolol in xrange(len(finalxx))])
plt.subplot(222)
plt.title("Percentagem de W com valor =0")
import pylab
#print yyy
#pylab.ylim([0,100])
plt.plot(xg,yyy,"b",xg,yyy,"ro")
plt.subplot(223)
plt.title("Erro DEV ao longo dos lambdas")
plt.plot(xg,gmedia,"b",xg,gmedia,"ro")
plt.subplot(224)
plt.title("Erro teste ao longo dos lambdas")
plt.plot(xg,gmediatest,"b",xg,gmediatest,"ro")
#pylab.savefig("lasso_beta.png")
plt.show()
return wfinal,yfinal,final[1]