def get_step(w_new,w_old,x,error_old,error_old_old,lamb):
grad_new=grad_beta.get_gradient(error_old,x,w_new,lamb)
grad_old=grad_beta.get_gradient(error_old,x,w_old,lamb)
R=grad_new-grad_old
S=w_new-w_old
#pdb.set_trace()
A=R.transpose()*R
B=R.transpose()*S
if A==0 or B==0:
return 0
else:
xpto=(A)/(B)
xpto=xpto.todense()[0,0]
return xpto
error=(Xtrain*w_old)-Y[3:7,0]
if i==0:
step_size=0.0000001
else:
error_old=error=(Xtrain*w_old)-Y[3:7,0]
error_old_old=(Xtrain*w_old_old)-Y[3:7,0]
alpha=get_step(w_old,w_old_old,Xtrain,error_old,error_old_old,lamb)
if alpha==0:
print "**ERRO**"
print "aplha=0, impossivel continuar o algorimto"
break
step_size=sigma/alpha
#print "fora:",step_size
grad1=grad_beta.get_gradient(error,Xtrain,w_old,lamb)
w_new = w_old - (step_size * grad1)
error=(Xtrain*w_new)-Y[3:7,0]
dif=w_new-w_old
dif=dif.transpose()*dif
y_new=grad_beta.get_func(error,w_new,lamb)[0,0] #funcao de erro
count=0
if i!=0:
while y_new>=y_old-sigma2*alpha*dif[0,0]:
count=count+1
