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
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
if __name__ == '__main__':
dictionary,total,y=cria_dados.read_output("../le_ficheiro/train_meta.txt")
X,Y=cria_dados.criaXY(dictionary,total,y)
X,total=cria_dados.delcomun(X,total)
Y=sparse.csr_matrix(Y)
X=sparse.csr_matrix(X)
vec=sparse.csr_matrix([5 for i in xrange(X.shape[1])])
vec=vec.transpose()
W,F=lasso(X,Y,vec,total)
print "----------erro---------"
print "TESTE",RRegression_beta.erro("../le_ficheiro/test_meta.txt",W,total)
print "-----------------------"
print "TRAIN",RRegression_beta.erro("../le_ficheiro/train_meta.txt",W,total)
print "-----------------------"
print "DEV",RRegression_beta.erro("../le_ficheiro/dev_meta.txt",W,total)