Active learning for LR

X_test = np.vstack([test_x1, test_x2, test_x3, test_x4, test_x5, test_x6])
y_test = np.array([1.0, 1.0, 1.0, 1.0, -1.0, -1.0])

clf.model.coef_ = clf.model.coef_.reshape(1,clf.model.coef_.shape[0])
# hack to set the classes
try:
	clf.model.fit([],[0,1])
except:
	pass

# gradient update step simulated
alpha = 0.00125
w_u = clf.model.coef_.flatten()
b_u = clf.model.intercept_
for idx in xrange(X_test.shape[0]):
	w_u = w_u - Log.dloss(Log(),clf.model.decision_function(X_test[idx]),y_test[idx])*X_test[idx]*alpha
	b_u = b_u - Log.dloss(Log(),clf.model.decision_function(X_test[idx]),y_test[idx])*alpha

clf2 = LogisticRegression()
clf2.coef_ = w_u.reshape(1,w_u.shape[0])
clf2.intercept_ = b_u
try:
	clf2.fit([],[0,1])
except:
	pass

print "NEW", clf2.predict_proba(X_test)
print "OLD", clf.model.predict_proba(X_test)
print "NEW", clf2.predict(X_test)
print "OLD", clf.model.predict(X_test)
print "NEW", clf2.coef_, clf2.intercept_
print "OLD",clf.model.coef_, clf.model.intercept_