start_t = time.time()
clf2 = DualKSVM(lmda=C/n, kernelstr='rbf', gm=gamma, nsweep=int(4*n), algo_type='cd')
clf2.fit(x[train_index, :], y[train_index], x[test_index, :], y[test_index])
if C in trainerr_cd:
trainerr_cd[C] = np.vstack((trainerr_cd[C], clf2.err_tr))
testerr_cd[C] = np.vstack((testerr_cd[C], clf2.err_te))
obj_cd[C] = np.vstack((obj_cd[C], clf2.obj))
else:
trainerr_cd[C] = clf2.err_tr
testerr_cd[C] = clf2.err_te
obj_cd[C] = clf2.obj
print "cd svm time %f" % (time.time() - start_t)
start_t = time.time()
clf3 = Pegasos(lmda=C/n, gm=gamma, kernelstr='rbf', nsweep=5, batchsize=1)
clf3.fit(x[train_index, :], y[train_index], x[test_index, :], y[test_index])
if C in trainerr_pega:
trainerr_pega[C] = np.vstack((trainerr_pega[C], clf3.err_tr))
testerr_pega[C] = np.vstack((testerr_pega[C], clf3.err_te))
obj_pega[C] = np.vstack((obj_pega[C], clf3.obj))
else:
trainerr_pega[C] = clf3.err_tr
testerr_pega[C] = clf3.err_te
obj_pega[C] = clf3.obj
print "pegasos time %f" % (time.time() - start_t)
# start_t = time.time()
# clf4 = DualKSVM(lmda=C/n, kernel='rbf', gm=gamma, nsweep=n, rho=rho2, algo_type='scg_da')
# clf4.fit(x[train_index, :], y[train_index], x[test_index, :], y[test_index])
# if C in trainerr_dasvm2:
# trainerr_dasvm2[C] = np.vstack((trainerr_dasvm2[C], clf4.err_tr))
# trainerr_md2[i].append(clf_md[i].err_tr2)
# testerr_md2[i].append(clf_md[i].err_te2)
obj_md2[i].append(clf_md[i].obj2)
print "dualsvm time %f " % (time.time() - start_t)
start_t = time.time()
clf_cd = DualKSVM(lmda=C / n, kernelstr='rbf', gm=gamma, nsweep=int(4 * n), algo_type='cd')
clf_cd.fit(x[train_index, :], y[train_index], x[test_index, :], y[test_index])
trainerr_cd.append(clf_cd.err_tr)
testerr_cd.append(clf_cd.err_te)
obj_cd.append(clf_cd.obj)
print "cd svm time %f" % (time.time() - start_t)
start_t = time.time()
clf_pega = Pegasos(lmda=C / n, gm=gamma, kernelstr='rbf', nsweep=5, batchsize=1)
clf_pega.fit(x[train_index, :], y[train_index], x[test_index, :], y[test_index])
trainerr_pega.append(clf_pega.err_tr)
testerr_pega.append(clf_pega.err_te)
obj_pega.append(clf_pega.obj)
trainerr_pega.append(clf_pega.err_tr)
testerr_pega.append(clf_pega.err_te)
obj_pega.append(clf_pega.obj)
print "pegasos time %f" % (time.time() - start_t)
for i, row in enumerate(rholist):
trainerr_dasvm[i] = np.asarray(trainerr_dasvm[i])
testerr_dasvm[i] = np.asarray(testerr_dasvm[i])
obj_dasvm[i] = np.asarray(obj_dasvm[i])
trainerr_md[i] = np.asarray(trainerr_md[i])
testerr_md[i] = np.asarray(testerr_md[i])
obj_md[i] = np.asarray(obj_md[i])
# x_train = scalar.transform(x_train)
mm_scale = preprocessing.MinMaxScaler(feature_range=(-1, 1))
x_train = mm_scale.fit_transform(x_train)
x_test = mm_scale.transform(x_test)
ntr = x_train.shape[0]
# gm = 1.0/1
gm = 1.0/ntr
# lmda = 1/float(ntr)
lmda = 1000/float(ntr)
print('train dual svm')
dsvm = DualKSVM(lmda=lmda, gm=gm, kernelstr='rbf', nsweep=0.8 * ntr, b=5, c=1)
dsvm.fit(x_train, y_train, x_test, y_test, )
print ('train Pegasos')
kpega = Pegasos(lmda=lmda, gm=gm, kernelstr='rbf', nsweep=3)
kpega.fit(x_train, y_train, x_test, y_test)
clf = svm.SVC(C=lmda*ntr, kernel='rbf', gamma=gm, verbose=True,)
# clf_da = svm.SVC(C=lmda*ntr, kernel='rbf', gamma=gm, fit_intercept=False)
clf.fit(x_train, y_train)
pred = clf.predict(x_test)
err_libsvm = zero_one_loss(pred, y_test)
print "sklearn err %f" % err_libsvm
plt.figure()
plt.plot(dsvm.nker_opers, dsvm.err_tr, 'rx-', label='dc train error')
plt.plot(kpega.nker_opers, kpega.err_tr, 'b.-', label='pegasos train error')
plt.plot(dsvm.nker_opers, dsvm.err_te, 'gx-', label='dc test error')
plt.plot(kpega.nker_opers, kpega.err_te, 'y.-', label='pegasos test error')
plt.xlabel('number of kernel products')
if one_vs_rest:
