def plot_convergence_synthetic(svm, lr):
for i in range(4):
a = data[i]
b = Label[i]
n, m = np.shape(a)
a = np.vstack((a, np.ones((1, m))))
b = b.reshape(-1, 1)
x = np.zeros((3, 1))
a1 = a
b1 = b
if svm:
x_bt, norm_bt, _, acc_bt, duration_bt, diteration_bt = SVM.BackTracking(
a, b, x, args.l, args.d, False, a1, b1)
f_bt = open('svm_acc_bt_' + str(i) + '.txt', mode='a+')
f_bt.write(
np.str(acc_bt) + ' ' + str(duration_bt) + ' ' +
str(diteration_bt))
x_AGM, norm_AGM, _, acc_AGM, duration_AGM, diteration_AGM = SVM.AGM(
a, b, x, args.l, args.d, False, a1, b1)
f_AGM = open('svm_acc_AGM_' + str(i) + '.txt', mode='a+')
f_AGM.write(
np.str(acc_AGM) + ' ' + str(duration_AGM) + ' ' +
str(diteration_AGM))
x_BFGS, norm_BFGS, _, acc_BFGS, duration_BFGS, diteration_BFGS = SVM.G_BFGS(
a, b, x, args.l, args.d, False, a1, b1)
f_BFGS = open('svm_acc_BFGS_' + str(i) + '.txt', mode='a+')
f_BFGS.write(
np.str(acc_BFGS) + ' ' + str(duration_BFGS) + ' ' +
str(diteration_BFGS))
Draw.gradient_plot(len(norm_bt), norm_bt, 'backtracking')
Draw.gradient_plot(len(norm_AGM), norm_AGM, 'AGM')
Draw.gradient_plot(len(norm_BFGS), norm_BFGS, 'BFGS')
plt.savefig('SVM_convergence_' + str(i))
# plt.show()
plt.close()
if lr:
x_AGM, norm_AGM, _, acc_AGM, duration_AGM, diteration_AGM = LR.AGM(
a, b, x, args.l, False, a1, b1)
f_AGM = open('lr_acc_AGM_' + str(i) + '.txt', mode='a+')
f_AGM.write(
np.str(acc_AGM) + ' ' + str(duration_AGM) + ' ' +
str(diteration_AGM))
x_L_BFGS, norm_L_BFGS, _, acc_L_BFGS, duration_L_BFGS, diteration_L_BFGS = LR.L_BFGS(
a, b, x, args.m, args.l, False, a1, b1)
f_L_BFGS = open('lr_acc_LBFGS_' + str(i) + '.txt', mode='a+')
f_L_BFGS.write(
np.str(acc_L_BFGS) + ' ' + str(duration_L_BFGS) + ' ' +
str(diteration_L_BFGS))
Draw.gradient_plot(len(norm_AGM), norm_AGM, 'AGM')
Draw.gradient_plot(len(norm_L_BFGS), norm_L_BFGS, 'L_BFGS')
plt.savefig('lr_convergence_' + str(i))
# plt.show()
plt.close()
def plot_division(svm, lr):
for i in range(4):
a = data[i]
c1 = C1[i]
c2 = C2[i]
b = Label[i]
n, m = np.shape(a)
a = np.vstack((a, np.ones((1, m))))
b = b.reshape(-1, 1)
x = np.zeros((3, 1))
a1 = a
b1 = b
if svm:
path_BT = 'svm_BT_a' + str(i)
path_AGM = 'svm_AGM_a' + str(i)
path_BFGS = 'svm_BFGS_a' + str(i)
x_bt, norm_bt, _, acc_bt, duration_bt, diteration_bt = SVM.BackTracking(
a, b, x, args.l, args.d, False, a1, b1)
x_AGM, norm_AGM, _, acc_AGM, duration_AGM, diteration_AGM = SVM.AGM(
a, b, x, args.l, args.d, False, a1, b1)
x_BFGS, norm_BFGS, _, acc_BFGS, duration_BFGS, diteration_BFGS = SVM.G_BFGS(
a, b, x, args.l, args.d, False, a1, b1)
Draw.plot_division(c1, c2, x_AGM, path_BT, 'svm_BT')
Draw.plot_division(c1, c2, x_AGM, path_AGM, 'svm_AGM')
Draw.plot_division(c1, c2, x_BFGS, path_BFGS, 'svm_BFGS')
if lr:
path_AGM = 'lr_AGM_a' + str(i)
path_LBFGS = 'lr_LBFGS_a' + str(i)
x_AGM, norm_AGM, _, acc_AGM, duration_AGM, diteration_AGM = LR.AGM(
a, b, x, args.l, False, a1, b1)
x_L_BFGS, norm_L_BFGS, _, acc_L_BFGS, duration_L_BFGS, diteration_L_BFGS = LR.L_BFGS(
a, b, x, args.m, args.l, False, a1, b1)
Draw.plot_division(c1, c2, x_AGM, path_AGM, 'AGM')
Draw.plot_division(c1, c2, x_L_BFGS, path_LBFGS, 'LBFGS')
