コード例 #1
0
ファイル: main.py プロジェクト: kane97-ui/Optimization
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()
コード例 #2
0
ファイル: main.py プロジェクト: kane97-ui/Optimization
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')