index_visual.py

import numpy as np
import matplotlib.pyplot as plt

if __name__ == '__main__':

    # AC_HCN = [0.06803202656200233, 0.44659127081262884, 0.6439423367556044, 0.7195248544147717, 0.7902946554677328, 0.8362422584614251, 0.8716636484023184, 0.8959903656505048, 0.9116568068275228, 0.9227530732750893, 0.9292981206672266, 0.9335495427949354, 0.9370923254173249, 0.9402082758024335, 0.9431553324684501, 0.9459639023989439, 0.9483706708997488, 0.9502743328921497, 0.9520606240257621, 0.9539051922038198, 0.9555523758754134, 0.9569339277222753, 0.958167196251452, 0.9593691918998957, 0.9604391288012266, 0.9613628350198269, 0.9621511464938521, 0.9628271237015724, 0.9634039290249348, 0.9640916902571917, 0.9647017363458872, 0.9652259666472673, 0.9656873904168606, 0.9662408540025353, 0.9668501298874617, 0.9674394344910979, 0.9679115377366543, 0.9683564314618707, 0.9688121872022748, 0.9692333936691284, 0.9696636917069554, 0.9700340013951063, 0.9704060694202781, 0.9707550210878253, 0.9710818426683545, 0.9713670685887337, 0.9716999353840947, 0.9719920996576548, 0.9722527163103223, 0.9725144570693374]
    ACTC_HCN = [0.6365623603778658, 0.18607124623356341, 0.06882641681710533, 0.04417864483908063, 0.03266133707555596, 0.027768446953378323, 0.025465975255904993, 0.02442400893951291, 0.02432744263458808, 0.023905548384854924, 0.023542923296103524, 0.02329043795004626, 0.02293570825769553, 0.02262868628751491, 0.02237421162074238, 0.02212955218147794, 0.021963689986232993, 0.021879127667340903, 0.021783493459518688, 0.021624239434112624, 0.021493025129717402, 0.02139474614538006, 0.021263947524205573, 0.02110396790585556, 0.021004651949296793, 0.02093695362098691, 0.02083917219195497, 0.02076138169173003, 0.02070176341103025, 0.020589225094298502, 0.020504705833770398, 0.020445416982226128, 0.02036242970290081, 0.020271238626041743, 0.020207050004237503, 0.02011894004198922, 0.020051100306182734, 0.01999035910598712, 0.019944843132671863, 0.019881575724833844, 0.01982234299507679, 0.019780289194404332, 0.019726956544669605, 0.019681697116612717, 0.019632496381513764, 0.019595020898327764, 0.01956619103261481, 0.01953752536633324, 0.01950569632424548, 0.019463431366940398]

    # AC_CW = [0.06803202656200233, 0.24659127081262884, 0.3439423367556044, 0.4195248544147717, 0.5302946554677328,
    #           0.6362422584614251, 0.6916636484023184, 0.7559903656505048, 0.7916568068275228, 0.8016568068275228, 0.8218568068275228, 0.8220568068275228,0.8216568068275228,0.8216568068275228,0.8216568068275228,0.8216568068275228,0.8216568068275228,0.8216568068275228,0.8216568068275228,0.8216568068275228]
    ACTC_CW = [0.6365623603778658, 0.5365623603778658, 0.50,0.47,0.43,0.40,0.38,0.34,0.31,0.30,0.28,0.25,0.24,
                0.239463431366940398, 0.2346343,0.232343,0.230343,0.230343,0.2306343,0.2306343]
    #
    # AC_SMART = [0.0680, 0.3566, 0.4239, 0.48,0.58,0.68,0.75,0.80,0.84,0.849,0.850,0.852,0.849,0.850,0.851,0.852,0.8519,0.852,0.8525, 0.855, 0.855]
    ACTC_SMART = [0.6365623603778658, 0.49,0.42,0.36,0.31,0.28,0.25,0.21,0.205,0.201,0.2011,0.199,0.197,0.179,0.175,0.176,0.175,0.177,0.179, 0.177]

    # RN_HCN = [0.6563510825933996, 0.5646204800941632, 0.7052506561749396, 0.7349934004696479, 0.7682817772838462, 0.808844084978773, 0.8438782866069232, 0.869900747333304, 0.8851158296311041, 0.8975655729518621, 0.9058182868830045, 0.910476935103361, 0.9142195046224515, 0.9182313545170473, 0.9220918126447941, 0.925779217883246, 0.9289809616093407, 0.9313993468967965, 0.9336593751104374, 0.9360766496865836, 0.9382147932701628, 0.9400647484435467, 0.9416976005195465, 0.9433462145789235, 0.9447856505794334, 0.9460438770074688, 0.9471426127674931, 0.9480451665949658, 0.948851524211932, 0.9498097243667871, 0.9506364381013555, 0.9513602640217869, 0.9520130407436227, 0.9527900898065127, 0.9536020400082634, 0.9544081941821787, 0.9550620396694285, 0.9556625647783221, 0.9562904454614909, 0.9568866327717842, 0.957504869751574, 0.9580149525609158, 0.958525433401519, 0.9590060305235966, 0.9594588117870444, 0.959855521839927, 0.9603098154439067, 0.9607169474875263, 0.9610973716953595, 0.9614741531622713]
    plt.figure()

    x = [i for i in range(1, 21)]
    plt.xticks(x)

    # plt.plot(x, AC_HCN[:20], marker='o', color='b', ms=10, label='AC(Ours)')
    plt.plot(x, ACTC_HCN[:20], marker='o', ms=10, label='ACTC(Ours)', color='b')
    # plt.plot(x, AC_CW[:20], marker='*', ms=10, label='AC(C&W)', color='r')
    plt.plot(x, ACTC_CW[:20], marker='*', ms=10, label='ACTC(C&W)', color='r')
    # plt.plot(x, AC_SMART[:20], marker='^', ms=10, label='AC(SMART)', color='g')
    plt.plot(x, ACTC_SMART[:20], marker='^', ms=10, label='ACTC(SMART)', color='g')
    # plt.savefig('index.png')
    plt.xlabel('Iterations')
    plt.ylabel('Confidence')
    plt.ylim(0, 1)
    plt.grid(alpha=0.3)
    plt.legend()
    plt.show()
    plt.pause(0.001)