add graph part

2023-08-13 23:54:41 +08:00
parent 9ae9d9dfdc
commit 0a7087353d
2 changed files with 323 additions and 0 deletions
--- a/graph/fitness-time/neatpython.csv
+++ b/graph/fitness-time/neatpython.csv
@@ -0,0 +1,301 @@
 mean_totaltime,Average Fitness
 16.23358095,-1531.406262
 34.16262472,-1469.014171
 52.56341431,-1429.524865
 68.70164006,-1398.485718
 87.61529462,-1369.068788
 105.4480726,-1338.810888
 120.3260972,-1308.414005
 134.2059872,-1280.022147
 148.1975725,-1258.681994
 161.0129987,-1248.612688
 174.4836712,-1242.987934
 187.5842981,-1245.195745
 201.7285712,-1244.295623
 217.0596706,-1241.958828
 233.1745317,-1242.23715
 249.9489457,-1240.606505
 265.2109861,-1241.584935
 282.1818033,-1242.254389
 301.1290779,-1240.280126
 317.5243011,-1238.729867
 334.9861856,-1238.403718
 354.7175949,-1236.6442
 373.5419451,-1238.018048
 391.36524,-1236.910213
 410.9625152,-1235.673599
 430.0237711,-1234.652077
 449.6226187,-1236.544195
 468.2591698,-1233.919256
 489.2314327,-1236.904311
 510.1973371,-1236.444161
 530.6520383,-1234.882298
 552.1866553,-1236.077152
 574.0524314,-1235.339124
 595.665635,-1233.053687
 618.1490554,-1235.346718
 640.9171209,-1234.058986
 663.9380282,-1233.082789
 687.5950083,-1233.227056
 711.6376721,-1232.342045
 737.2806357,-1232.589748
 762.8286344,-1229.058886
 787.2488623,-1233.670237
 812.7839067,-1232.203953
 837.8952895,-1231.658721
 864.4482835,-1232.48289
 891.955594,-1230.740013
 920.0526731,-1229.984369
 949.259579,-1230.266798
 980.3165732,-1228.807429
 1010.551222,-1229.953455
 1042.59788,-1230.538116
 1073.075853,-1228.559482
 1106.577571,-1229.979631
 1139.959677,-1229.714391
 1173.958232,-1228.932949
 1209.781649,-1226.865015
 1245.958032,-1227.553255
 1281.796618,-1228.989607
 1321.521287,-1227.193127
 1361.788322,-1227.00891
 1401.135148,-1226.937434
 1440.664442,-1228.277831
 1480.485734,-1226.27974
 1523.433305,-1224.812807
 1566.113366,-1226.587234
 1610.083268,-1227.333781
 1654.933877,-1227.395313
 1700.679548,-1223.551766
 1747.142431,-1227.437868
 1794.300387,-1225.468609
 1840.876058,-1225.929573
 1888.193045,-1227.768213
 1936.7367,-1224.390808
 1984.760717,-1227.475326
 2033.411831,-1223.673373
 2084.356429,-1223.815094
 2135.077312,-1224.983295
 2186.35972,-1224.084667
 2238.44057,-1226.530143
 2292.238697,-1223.684731
 2345.740624,-1224.418812
 2399.445412,-1224.068882
 2453.074848,-1223.038722
 2506.191465,-1224.688486
 2560.467831,-1224.369871
 2615.123451,-1223.741346
 2669.90867,-1224.06301
 2723.421276,-1223.571537
 2777.560479,-1221.698283
 2832.592864,-1223.216613
 2887.017148,-1223.993198
 2941.483496,-1224.1121
 2995.423772,-1223.169763
 3050.948137,-1223.459228
 3106.069071,-1223.111025
 3159.795594,-1224.609992
 3214.050797,-1223.487228
 3269.429128,-1223.715971
 3324.581811,-1223.355198
 3380.680088,-1223.137084
 3436.805942,-1222.142975
 3492.414253,-1222.925149
 3549.505774,-1223.379747
 3607.501727,-1222.272351
 3664.639311,-1224.368308
 3722.034017,-1223.901903
 3779.537015,-1222.94451
 3836.6152,-1221.838935
 3894.218441,-1223.663879
 3951.011881,-1224.02154
 4007.885495,-1223.465968
 4063.988944,-1222.439965
 4122.273734,-1221.786334
 4179.429916,-1222.157602
 4237.573861,-1223.250346
 4295.552148,-1222.545104
 4353.574564,-1222.514246
 4411.656075,-1222.108242
 4468.160373,-1222.456373
 4523.770186,-1221.893127
 4578.243049,-1222.369885
 4633.660001,-1221.74155
 4689.995503,-1221.485722
 4745.046551,-1222.821859
 4799.297272,-1220.567495
 4855.528112,-1221.962994
 4912.181782,-1223.706512
 4968.106065,-1223.249642
 5023.349784,-1223.88398
 5077.95836,-1221.843378
 5132.109917,-1220.167506
 5185.437263,-1222.633295
 5239.11293,-1220.070837
 5292.425083,-1222.163853
 5344.871339,-1221.905685
 5398.5643,-1221.195668
 5453.522891,-1220.820716
 5510.54199,-1221.871458
 5565.892794,-1221.127944
 5619.692913,-1221.370239
 5672.505697,-1220.411562
 5726.742534,-1219.956466
 5780.314767,-1223.574739
 5831.786025,-1220.038731
 5883.706538,-1222.285694
 5935.963855,-1219.789742
 5990.031264,-1220.348486
 6042.056406,-1221.695037
 6094.931383,-1222.573256
 6149.866284,-1220.799972
 6201.410016,-1223.62492
 6254.863233,-1221.173897
 6307.299802,-1218.384144
 6358.419653,-1221.949964
 6410.39371,-1220.695003
 6462.294865,-1221.354209
 6514.29837,-1220.013649
 6566.028445,-1221.057066
 6619.112563,-1220.250728
 6671.586666,-1220.74064
 6724.056905,-1220.696191
 6778.867936,-1221.342228
 6834.066508,-1220.67166
 6887.848139,-1221.681324
 6940.725238,-1221.786548
 6993.728177,-1220.293248
 7047.068145,-1220.784974
 7101.058736,-1221.296277
 7155.695031,-1220.314099
 7211.131008,-1220.001403
 7264.672096,-1222.582639
 7319.855532,-1220.218512
 7373.416443,-1222.413372
 7426.826476,-1221.740957
 7481.033519,-1220.12891
 7535.098597,-1220.954482
 7589.86269,-1221.591681
 7645.189188,-1219.780434
 7700.019942,-1219.86627
 7754.408913,-1221.254077
 7809.17354,-1220.560716
 7863.955751,-1219.303522
 7919.377039,-1220.023321
 7973.91837,-1221.021169
 8030.191454,-1221.020316
 8086.216218,-1219.728618
 8143.879354,-1221.815614
 8200.316735,-1219.043831
 8257.940883,-1220.157413
 8314.852015,-1220.684859
 8370.81427,-1220.370599
 8428.692646,-1219.714431
 8484.287711,-1218.305622
 8537.958919,-1221.428118
 8594.432063,-1219.59307
 8649.300665,-1220.87185
 8704.677604,-1221.031256
 8760.582589,-1218.94684
 8818.110156,-1221.658566
 8874.317387,-1219.76819
 8931.379297,-1221.200231
 8988.303494,-1220.058048
 9045.237712,-1219.77335
 9102.797204,-1220.183627
 9158.2346,-1220.408912
 9213.641003,-1218.583548
 9270.128289,-1221.096059
 9325.747626,-1220.0666
 9381.558162,-1219.557564
 9438.852373,-1219.330095
 9495.594231,-1221.993273
 9553.896612,-1223.187594
 9611.398788,-1219.720465
 9670.280587,-1218.745421
 9727.735437,-1220.320216
 9785.576854,-1218.623738
 9842.847055,-1220.192794
 9900.27451,-1218.631224
 9960.404387,-1219.626358
 10020.38233,-1218.897498
 10081.03875,-1220.686493
 10142.4862,-1222.619642
 10203.81352,-1221.138795
 10263.73111,-1219.75704
 10322.01157,-1221.510761
 10383.70518,-1220.942931
 10444.83872,-1220.590052
 10505.46391,-1220.915548
 10565.21068,-1220.83712
 10623.1464,-1220.426405
 10681.92922,-1221.087758
 10740.52835,-1219.740644
 10799.10282,-1219.464613
 10858.83727,-1218.971765
 10917.77249,-1221.522595
 10978.69096,-1219.90022
 11039.15463,-1219.721981
 11100.71659,-1218.736818
 11160.92038,-1218.613676
 11223.41109,-1220.718136
 11286.18113,-1217.805265
 11347.3255,-1221.471473
 11409.22849,-1218.647645
 11471.5558,-1218.650381
 11534.54889,-1221.838598
 11596.56306,-1216.725289
 11658.5901,-1220.448722
 11720.84754,-1218.200733
 11781.86972,-1219.514898
 11844.25683,-1219.122911
 11905.98746,-1219.437825
 11967.72706,-1220.671814
 12029.60287,-1221.425081
 12090.42101,-1220.465861
 12151.72214,-1218.325056
 12215.04968,-1221.885592
 12276.16149,-1217.636273
 12335.90203,-1218.246472
 12396.63493,-1218.821679
 12459.444,-1220.249942
 12522.88157,-1218.853892
 12585.31227,-1220.934625
 12647.22732,-1219.792465
 12707.95382,-1219.762632
 12770.33686,-1217.819044
 12833.20918,-1218.993567
 12896.81557,-1219.114086
 12958.97821,-1219.069654
 13023.42444,-1219.832851
 13086.90235,-1220.588547
 13149.95939,-1217.843585
 13214.8704,-1218.933695
 13278.31332,-1218.540474
 13342.29491,-1219.083535
 13404.31573,-1221.076755
 13465.29136,-1220.365732
 13526.70887,-1217.986482
 13590.52374,-1219.40228
 13654.7832,-1217.818409
 13718.30046,-1218.816018
 13781.31456,-1220.176623
 13843.58471,-1218.819039
 13905.20301,-1218.231392
 13967.45721,-1219.155412
 14030.04214,-1219.768839
 14092.84348,-1219.087143
 14155.60131,-1217.773162
 14218.77685,-1218.442359
 14281.016,-1219.142937
 14346.43126,-1218.540456
 14409.63096,-1220.27516
 14475.35352,-1220.047078
 14541.72266,-1217.934667
 14605.84911,-1219.121418
 14673.27209,-1219.250748
 14737.62863,-1220.368594
 14802.66072,-1220.611695
 14867.97276,-1221.085498
 14935.65309,-1216.312149
 15001.18007,-1218.441571
--- a/graph/fitness-time/script.py
+++ b/graph/fitness-time/script.py
@@ -0,0 +1,22 @@
 from matplotlib import pyplot as plt
 import numpy as np
 # 使用 genfromtxt 函数读取 CSV 文件
 data = np.genfromtxt('neatpython.csv', delimiter=',', skip_header=1)  # 假设有一个头部行
 mean_time, fitness_mean = data.T
 fig, ax = plt.subplots()
 ax.plot(mean_time, fitness_mean, color='green', label='NEAT-Python', linestyle=':')
 ax.set_xlabel('Time (s)')
 ax.set_ylabel('Average Fitness')
 ax.set_xlim(0, 500)
 ax.set_ylim(-2000, -1000)
 ax.legend()
 # ci = 1.96 * neatax_sem
 # lower_bound = neatax_mean - ci
 # upper_bound = neatax_mean + ci
 # plt.plot(mean_time, fitness_mean, color='r', label='NEAT-Python')
 # plt.fill_between(x_axis, lower_bound, upper_bound, color='red', alpha=0.2)
 fig.show()