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06-01 疑似乱数

疑似乱数

01-01. 擬似乱数列の生成

# 01-01. 擬似乱数列の生成
import random
for i in range(100):
    r = random.randrange(1, 1001)
    print(r)
104
493
331
520
829
711
820
784
385
69
69
918
52
638
987
298
432
308
530
211
396
892
985
277
208
918
623
352
125
445
4
827
299
152
673
441
941
838
414
332
418
284
100
420
802
856
442
583
811
451
354
549
517
327
16
109
472
30
543
349
285
312
576
800
807
253
381
574
869
246
809
440
46
670
631
73
112
495
56
483
452
103
597
852
646
496
204
723
443
775
176
541
996
694
328
891
647
690
977
661

01-02. シード

# 01-02. シード
random.seed(1)
for i in range(100):
    r = random.randrange(1, 1001)
    print(r)
138
583
868
822
783
65
262
121
508
780
461
484
668
389
808
215
97
500
30
915
856
400
444
623
781
786
3
713
457
273
739
822
235
606
968
105
924
326
32
23
27
666
555
10
962
903
391
703
222
993
433
744
30
541
228
783
449
962
508
567
239
354
237
694
225
780
471
976
297
949
23
427
858
939
570
945
658
103
191
645
742
881
304
124
761
341
918
739
997
729
513
959
991
433
520
850
933
687
195
311

01-03. シーケンス操作

# 01-03. シーケンス操作
a_list = [23, 22, 32, 12, 31, 30, 3, 35, 26, 36]
# choice
print("# random.choice")
for i in range(30):
    r = random.choice(a_list)
    print(r)
# random.choice
31
36
35
26
3
36
23
35
12
3
3
32
30
26
30
22
35
26
22
32
26
3
30
35
23
35
23
31
36
36

# choices
weights = [0, 1, 1, 1, 1, 1, 1, 1, 1, 10]
print("# random.choices")
for i in range(30):
    r = random.choices(a_list, weights=weights, k=5)
    print(r)
# random.choices
[36, 36, 31, 30, 22]
[31, 36, 36, 26, 35]
[36, 35, 36, 36, 36]
[36, 35, 36, 36, 36]
[36, 12, 36, 31, 36]
[36, 35, 36, 36, 26]
[36, 26, 22, 36, 36]
[3, 36, 36, 36, 36]
[31, 32, 36, 36, 36]
[22, 36, 32, 36, 36]
[36, 3, 30, 36, 31]
[3, 31, 30, 36, 36]
[36, 3, 36, 36, 12]
[3, 35, 36, 30, 30]
[36, 36, 36, 26, 36]
[30, 26, 22, 36, 36]
[36, 26, 36, 36, 36]
[36, 36, 36, 22, 36]
[36, 36, 26, 36, 12]
[31, 22, 32, 32, 36]
[3, 12, 36, 12, 36]
[36, 36, 31, 36, 36]
[36, 36, 36, 36, 22]
[31, 32, 36, 36, 36]
[36, 36, 26, 36, 22]
[36, 26, 3, 12, 36]
[36, 36, 12, 26, 30]
[32, 35, 36, 36, 36]
[36, 36, 36, 32, 22]
[12, 12, 36, 30, 3]

01-04. シーケンス操作 sample

# 01-04. シーケンス操作 sample
a_list = [23, 22, 32, 12, 31, 30, 3, 35, 26, 36]

# sample
print("# random.sample")
for i in range(30):
    r = random.sample(a_list, k=5)
    print(r)
# random.sample
[26, 31, 30, 32, 3]
[22, 31, 12, 3, 26]
[35, 32, 22, 26, 23]
[3, 22, 36, 35, 26]
[32, 30, 22, 31, 3]
[3, 22, 12, 31, 23]
[31, 30, 36, 35, 3]
[22, 35, 31, 23, 3]
[31, 23, 26, 35, 12]
[22, 23, 12, 36, 31]
[3, 32, 22, 12, 35]
[12, 32, 22, 36, 3]
[26, 31, 36, 30, 12]

[30, 22, 12, 36, 32]
[23, 36, 26, 3, 32]
[36, 30, 35, 12, 32]
[3, 22, 26, 32, 31]
[35, 22, 31, 26, 36]
[26, 35, 30, 32, 22]
[26, 12, 31, 22, 3]
[30, 22, 31, 23, 12]
[22, 30, 12, 35, 32]
[23, 30, 32, 35, 31]
[31, 12, 30, 23, 36]
[36, 22, 12, 26, 23]
[12, 3, 22, 32, 31]
[22, 36, 23, 30, 35]
[31, 30, 35, 12, 22]
[22, 26, 30, 23, 31]
[32, 36, 26, 22, 35]

# シャッフル
print("# random.sample シャッフル")
for i in range(30):
    r = random.sample(a_list, k=len(a_list))
    print(r)
# random.sample シャッフル
[31, 22, 36, 26, 3, 30, 23, 35, 12, 32]
[31, 3, 32, 23, 30, 22, 35, 26, 36, 12]
[26, 31, 35, 3, 36, 12, 23, 22, 32, 30]
[31, 35, 23, 3, 30, 12, 26, 36, 22, 32]
[36, 32, 26, 35, 3, 12, 31, 23, 30, 22]
[35, 23, 32, 31, 36, 3, 12, 30, 26, 22]
[30, 35, 26, 22, 36, 12, 32, 31, 3, 23]
[23, 22, 30, 26, 31, 3, 32, 35, 12, 36]
[32, 35, 22, 3, 23, 31, 12, 30, 36, 26]
[3, 12, 23, 26, 30, 36, 32, 22, 35, 31]
[26, 22, 31, 30, 23, 32, 3, 12, 35, 36]
[12, 3, 26, 36, 22, 32, 35, 23, 30, 31]
[22, 3, 26, 23, 30, 32, 31, 35, 36, 12]
[12, 26, 35, 31, 23, 30, 22, 36, 3, 32]
[31, 32, 12, 26, 3, 36, 30, 35, 22, 23]
[26, 30, 35, 12, 23, 22, 3, 36, 31, 32]
[23, 22, 36, 31, 32, 26, 35, 30, 3, 12]
[3, 26, 30, 36, 31, 32, 23, 12, 22, 35]
[30, 31, 3, 32, 36, 26, 12, 23, 22, 35]
[12, 26, 23, 32, 30, 31, 22, 36, 3, 35]
[32, 35, 12, 36, 31, 23, 26, 3, 22, 30]
[30, 22, 35, 36, 26, 32, 23, 3, 12, 31]
[31, 32, 22, 3, 36, 23, 26, 35, 12, 30]
[32, 35, 31, 12, 22, 3, 23, 30, 26, 36]
[22, 30, 36, 26, 12, 23, 35, 32, 31, 3]
[3, 31, 26, 22, 35, 36, 30, 12, 32, 23]
[22, 26, 30, 12, 31, 35, 23, 3, 36, 32]
[30, 12, 3, 31, 26, 22, 36, 35, 32, 23]
[31, 12, 23, 3, 36, 26, 32, 22, 35, 30]
[12, 22, 32, 3, 31, 36, 26, 35, 30, 23]

01-05. 連続確率分布

# 01-05. 連続確率分布

# 一様分布
# random
r_list_uniform_1 = []
for i in range(100):
    r = random.random()
    r_list_uniform_1.append(r)
print("一様分布（0~1）", r_list_uniform_1)
一様分布（0~1） [0.5265923229048832, 0.13861974163698576, 0.13809799323879335, 0.7157497662356598, 0.36108976833344886, 0.7513763114866316, 0.2404936039137613, 0.7181581423147705, 0.7184769263967773, 0.3054958810525106, 0.10638543387964139, 0.3970078551871341, 0.49236150032733617, 0.09997421469778434, 0.18676126036778584, 0.055343052815480465, 0.5975135715550439, 0.8888761233719161, 0.2165577909596218, 0.03471343587681974, 0.7039235944191828, 0.8149105587896851, 0.9641215867338897, 0.6131789568237019, 0.34244316565189636, 0.8378686180306556, 0.11806710521312225, 0.6926369381896267, 0.0952308492516365, 0.3997057470173988, 0.49502288140217887, 0.377894273032341, 0.16859757880447968, 0.2317173126022275, 0.8201499974998944, 0.46257580479248983, 0.5799327447235099, 0.2119070176161595, 0.7149350587865332, 0.33011725914726364, 0.5936185874860408, 0.9094870627958156, 0.9943934088859884, 0.04621794831314552, 0.797442711928691, 0.8575878253608825, 0.3195744372072056, 0.3831476259821177, 0.5802537596763331, 0.9188402309707125, 0.39992859333804187, 0.8800301687734118, 0.7585605282041756, 0.1522730797062255, 0.9136799203638493, 0.015181052589951283, 0.1451782500468748, 0.6648112128866874, 0.05711968663889244, 0.3794898856741835, 0.12997885852693347, 0.4628892738532562, 0.8399803437546011, 0.9060843513491861, 0.03546964032188504, 0.060851756668864554, 0.8406240353653226, 0.0428147832556115, 0.273590265071345, 0.11743671769283648, 0.09103770695709379, 0.027622889724836064, 0.6375130126648525, 0.7446142679398566, 0.6867713765586763, 0.8456227719182262, 0.6630161884986934, 0.38970192767534384, 0.6310630237160113, 0.9695948083687032, 0.6416033330232526, 0.24309173409213014, 0.0601840957099572, 0.9351659997400953, 0.5904954982942084, 0.3496147426104088, 0.6053527496610309, 0.5602575960634735, 0.5221717727865457, 0.06080464202945668, 0.3532275523761348, 0.4126500229395509, 0.199368340608838, 0.880105231228507, 0.4241197773808294, 0.6623856654024448, 0.7135464494458958, 0.7432830602725053, 0.7211152909126985, 0.7522085016390995]

# uniform
r_list_uniform_2 = []
for i in range(100):
    r = random.uniform(5, 10)
    r_list_uniform_2.append(r)
print("一様分布（5~10）", r_list_uniform_2)
一様分布（5~10） [6.257903470753821, 9.882018383464484, 5.7550487689193, 9.593236975496506, 9.272843876037815, 9.260821455899837, 5.26405627418767, 5.456090417219498, 9.06527901116161, 7.3458341323259395, 6.8512659556896285, 9.923437361146787, 5.2005896764482005, 7.657325269028024, 7.216748880753536, 5.641015615143388, 6.975941313929937, 8.53823702405251, 9.41157804601204, 5.123098557316717, 7.622547793015446, 5.451882975176292, 9.001967285775175, 5.428926397183523, 5.170966605085692, 6.921181010386443, 8.6630308725315, 6.566033465237238, 5.650024498265237, 8.972861110425859, 9.034596909475924, 9.27929899386286, 6.518722366320285, 7.124151805094868, 6.226949971271267, 7.7858874650825305, 6.650535833948739, 6.693316679795091, 8.918107092048682, 9.781480800201113, 7.920701596183792, 5.523439650599788, 8.262874663423052, 7.243058589240401, 9.940152785131566, 8.596907475739934, 9.173930532536044, 8.50643130094106, 7.678095028931959, 9.484091959140628, 9.158085323540046, 6.456629438071645, 5.7851594761004375, 6.8517593439384745, 7.605388362862929, 5.4869004491531435, 6.726896432279302, 7.8745283210599, 5.217873092759256, 9.074743382594146, 8.25558522841639, 6.568250857948819, 6.4916049062758425, 6.763080703939102, 6.626443481025715, 8.742568884793766, 7.505284287356263, 7.63064198649913, 5.743782494854583, 9.57209001208863, 6.627864643361668, 6.63782226194106, 5.344230698489165, 9.897057908758978, 7.398489209046295, 9.564423686421119, 9.638086212487417, 9.848760715891707, 9.078146438657564, 9.627216125956563, 9.61144661825029, 9.006838390830925, 5.6729060802134175, 7.618558611429203, 7.8780200650207455, 9.962487639930789, 8.919742749831263, 8.514581083274777, 8.733245184222193, 6.807888820417384, 9.711567789201084, 8.217504448076145, 7.012873042650083, 7.322857886488043, 9.898774636553663, 7.660641987157691, 5.838987679372441, 5.7417749706702494, 8.436210983288738, 7.8138776545750925]

# 正規分布
r_list_Gauss = []
for i in range(100):
    r = random.gauss(0, 1)
    r_list_Gauss.append(r)
print("正規分布", r_list_Gauss)
正規分布 [0.5324375935904874, -0.3530779375782036, -1.3683741456475194, 0.8550825306685746, 0.43469013892015146, 0.14155661457278193, -0.7537828813773354, -0.22263462242443993, 0.6096537715636479, 0.48489767776433806, -0.8248452313401039, -0.9023965058382205, 0.34249730702473463, 0.18409849853991048, 0.8484820527112336, -1.1582125725053838, 0.9214086014444441, 1.7634074969742954, 0.9491413321949335, 0.13112646629094207, 0.9061431298592266, -1.2870797762586406, -0.4434220775745028, 2.0594570576366276, -1.6346161195291298, -1.157718860951095, 0.8228223320168564, -0.6567184327555143, -0.5651032811994183, -1.1194969459729216, 1.682237422911142, -0.6088058499271607, -0.27761469543636275, -1.8130900242341954, 0.7699729664826502, -0.008902331892990233, 0.49864433585385454, 1.5806230586981524, 0.15311291220500362, -1.1919806958263168, -1.0135889886150027, 0.08248995203401366, 1.32075594399839, -1.1988114571728905, -0.24668430582302578, -0.1450480958168968, 0.6574081580897599, -0.8913292640974034, 0.3116957216667728, 0.7838058354177626, -0.037866762244599075, -0.09803420161996611, 0.618793583055987, 0.5814126043641078, 1.2583567956103352, -1.0789656223169575, 1.2304906486400156, -0.22839071358666393, -1.1388952978780043, -0.5532281757233414, -1.2336128003688107, -0.2003717116136265, 1.0340562311015782, -2.2498212769594184, -1.1824327808803452, 0.7682248345284534, -0.30097882883050847, 0.7926079366310617, -1.327513684363958, -0.05272080362551487, -2.606046552863915, -0.8466556917182078, 0.7399065599766491, 1.215481411549136, 1.631408091907619, -0.05608163817060093, -0.8775564670832912, -0.3857148741498997, -1.9181306028911123, 1.3545970556258886, 1.177966182383311, -0.9098131673999311, 1.831576151410436, -1.3528606330079138, 0.5596126918649947, -0.8086752658193437, -1.7382947519171057, 0.3958475718858402, -1.1587062793401812, 1.1936940207218614, -0.8996917306991808, 0.10550901197176209, -0.47897015826125133, 0.1354140612230567, -0.6322787018890805, 0.8204726302534867, 0.6105451410390148, 0.06998263108965841, -0.12692339031175426, 1.9713229451563317]

01-06. 分布のプロット

# 01-06. 分布のプロット
import matplotlib.pyplot as plt
plt.hist(r_list_Gauss)
plt.title("Gaussian distribution")
<Figure size 640x480 with 1 Axes>

ループからの脱出：break文

01-07. ループの中断

# 01-07. ループの中断
for i in range(100):
    print(i)
    if i == 10:
        break
print("ループ終了")
0
1
2
3
4
5
6
7
8
9
10
ループ終了

01-08. ネストされたループの中断

# 01-08. ネストされたループの中断
for i in range(20):
    for j in range(20):
        print(i, ", ", j)
        if j == 5:
            break
0 ,  0
0 ,  1
0 ,  2
0 ,  3
0 ,  4
0 ,  5
1 ,  0
1 ,  1
1 ,  2
1 ,  3
1 ,  4
1 ,  5
2 ,  0
2 ,  1
2 ,  2
2 ,  3
2 ,  4
2 ,  5
3 ,  0
3 ,  1
3 ,  2
3 ,  3
3 ,  4
3 ,  5
4 ,  0
4 ,  1
4 ,  2
4 ,  3
4 ,  4
4 ,  5
5 ,  0
5 ,  1
5 ,  2
5 ,  3
5 ,  4
5 ,  5
6 ,  0
6 ,  1
6 ,  2
6 ,  3
6 ,  4
6 ,  5
7 ,  0
7 ,  1
7 ,  2
7 ,  3
7 ,  4
7 ,  5
8 ,  0
8 ,  1
8 ,  2
8 ,  3
8 ,  4
8 ,  5
9 ,  0
9 ,  1
9 ,  2
9 ,  3
9 ,  4
9 ,  5
10 ,  0
10 ,  1
10 ,  2
10 ,  3
10 ,  4
10 ,  5
11 ,  0
11 ,  1
11 ,  2
11 ,  3
11 ,  4
11 ,  5
12 ,  0
12 ,  1
12 ,  2
12 ,  3
12 ,  4
12 ,  5
13 ,  0
13 ,  1
13 ,  2
13 ,  3
13 ,  4
13 ,  5
14 ,  0
14 ,  1
14 ,  2
14 ,  3
14 ,  4
14 ,  5
15 ,  0
15 ,  1
15 ,  2
15 ,  3
15 ,  4
15 ,  5
16 ,  0
16 ,  1
16 ,  2
16 ,  3
16 ,  4
16 ,  5
17 ,  0
17 ,  1
17 ,  2
17 ,  3
17 ,  4
17 ,  5
18 ,  0
18 ,  1
18 ,  2
18 ,  3
18 ,  4
18 ,  5
19 ,  0
19 ,  1
19 ,  2
19 ,  3
19 ,  4
19 ,  5
基礎編
06 ランダムな現象
基礎編
06-02 ランダムウォーク，ライト-フィッシャー モデル