In [20]:
from gensim import corpora, models, similarities
from collections import defaultdict
import gensim
from bs4 import BeautifulSoup
#dictionary for probability table
table = {}
#All Ids file
file01 = open('cu_IDs.txt', 'r')
ID = file01.readlines()
#Doctorates and Divisions file
file02 = open('cu_doc_div.txt', 'r')
dict_div = file02.readlines()
#20% arrays convertion to vectors
doc001 = ["NULL"]
doc002 = ["NULL"]
doc003 = ["NULL"]
doc004 = ["NULL"]
doc005 = ["NULL"]
#Probability arrays and holder
box = []
box1 = ["NULL"]
box2 = ["NULL"]
box3 = ["NULL"]
box4 = ["NULL"]
box5 = ["NULL"]
#increment variable for 20% arrays
a = 0
b = 0
c = 0
d = 0
e = 0
f = 0
num = 0
num1 = 0
count = 0
# 20% abstract files
file1 = open('cu_test_doc1.txt', 'r')
doc1 = file1.readlines()
file2 = open('cu_test_doc2.txt', 'r')
doc2 = file2.readlines()
file3 = open('cu_test_doc3.txt', 'r')
doc3 = file3.readlines()
file4 = open('cu_test_doc4.txt', 'r')
doc4 = file4.readlines()
file5 = open('cu_test_doc5.txt', 'r')
doc5 = file5.readlines()
# 20% Directorate/Division files
file10 = open('cu_test_dir_div1.txt', 'r')
dd1 = file1.readlines()
file20 = open('cu_test_dir_div2.txt', 'r')
dd2 = file2.readlines()
file30 = open('cu_test_dir_div3.txt', 'r')
dd3 = file3.readlines()
file40 = open('cu_test_dir_div4.txt', 'r')
dd4 = file4.readlines()
file50 = open('cu_test_dir_div5.txt', 'r')
dd5 = file5.readlines()
#Load 80% Dictionary
dictionary1 = corpora.Dictionary.load('/tmp/cu_dict1.dict')
dictionary2 = corpora.Dictionary.load('/tmp/cu_dict2.dict')
dictionary3 = corpora.Dictionary.load('/tmp/cu_dict3.dict')
dictionary4 = corpora.Dictionary.load('/tmp/cu_dict4.dict')
dictionary5 = corpora.Dictionary.load('/tmp/cu_dict5.dict')
#Load 80% Corpus
corpus1 = corpora.MmCorpus('/tmp/cu_corpus1.mm')
corpus2 = corpora.MmCorpus('/tmp/cu_corpus2.mm')
corpus3 = corpora.MmCorpus('/tmp/cu_corpus3.mm')
corpus4 = corpora.MmCorpus('/tmp/cu_corpus4.mm')
corpus5 = corpora.MmCorpus('/tmp/cu_corpus5.mm')
#Load 80% lda model
lda1 = models.LdaModel.load('/tmp/CuModel_1.lda')
lda2 = models.LdaModel.load('/tmp/CuModel_2.lda')
lda3 = models.LdaModel.load('/tmp/CuModel_3.lda')
lda4 = models.LdaModel.load('/tmp/CuModel_4.lda')
lda5 = models.LdaModel.load('/tmp/CuModel_5.lda')
for x in range(20):
if num == 0:
doc001[0] = dictionary1.doc2bow(doc1[num].lower().split())
doc002[0] = dictionary2.doc2bow(doc2[num].lower().split())
doc003[0] = dictionary3.doc2bow(doc3[num].lower().split())
doc004[0] = dictionary4.doc2bow(doc4[num].lower().split())
num += 1
else:
holder1 = dictionary1.doc2bow(doc1[num].lower().split())
doc001.append(holder1)
holder2 = dictionary2.doc2bow(doc2[num].lower().split())
doc002.append(holder2)
holder3 = dictionary3.doc2bow(doc3[num].lower().split())
doc003.append(holder3)
holder4 = dictionary4.doc2bow(doc4[num].lower().split())
doc004.append(holder4)
num += 1
for y in range(18):
if num1 == 0:
doc005[0] = dictionary5.doc2bow(doc5[num1].lower().split())
num1 += 1
else:
holder5 = dictionary5.doc2bow(doc5[num1].lower().split())
doc005.append(holder5)
num1 += 1
for n in range(20):
if a == 0:
D = doc001[0] #0-19 --> 20
box1[0] = lda1[D]
a += 1
else:
box1.append(lda1[doc001[n]])
for n in range(20):
if b == 0:
D = doc002[0] #0-19 --> 20
box2[0] = lda2[D]
b += 1
else:
box2.append(lda2[doc002[n]])
for n in range(20):
if c == 0:
D = doc003[0] #0-19 --> 20
box3[0] = lda3[D]
c += 1
else:
box3.append(lda3[doc003[n]])
for n in range(20):
if d == 0:
D = doc004[0] #0-19 --> 20
box4[0] = lda4[D]
d += 1
else:
box4.append(lda4[doc004[n]])
for n in range(18):
if e == 0:
D = doc005[0] #0-17 --> 18
box5[0] = lda5[D]
e += 1
else:
box5.append(lda5[doc005[n]])
box = box1 + box2 + box3 + box4 + box5
for probs in range(98):
table[ID[probs]] = box[probs]
for k,v in table.items():
count += 1
if count == 1:
print('**********Model 1 Start**********\n')
print(str(count)+')','xml ID:',k,'\n',v,'\n')
if count == 20:
print('**********Model 2 Start**********\n')
if count == 40:
print('**********Model 3 Start**********\n')
if count == 60:
print('**********Model 4 Start**********\n')
if count == 80:
print('**********Model 5 Start**********\n')
if count == 98:
print('***********End of Output*********')
**********Model 1 Start**********
1) xml ID: 1560300
[(1, 0.018493223286796322), (5, 0.12981422701426032), (7, 0.086648201241953771), (8, 0.06616965771687576), (9, 0.14541191651913871), (11, 0.096875319853268888), (12, 0.036199620475446767), (14, 0.08374632460320286), (16, 0.059626370384143333), (17, 0.051997433596439109), (18, 0.15444493110296462), (21, 0.0671517215734381)]
2) xml ID: 1602451
[(0, 0.082788552760726594), (2, 0.074625782091336856), (4, 0.073290397088433254), (5, 0.086168421578294865), (7, 0.024496827008192973), (8, 0.19599396585550929), (10, 0.059887348659282393), (11, 0.068909190188960467), (14, 0.020898281946553184), (15, 0.017180424483999934), (16, 0.053382052968887234), (17, 0.076002983550000611), (22, 0.15636283310982005)]
3) xml ID: 1541944
[(0, 0.064934778225378742), (1, 0.058780308400554634), (2, 0.061723346615090983), (3, 0.018558299303078663), (5, 0.020291553099124102), (9, 0.019777567816819622), (10, 0.12790661783127216), (12, 0.022410613579960978), (13, 0.08919518614241069), (15, 0.034419384221810165), (17, 0.051680200439563215), (18, 0.22002443121888646), (20, 0.07137283316925315), (22, 0.070933994980360252), (23, 0.020575058079742102), (24, 0.045285649361609684)]
4) xml ID: 1638888
[(0, 0.017601317172368222), (2, 0.031451545537107398), (3, 0.039256711692317078), (7, 0.35410527503866951), (8, 0.026035404189301073), (11, 0.13973402393235906), (13, 0.051361767781715757), (15, 0.12740327145501004), (16, 0.099390069417467114), (20, 0.035047582321176274), (22, 0.074724142573181065)]
5) xml ID: 1643020
[(0, 0.0217005640379619), (7, 0.23406441404999684), (8, 0.11966057677880509), (9, 0.034769491083365335), (10, 0.033391558423341586), (11, 0.015285665832784436), (12, 0.04191986709459021), (13, 0.041200635137574763), (16, 0.062144759113187405), (17, 0.039553146186638785), (21, 0.074706978763511941), (22, 0.18621255196102093), (23, 0.090541306688118744)]
6) xml ID: 1646691
[(0, 0.020494425546057331), (2, 0.042286857957489425), (7, 0.48558701325605924), (11, 0.065474221862856785), (12, 0.012847086475035534), (13, 0.10389296407086653), (14, 0.080018057708650586), (15, 0.097959378512630244), (22, 0.084842056465266311)]
7) xml ID: 1600874
[(0, 0.021876355836898183), (2, 0.023249154563764623), (4, 0.0137152636566893), (6, 0.13070421805492582), (8, 0.054181972442962446), (10, 0.074181290276168196), (11, 0.21411668402256498), (13, 0.071547114238154616), (14, 0.073677739846957191), (15, 0.017839884331989964), (16, 0.011485613298339104), (17, 0.011083119096763301), (20, 0.017950019774999479), (21, 0.12845189386110686), (22, 0.071807944992887687), (24, 0.06179406936685472)]
8) xml ID: 1601983
[(2, 0.11292580515030617), (4, 0.039600691190390606), (5, 0.18176014498596388), (6, 0.013379295404805518), (8, 0.11753485087633142), (11, 0.12573543686037042), (14, 0.032663554536590013), (15, 0.015687720169856992), (16, 0.05704286080252445), (20, 0.011004176998568985), (21, 0.042880579253448278), (22, 0.18627253565037555), (24, 0.05358895458660063)]
9) xml ID: 1507266
[(1, 0.07943137747474327), (4, 0.1721740298946135), (5, 0.070537126200026806), (6, 0.012565833215795026), (9, 0.051472129886673615), (11, 0.12799430561737107), (13, 0.043466251352410598), (14, 0.11345424417581873), (15, 0.11973344789795651), (16, 0.043463591786984911), (17, 0.032953509035916595), (19, 0.03987759688254465), (20, 0.088552232254051275)]
10) xml ID: 1563426
[(1, 0.021646446289378323), (2, 0.070178083158042706), (3, 0.1355242290576078), (4, 0.1466430004756665), (7, 0.063171225182893545), (9, 0.053961335224007846), (14, 0.13577665586286461), (17, 0.13921537208170109), (18, 0.044863022546234567), (20, 0.046083262041734283), (21, 0.06479725508540353), (22, 0.056869179858236134)]
11) xml ID: 1556563
[(2, 0.016122970236372131), (4, 0.016761191366342527), (5, 0.060117363305370895), (7, 0.024788318818330914), (8, 0.013621108877869839), (9, 0.095944516260217119), (12, 0.25148539067728654), (14, 0.19012799862773902), (17, 0.031377638584708108), (21, 0.058852102608339035), (22, 0.18632425368813471), (23, 0.026897882340594315)]
12) xml ID: 1642542
[(0, 0.01777084005523448), (2, 0.032036436853672877), (3, 0.11117140568002805), (7, 0.15781976539289455), (8, 0.01869528776796334), (10, 0.13790168028814595), (11, 0.011474017139722938), (12, 0.032966082538224049), (13, 0.12600579706081161), (15, 0.028646960273062412), (16, 0.062118346244833651), (17, 0.051306390841417776), (20, 0.017943942690741246), (22, 0.15204267778983976), (23, 0.039415805624689715)]
13) xml ID: 1629934
[(3, 0.10673784549661149), (7, 0.2210598335704656), (8, 0.011871266004737863), (9, 0.029034296561906994), (11, 0.11674223529179874), (15, 0.13846243054213145), (16, 0.095771029891415768), (17, 0.10259281943423006), (22, 0.1452405335549235), (23, 0.019639665371442881)]
14) xml ID: 1642102
[(2, 0.083056904032182244), (3, 0.096998142727970624), (7, 0.18991835117852743), (8, 0.11395980959405297), (9, 0.034079808336847567), (10, 0.028329754705740947), (11, 0.017555148556224944), (12, 0.099839539028717839), (14, 0.013827243787302769), (15, 0.13701360943967322), (17, 0.036328529429493335), (18, 0.028459699301694546), (22, 0.10194490658964132), (23, 0.015735533157420632)]
15) xml ID: 1544910
[(0, 0.038808989951221083), (1, 0.086488643747942226), (3, 0.088628316948092589), (5, 0.020311100018608849), (8, 0.037063699113244954), (10, 0.053749832411475011), (11, 0.033710141023025046), (13, 0.16970783257856467), (15, 0.011449768163734379), (16, 0.017892398757228926), (18, 0.15746163694225365), (19, 0.020566058332497906), (20, 0.033346207822882973), (21, 0.019380680733693856), (24, 0.20943469345523821)]
16) xml ID: 1638321
[(1, 0.019546407710112942), (2, 0.020419922301668284), (3, 0.083297739371170687), (6, 0.045263005858596501), (7, 0.054367877905549117), (8, 0.20955979124394153), (13, 0.072746007815438246), (15, 0.095493702114158252), (17, 0.24590414234521032), (21, 0.031056356596828334), (22, 0.057282763845618999), (23, 0.041813716596136391)]
17) xml ID: 1547399
[(0, 0.030523646867799535), (1, 0.076616536590871143), (4, 0.020974339815579435), (7, 0.23156198843134132), (9, 0.099528038446444894), (10, 0.060725726982130433), (13, 0.17425382635910633), (15, 0.026638124586326039), (16, 0.041432364727482449), (18, 0.17084495120843685), (24, 0.062420455983807931)]
18) xml ID: 1566346
[(0, 0.084092480639082839), (2, 0.21485840085145225), (5, 0.096702824779404159), (6, 0.017613765777534204), (8, 0.18578293148559885), (10, 0.05965016154894448), (11, 0.061865367858281686), (14, 0.01345045663978715), (15, 0.039634341089667895), (16, 0.10221349807376741), (22, 0.045611454780804084), (24, 0.074461816475305212)]
19) xml ID: 1531607
[(0, 0.032554144959252081), (1, 0.026057194102208497), (2, 0.017388019214215827), (3, 0.027637721980206802), (9, 0.16039092643094605), (10, 0.20394205022768053), (13, 0.19601332611648525), (17, 0.028737352216676466), (18, 0.12445267611316063), (21, 0.031431479270324948), (23, 0.089337263984201665), (24, 0.018414306884163824)]
20) xml ID: 1555224
[(1, 0.049462643022406765), (3, 0.066464160416621898), (4, 0.020534224445834186), (5, 0.030837349934842002), (8, 0.096149595175269692), (9, 0.15465422228539685), (11, 0.011031395578559007), (14, 0.20668168549398264), (16, 0.054902213321067539), (17, 0.022498781727470319), (18, 0.053154208397397269), (20, 0.026961955459773011), (21, 0.13078086094180608), (22, 0.050405592483926835)]
**********Model 2 Start**********
21) xml ID: 1531041
[(3, 0.023425645880654395), (4, 0.26118328059243184), (5, 0.066200409830528009), (8, 0.050476066164397086), (9, 0.041886681043727128), (12, 0.043788821715676475), (14, 0.14644992084473815), (15, 0.011223002959353193), (16, 0.025299295208987733), (17, 0.10253577736714145), (19, 0.059863648279045549), (20, 0.050264385455303623), (21, 0.055926650296592884), (22, 0.017735094334567072), (24, 0.041193549325834855)]
22) xml ID: 1527165
[(0, 0.017558797254965244), (2, 0.03549816086364542), (4, 0.31710716509661907), (8, 0.047804733808605213), (10, 0.046097568390246466), (11, 0.058835325492118555), (13, 0.047622868184670063), (14, 0.11039720257014715), (15, 0.05420231592641394), (20, 0.036629627211733573), (22, 0.048439098988824043), (24, 0.17524573270238641)]
23) xml ID: 1624705
[(0, 0.025876097448255763), (1, 0.028463129822725915), (2, 0.22219045497698783), (4, 0.045317236154379957), (5, 0.10139929337294509), (6, 0.045983225407897593), (8, 0.054481803473232358), (10, 0.01044995653261337), (14, 0.10737731596692027), (16, 0.07556823668116025), (17, 0.10248967732424395), (22, 0.097455746405794977), (24, 0.079966459972864792)]
24) xml ID: 1551605
[(1, 0.030836656070586419), (3, 0.050813512889751535), (7, 0.10974097892567845), (8, 0.20533204594133442), (9, 0.31646737647022966), (12, 0.12252935293767628), (14, 0.029718347201437317), (16, 0.017257576813588922), (20, 0.010930615170094315), (22, 0.088136262340372779), (24, 0.013757275238785617)]
25) xml ID: 1660329
[(0, 0.024535109656286382), (2, 0.010910790538561367), (5, 0.024731500018693505), (8, 0.058922926276572626), (10, 0.078325776765957242), (11, 0.032430055092799039), (13, 0.085048882626635278), (14, 0.054846198551923922), (15, 0.18884616153111), (16, 0.21379880058569972), (18, 0.052447795624577243), (20, 0.076015659592933327), (21, 0.026451587209953355), (22, 0.040991846486993576), (23, 0.029437022435438741)]
26) xml ID: 1640664
[(0, 0.015102760161458003), (7, 0.14891414098193195), (8, 0.17261481848639643), (9, 0.050439522214447627), (10, 0.047815944966517566), (11, 0.012170567574416914), (12, 0.050004493892076542), (13, 0.020388515414939905), (15, 0.18368872992923985), (17, 0.065120145174124219), (23, 0.22845734233597109)]
27) xml ID: 1512342
[(1, 0.020326276754558122), (2, 0.024271442476855379), (5, 0.1008798290124976), (6, 0.016744127022923702), (8, 0.047277454367132994), (11, 0.26175085008569088), (13, 0.034498621472922229), (14, 0.059540371938447363), (15, 0.075424204196480396), (20, 0.2348275604331134), (22, 0.016176684307798794), (24, 0.10443072607928547)]
28) xml ID: 1611714
[(0, 0.076063308034052132), (1, 0.017369694633960719), (2, 0.18742787633984606), (4, 0.029737052122659452), (5, 0.084636815506376362), (8, 0.078429918563374904), (11, 0.039056288209334614), (15, 0.20792403479445887), (20, 0.066366076144758424), (21, 0.072420373994359105), (22, 0.13347995406111463)]
29) xml ID: 1565809
[(0, 0.10085351946991969), (2, 0.084488268688985971), (3, 0.033095689076857081), (5, 0.13327931116961331), (8, 0.10723192212407744), (10, 0.019214525316231339), (13, 0.019492736708672138), (14, 0.061797350026206815), (16, 0.24784333619461216), (21, 0.054327481267710843), (22, 0.1323543545800992)]
30) xml ID: 1501695
[(2, 0.04309080002101192), (3, 0.013796589387992881), (4, 0.12414018676747013), (5, 0.024640645372600526), (8, 0.071292141684422314), (9, 0.016925814841107372), (10, 0.034956065511752059), (14, 0.20955066654422203), (15, 0.1097518051882853), (16, 0.064710637914600674), (17, 0.15774717230548327), (21, 0.03698619192220045), (24, 0.079806450993636618)]
31) xml ID: 1600746
[(2, 0.18823471198307226), (3, 0.31812504041112694), (8, 0.033418966127512599), (10, 0.10001877431537803), (11, 0.090365101444468382), (14, 0.052015720925488167), (15, 0.019135940885101406), (16, 0.026014078779781513), (17, 0.06730752092545568), (22, 0.085904753530723951), (24, 0.013501943862844815)]
32) xml ID: 1601485
[(0, 0.034820036518450961), (1, 0.016426703659861559), (2, 0.024240467794794798), (4, 0.084713990574583259), (5, 0.24249236230316562), (8, 0.26484709539885259), (11, 0.048639238476292444), (16, 0.029827511245893758), (17, 0.015640035146288294), (18, 0.062018407595423403), (21, 0.080455054178973862), (22, 0.036413919520126931), (24, 0.055594209844829032)]
33) xml ID: 1500007
[(3, 0.012021567014814645), (4, 0.067768208978749661), (5, 0.079119339934128424), (7, 0.024011696375356471), (8, 0.023233914053454745), (11, 0.071358113208635887), (13, 0.015257159441022128), (14, 0.020786721160953689), (15, 0.26054640186613148), (20, 0.19424857463703724), (22, 0.16321890466867556), (24, 0.042139059416206361)]
34) xml ID: 1511051
[(2, 0.18160355657867075), (4, 0.14649996158096568), (6, 0.02091271002632944), (7, 0.021489970896187691), (8, 0.10104059021970789), (9, 0.085499016305922076), (11, 0.01736221685810339), (17, 0.061372600191859319), (22, 0.11351876209943053), (24, 0.23680873421572238)]
35) xml ID: 1563315
[(1, 0.17836100946479239), (2, 0.060679568760362651), (4, 0.040419153688932133), (5, 0.033049064558267532), (7, 0.0155507877314272), (8, 0.067589816326698041), (11, 0.069236638434928652), (14, 0.1969899843285135), (16, 0.18411924528894727), (18, 0.031391174561773441), (21, 0.0518017119404438), (22, 0.050251353731408419)]
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37) xml ID: 1551511
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38) xml ID: 1640645
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39) xml ID: 1504619
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40) xml ID: 1624641
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**********Model 3 Start**********
41) xml ID: 1547164
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42) xml ID: 1517014
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43) xml ID: 1647361
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44) xml ID: 1527193
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45) xml ID: 1553126
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47) xml ID: 1565268
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48) xml ID: 1552214
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49) xml ID: 1612416
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50) xml ID: 1563435
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51) xml ID: 1510790
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52) xml ID: 1611136
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53) xml ID: 1608663
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54) xml ID: 1633608
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55) xml ID: 1559711
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56) xml ID: 1554385
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57) xml ID: 1527421
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58) xml ID: 1547107
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59) xml ID: 1518455
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60) xml ID: 1552794
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**********Model 4 Start**********
61) xml ID: 1629437
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63) xml ID: 1602006
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64) xml ID: 1549977
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65) xml ID: 1531127
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67) xml ID: 1522191
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73) xml ID: 1551534
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[(0, 0.034656986340733748), (2, 0.29635212995757115), (4, 0.029713702460201847), (5, 0.093666987609570845), (8, 0.062376693093087208), (11, 0.029915176568514452), (14, 0.042015764904565815), (15, 0.21408046697120539), (16, 0.025295582272701303), (21, 0.11046879463347832), (22, 0.032230506562466558), (24, 0.019798310442323917)]
79) xml ID: 1632881
[(1, 0.040763851514797833), (2, 0.041070423021142589), (3, 0.045764372363815906), (6, 0.047151948199475373), (7, 0.19942799187077415), (8, 0.03535436371886299), (10, 0.01326850171382288), (11, 0.05842454693585318), (13, 0.075976268405588659), (14, 0.066406315067006103), (15, 0.16808404603445351), (16, 0.05510042552241666), (21, 0.015840001316915927), (22, 0.1092719674550867), (23, 0.024994201665887505)]
80) xml ID: 1565508
[(3, 0.018821966901691519), (7, 0.089885647245855127), (8, 0.037709301115021347), (9, 0.069703534915427245), (11, 0.081268199801239727), (14, 0.34343132069894844), (16, 0.038168899534088217), (17, 0.018113412995086123), (18, 0.078536752980307753), (19, 0.031766734042183678), (21, 0.13174744165357535), (24, 0.056653239728921036)]
**********Model 5 Start**********
81) xml ID: 1529927
[(2, 0.4472174439816155), (4, 0.12662838139109509), (5, 0.038152953436699548), (8, 0.043721655968437134), (9, 0.040216086057625944), (11, 0.040305446905655996), (14, 0.053774095906049216), (15, 0.059052803953802829), (20, 0.020578487167443586), (22, 0.024617325287312253), (24, 0.08873254738242338)]
82) xml ID: 1606882
[(0, 0.014836665492982123), (2, 0.024568561334030119), (4, 0.052599954985916735), (5, 0.017559559856834633), (6, 0.08091225906615912), (8, 0.049057191483514707), (10, 0.059415576228320444), (15, 0.39192427968173604), (17, 0.035471616030515137), (18, 0.1137719634065059), (21, 0.088220934093578729), (24, 0.06821772973023657)]
83) xml ID: 1553565
[(4, 0.051648003561900753), (5, 0.056392994838281724), (7, 0.037037301603708495), (8, 0.13070001887286486), (9, 0.076276627548113138), (11, 0.092918104738352339), (14, 0.049201669518683738), (15, 0.037537494129120122), (16, 0.038811724084380841), (18, 0.20240607156888862), (21, 0.13445053789606051), (22, 0.088680057699702552)]
84) xml ID: 1537924
[(1, 0.037292011968975955), (2, 0.021388178730926358), (3, 0.1128257927173459), (4, 0.015597444369761427), (6, 0.011558994500067679), (7, 0.023481926781040417), (9, 0.021905812398942671), (10, 0.092417983790437211), (13, 0.16946692927547788), (17, 0.036631108473929239), (18, 0.12326456127455483), (20, 0.038690198061984843), (21, 0.016884085808282174), (24, 0.26635800436280765)]
85) xml ID: 1633952
[(3, 0.051716850061470129), (5, 0.037686696047349644), (7, 0.16666170890609913), (8, 0.013439007961906654), (9, 0.057171843775153164), (11, 0.016122957780530817), (13, 0.1267195856504463), (15, 0.12762860607951418), (17, 0.13761157507118132), (20, 0.021476483204826591), (21, 0.13608130582572209), (22, 0.10460645655839974)]
86) xml ID: 1617040
[(0, 0.058444690366687507), (1, 0.016636702319408815), (2, 0.059001017401180501), (3, 0.011751435733801425), (4, 0.058435283309824768), (5, 0.035135429096370373), (6, 0.011262391279616953), (8, 0.03147331470000457), (10, 0.036718513247342308), (11, 0.013376883129683517), (14, 0.015230789361846664), (15, 0.01817469954695989), (16, 0.029988725385812345), (17, 0.49046578876541208), (21, 0.014079054378991063), (22, 0.098246334608486252)]
87) xml ID: 1619950
[(0, 0.13342259499395326), (2, 0.12841745873526816), (6, 0.025955702337570375), (7, 0.024620319983072458), (8, 0.021572286764543414), (10, 0.039238146182082892), (13, 0.069687298074248477), (14, 0.087634374108901583), (16, 0.09086220597370108), (17, 0.012475801987903575), (21, 0.078290774028709195), (22, 0.28337859238502688)]
88) xml ID: 1620695
[(0, 0.04079166462927878), (1, 0.010909531036706275), (2, 0.21067676858758885), (5, 0.19239409203188917), (8, 0.17378432689115653), (11, 0.037356013126689964), (13, 0.09271141063635821), (14, 0.098414266183044885), (15, 0.045824218602747081), (21, 0.035499058427890244), (22, 0.056501035167177832)]
89) xml ID: 1547236
[(0, 0.061349844640428378), (2, 0.015328645471930316), (3, 0.092494737935739091), (4, 0.042808813813259183), (5, 0.012572828951828567), (9, 0.06090730195365416), (10, 0.20193248704759367), (11, 0.018013017780051783), (12, 0.043974272944664397), (13, 0.092636721350153398), (15, 0.05736200621810076), (16, 0.022538336376090633), (18, 0.16669629317643808), (20, 0.033443654236433078), (21, 0.016526004225839268), (22, 0.035021835634622162), (23, 0.024057431819509284)]
90) xml ID: 1600767
[(0, 0.023137311702656933), (2, 0.14606373463137473), (3, 0.50039497751008255), (5, 0.017404196361553145), (8, 0.028109843658074236), (10, 0.060852025493126259), (11, 0.034808344204012874), (13, 0.092807462296264548), (15, 0.020787405441252085), (17, 0.014671653312653474), (18, 0.038811680284054371), (24, 0.016559967254733889)]
91) xml ID: 1542727
[(1, 0.18329159493685537), (2, 0.0645862865061438), (6, 0.036822532827555501), (9, 0.12244217443800173), (10, 0.062121994687008721), (12, 0.024763458625080274), (13, 0.12200774154428594), (16, 0.019986953751821988), (17, 0.15940508073764917), (18, 0.043408666782320714), (21, 0.043584586391221598), (22, 0.055276425522555017), (23, 0.03595090453562455), (24, 0.022654119721942943)]
92) xml ID: 1540025
[(0, 0.17457354300736164), (1, 0.20351286760487783), (3, 0.016297265564302783), (6, 0.074040566475441397), (10, 0.075947379727588527), (13, 0.14605915628825425), (18, 0.22491123735420748), (21, 0.047410093050124218), (22, 0.032086600604471709)]
93) xml ID: 1537756
[(0, 0.15925739499980138), (1, 0.087738315205392478), (2, 0.08603666602368315), (3, 0.056047370807283323), (6, 0.040935282006001342), (9, 0.023900803768977949), (10, 0.049229524327794906), (11, 0.010157501575740064), (13, 0.09854714978175301), (14, 0.010622267233897325), (18, 0.14730634175744795), (20, 0.030259654642995709), (22, 0.11242110709217357), (23, 0.02403356119863841), (24, 0.053845315537228385)]
94) xml ID: 1642143
[(3, 0.10065881720546721), (7, 0.10644787995951535), (8, 0.048125744989425755), (9, 0.022119865812942435), (10, 0.030211883811829134), (13, 0.026626422904534611), (15, 0.16180152465763051), (16, 0.045007257208858828), (17, 0.021466665794963955), (18, 0.01945563014973984), (21, 0.0475329796551862), (22, 0.30461107887465205), (23, 0.062577605618265336)]
95) xml ID: 1502392
[(1, 0.10915630269557357), (2, 0.063468183002674997), (3, 0.030701350339844453), (4, 0.32146280781285458), (7, 0.0233744363651163), (9, 0.056420983145850902), (11, 0.05515344690571606), (14, 0.13196828372596994), (15, 0.066104059526665968), (16, 0.069864269119842823), (23, 0.010839047268508093), (24, 0.057189309429352637)]
96) xml ID: 1502293
[(0, 0.035315467723420618), (2, 0.052968539462712488), (4, 0.022768270601758466), (8, 0.064545759979190265), (14, 0.17962898890194032), (15, 0.53666880235907244), (20, 0.031488530632356619), (21, 0.039235410449459142), (24, 0.024223740800561114)]
97) xml ID: 1644552
[(2, 0.05138404944569145), (7, 0.48611705953889583), (8, 0.063546052704467657), (15, 0.1097890366906121), (16, 0.04042978838897289), (17, 0.034045520302953207), (20, 0.025695730460723251), (22, 0.15189792459199788), (23, 0.026026472224609274)]
98) xml ID: 1522751
[(0, 0.058775278730499009), (4, 0.23717439725382858), (5, 0.03958895136715819), (8, 0.05206320173652066), (9, 0.084905620165875398), (10, 0.12252972002722433), (13, 0.041378835695453996), (15, 0.061838387223781918), (18, 0.088835061608119048), (19, 0.012537638091945122), (20, 0.16721306468722855), (24, 0.029065355222755773)]
***********End of Output*********
In [107]:
from gensim import corpora, models, similarities
from collections import defaultdict
import gensim
top1 = []
top2 = []
top3 = []
top4 = []
top5 = []
topics = [top1,top2,top3,top4,top5]
lda1 = models.LdaModel.load('/tmp/CuModel_1.lda')
lda2 = models.LdaModel.load('/tmp/CuModel_2.lda')
lda3 = models.LdaModel.load('/tmp/CuModel_3.lda')
lda4 = models.LdaModel.load('/tmp/CuModel_4.lda')
lda5 = models.LdaModel.load('/tmp/CuModel_5.lda')
for i in range(25):
top1.append(lda1.print_topic(i))
top2.append(lda2.print_topic(i))
top3.append(lda3.print_topic(i))
top4.append(lda4.print_topic(i))
top5.append(lda5.print_topic(i))
lda1.print_topics(25)
Out[107]:
[(0,
'0.010*tactons + 0.010*information + 0.010*organizational + 0.010*faculty + 0.010*stem + 0.008*support + 0.008*multimodal + 0.008*equity + 0.008*women + 0.008*advance'),
(1,
'0.014*power + 0.012*systems + 0.010*energy + 0.010*performance + 0.010*processing + 0.010*ceramic + 0.010*center + 0.008*technologies + 0.008*materials + 0.008*measurement'),
(2,
'0.016*mercury + 0.014*education + 0.011*carbon + 0.011*coastal + 0.010*study + 0.009*learning + 0.008*outcomes + 0.008*flooding + 0.008*extreme + 0.008*programs'),
(3,
'0.020*material + 0.018*design + 0.016*development + 0.014*faculty + 0.010*workshop + 0.010*graduate + 0.010*stem + 0.010*materials + 0.009*undergraduate + 0.009*students'),
(4,
'0.009*design + 0.009*cyberbullying + 0.008*engineering + 0.006*grain + 0.006*systems + 0.006*processes + 0.006*steel + 0.006*membrane + 0.005*modeling + 0.005*ambic'),
(5,
'0.012*heat + 0.010*students + 0.009*commercialization + 0.008*energy + 0.008*generation + 0.008*power + 0.007*graduate + 0.006*technology + 0.006*potential + 0.006*theory'),
(6,
'0.018*vehicle + 0.016*system + 0.014*control + 0.009*based + 0.009*detection + 0.009*algorithms + 0.009*cyber-attack + 0.007*systems + 0.007*communication + 0.007*security'),
(7,
'0.022*anions + 0.011*receptors + 0.008*interactions + 0.007*simulation + 0.006*molecular + 0.006*students + 0.006*units + 0.006*should + 0.006*catalyst + 0.006*liquid'),
(8,
'0.013*water + 0.013*network + 0.010*science + 0.009*homes + 0.008*security + 0.008*studies + 0.007*data + 0.006*technology + 0.006*energy + 0.006*infrastructure'),
(9,
'0.013*water + 0.013*quality + 0.011*implant + 0.010*materials + 0.010*charging + 0.010*network + 0.009*data + 0.008*urban + 0.008*optimization + 0.008*artificial'),
(10,
'0.018*theory + 0.018*spaces + 0.011*buildings + 0.011*signals + 0.009*building + 0.009*mathematical + 0.009*de + 0.009*branges + 0.007*design + 0.007*analysis'),
(11,
'0.010*microwave + 0.009*students + 0.008*yeast + 0.008*dynamics + 0.007*technology + 0.007*properties + 0.007*chemical + 0.007*experience + 0.007*ionic + 0.007*polymers'),
(12,
'0.022*data + 0.015*layer + 0.015*surface + 0.015*provenance + 0.010*distributed + 0.010*systems + 0.007*similarity + 0.007*turbulence + 0.007*mmo + 0.007*monin-obukhov'),
(13,
'0.010*infrastructure + 0.010*program + 0.009*systems + 0.007*simulation + 0.007*engineering + 0.007*model + 0.007*critical + 0.007*students + 0.007*technology + 0.007*emerging'),
(14,
'0.011*students + 0.008*science + 0.008*training + 0.008*cybersecurity + 0.006*internet + 0.006*program + 0.006*engineering + 0.006*development + 0.006*infrastructure + 0.006*national'),
(15,
'0.011*students + 0.010*stem + 0.010*conference + 0.007*graduate + 0.007*latency + 0.006*network + 0.005*highly + 0.005*program + 0.005*grades + 0.005*middle'),
(16,
'0.017*nucleation + 0.017*ice + 0.012*process + 0.010*heterogeneous + 0.009*curing + 0.009*molecular + 0.009*materials + 0.007*students + 0.007*processing + 0.007*biobased'),
(17,
'0.019*sustainability + 0.016*learning + 0.015*courses + 0.012*faculty + 0.011*modules + 0.011*development + 0.010*different + 0.010*disposal + 0.009*stand-alone + 0.008*module'),
(18,
'0.028*computing + 0.013*consortium + 0.013*national + 0.013*campuses + 0.009*carc + 0.009*campus + 0.009*capabilities + 0.006*expertise + 0.006*facilitation + 0.006*best'),
(19,
'0.022*geoscience + 0.015*students + 0.009*program + 0.007*develop + 0.007*earpiece + 0.007*undergraduate + 0.007*majors + 0.007*geosciences + 0.006*behaviors + 0.006*health'),
(20,
'0.014*engineering + 0.011*students + 0.010*mtbe + 0.007*origami + 0.006*microbial + 0.006*model + 0.005*folding + 0.005*ejected + 0.004*graduate + 0.004*specific'),
(21,
'0.018*privacy + 0.010*production + 0.008*team + 0.008*light + 0.008*capacity + 0.008*existing + 0.008*users + 0.007*puritm + 0.007*manufacturing + 0.006*membrane'),
(22,
'0.010*properties + 0.010*novel + 0.010*sensitivity + 0.008*development + 0.008*cell + 0.008*nps + 0.008*cells + 0.008*biological + 0.007*corona + 0.007*np'),
(23,
'0.022*amulet + 0.014*applications + 0.014*technology + 0.011*health + 0.011*mhealth + 0.008*wearable + 0.008*platform + 0.006*software + 0.006*life + 0.006*framework'),
(24,
'0.008*field + 0.007*power + 0.007*students + 0.006*experiences + 0.006*support + 0.005*design + 0.005*systems + 0.005*change + 0.005*capacity + 0.005*lab')]
In [1]:
#****************************************Main VERSION**********************************************
from collections import deque
from gensim import corpora, models, similarities
from collections import defaultdict
import gensim
import matplotlib.pyplot as plt
from scipy import stats
import numpy as np
import scipy as sp
import scipy.stats
%matplotlib inline
class topic:
count = 0
def __init__(self,topic_name):
self.name = topic_name
self.abstract = []
topic.count +=1
def addlist(self,abstr):
self.abstract.append(abstr)
def delete(self):
topic.count-=1
class counter:
count = 0
def __init__(self):
counter.count+=1
def delete(self):
counter.count = 0
# 20% abstract files
file1 = open('cu_test_doc_1.txt', 'r')
abs1 = file1.readlines()
#file2 = open('cu_test_doc_2.txt', 'r')
#abs2 = file2.readlines()
#file3 = open('cu_test_doc_3.txt', 'r')
#abs3 = file3.readlines()
#file4 = open('cu_test_doc_4.txt', 'r')
#abs4 = file4.readlines()
#file5 = open('cu_test_doc_5.txt', 'r')
#abs5 = file5.readlines()
# 20% Directorate/Division files
file10 = open('cu_test_dir_div_1.txt', 'r')
dd1 = file10.readlines()
#file20 = open('cu_test_dir_div_2.txt', 'r')
#dd2 = file20.readlines()
#file30 = open('cu_test_dir_div_3.txt', 'r')
#dd3 = file30.readlines()
#file40 = open('cu_test_dir_div_4.txt', 'r')
#dd4 = file40.readlines()
#file50 = open('cu_test_dir_div_5.txt', 'r')
#dd5 = file50.readlines()
#Load models 80%
lda1 = models.LdaModel.load('CuModel_1.lda')
#lda2 = models.LdaModel.load('/tmp/CuModel_2.lda')
#lda3 = models.LdaModel.load('/tmp/CuModel_3.lda')
#lda4 = models.LdaModel.load('/tmp/CuModel_4.lda')
#lda5 = models.LdaModel.load('/tmp/CuModel_5.lda')
#Load 80% Dictionary
dictionary1 = corpora.Dictionary.load('cu_dict1.dict')
#dictionary2 = corpora.Dictionary.load('/tmp/cu_dict2.dict')
#dictionary3 = corpora.Dictionary.load('/tmp/cu_dict3.dict')
#dictionary4 = corpora.Dictionary.load('/tmp/cu_dict4.dict')
#dictionary5 = corpora.Dictionary.load('/tmp/cu_dict5.dict')
#Directorates and Divisions files
file = open('cu_doc_div.txt', 'r')
dd = file.readlines()
#All Abstracts
file2 = open('cu_tigers.txt','r')
abstr = file2.readlines()
def dd_compare(test_topic,abst):
#Directorates and Divisions files from 80% group
file = open('cu_doc_div2.txt', 'r')
dd = file.readlines()
#Unique topic count
t_file = open('topic_count.txt', 'r')
num = t_file.read()
TOPIC_COUNT = int(num)
#Topic Name Dictionary
global name
name ={}
#Condition Variables
y = 0
new_name = 0
fcount = 0
while y != TOPIC_COUNT:
for x in range(len(test_topic)):
if test_topic[x] == dd[y]:
if new_name == 0:
fcount+=1
name["topic{0}".format(fcount)] = topic(dd[y])#create an object and name it
new_name +=1
name["topic{0}".format(fcount)].addlist(abst[x])#add abstracts from matches
#print(fcount)
if x == len(test_topic)-1:
if new_name > 0:
new_name-=1
y+=1
def mean_confidence_interval(data, confidence=0.95):
a = 1.0*np.array(data)
n = len(a)
m, se = np.mean(a), scipy.stats.sem(a)
h = se * sp.stats.t._ppf((1+confidence)/2., n-1)
return m-h, m+h
#Outputs dd compare when called after dd compare
def output_printer():
for x in range(topic.count):
print(name['topic{0}'.format(x+1)].name)
for x in name['topic{0}'.format(x+1)].abstract:
print(x,"\n")
#convert to bag of words
def bagofwords(words,dictionary):
return dictionary.doc2bow(words.lower().split())
def abs_distr_graph(abs_num,top_name,dictionary,model):
#Select a topic
top = "topic" + str(top_name)
graph = "OK"
t_file = open('topic_count.txt', 'r')
num = t_file.read()
TOPIC_COUNT = num
if 0 < top_name <= topic.count:
topic_abs = name[top].abstract
topic_name = name[top].name
#number of graphs
v = counter()
plt.figure(num=v.count)
for index in range(abs_num):
#convert to bag of words
bow = bagofwords(topic_abs[index],dictionary1)
#Check to makes sure the value entered is not zero and not greater than the maximum number of abstracts
if (abs_num <= len(topic_abs)) and (abs_num != 0):
vec = model[bow]
x = [x[0] for x in vec] #(1,2,3,4..)
y = [x[1] for x in vec] #(.345,.0123,..)
plt.plot(x,y,marker = "o",linestyle ="")
#label=vec
#plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderpad=3,fontsize = 50)
else:
print("**ERROR: End of Abstract list or you asked for 0 abstracts.**")
print("***Total Abstracts for Requested Topic:",len(topic_abs))
print("***Current Topic Request:",topic_name)
graph = "Denied"
#If numbers entered are within the scope of the program it will produce a graph
if graph == "OK":
plt.xlabel('Topics (0-'+ TOPIC_COUNT + ')')
plt.ylabel('Probability of Correlation with Topic')
plt.title('Topic Name: '+ topic_name)
plt.show
else:
print("Topic does not exist. Try a different topic number.")
#create objects for 20% group 1
dd_compare(dd1,abs1)
#abs_distr_graph(Number of Abstracts to Graph,Topic Number,Dictionary,Model)
abs_distr_graph(20,1,dictionary1,lda1)
#abs_distr_graph(3,1,dictionary1,lda1)
print("Done")
Done
In [2]:
print(name["topic1"].name)
print(name["topic1"].abstract[0])
Direct For Computer & Info Scie & Enginr Division Of Computer and Network Systems
The past few years have seen unprecedented growth in mobile data consumption Powered in large part by the rapid adoption of smart phones and tablets the growth in wireless data creates phenomenal challenges for the wireless industry which has been unable to meet the demand for rich mobile content through cellular networks This has led to the investigation of solutions by network operators that aim to utilize WiFi radios present in these mobile devices to deliver content without using the cellular radio links also known as content offloading Industry led approaches aim to utilize WiFi infrastructure in the form of access points to offload this content but these have various deployment issues Research has lately focused on the potential of proximity based peer content sharing since proximity enables low power high speed data exchanges which in turn allows mobile devices to proactively share data with one another No large scale study using real world situations has established the potential for such content sharing to provide capacity gains until now This proposal aims to conduct solid preliminary pilot studies evaluating several of the foundational claims in this area specifically as to whether sufficient potential exists in the right time right place and with reasonably viable deployment scenarios The proposed work will gather and evaluate pilot data in both highly amenable environments WiFi across multiple tailgate offerings of hundreds of users as well as more challenging environments daily commuter trains to from Chicago br br The broader impact of the work will be to either demonstrate the potential viability for proximity based solutions or to present compelling evidence that such proximity solutions are unlikely to yield significant benefits Further broader impacts for the work include data sharing capabilities with respect to temporal characterizations of redundancy across mobile devices in several real world scenarios
In [10]:
#Takes two lists of probabibility distribution and returns a list with the KS statistic in index 0 and p-value in index 1
#example: x = [(1,.01234),(2,.2345),..] y = [(0,.2367),(1,.02345)]
def statistic(x,y):
if len(x) and len(y) > 0:
#store lists
fullX = []
fullY = []
#index
j = 0
#Convert from a 2D list to a 1D or reshape
#List 1
x1 = [x[0] for x in x]
y1 = [x[1] for x in x]
#List 2
x2 = [v[0] for v in y]
y2 = [v[1] for v in y]
#Sort List
x1.sort()
x2.sort()
#Store Size
size = len(x1)
size2 = len(x2)
#Fill end of lists with zeros
for value in range(55-size):
x1.append(0)
y1.append(0)
for value in range(55-size2):
x2.append(0)
y2.append(0)
#fill comparison lists
for value in range(55):
fullX.append(0)
fullY.append(0)
#replace zeroes at the corresponding index with the probability distribution
for i in range(55):
value1 = y1[i]
index1 = x1[i]
fullX[index1] = value1
value2 = y2[i]
index2 = x2[i]
fullY[index2] = value2
#import statistic library
from scipy import stats
#First value = KS Statistic Second value = P-value
return stats.ks_2samp(fullX, fullY)
In [28]:
#Must run the code above for this code to operate
#Prints a graph for all distributions
def LargeGraph():
t_file = open('topic_count.txt', 'r')
num = t_file.read()
TOPIC_COUNT = num
t_file.close()
n = 1
plt.figure(num=None, figsize=(20, 6))
while n != topic.count:
#Access topic objects 1 at a time
top = "topic" + str(n)
for vec in range(len(name[top].abstract)):
#Convert to bag of words and pass information through model
bow = bagofwords(name[top].abstract[vec],dictionary1)
vec = lda1[bow]
x = [x[0] for x in vec] #collect all the x values
y = [x[1] for x in vec] #collect all the y values
#Plot the graph for the probability distribution
plt.plot(x,y,marker = "o",linestyle = "")
n+=1
plt.xlabel('Topics (0-'+ TOPIC_COUNT + ')')
plt.ylabel('Probability of Correlation with Topic')
plt.title('All Topics')
plt.show
LargeGraph()
[(8, 0.89090909090909354)]
[(4, 0.83636363636363409)]
[(2, 0.93454545454545634)]
[(4, 0.90181818181818429)]
[(11, 0.73917633008648009), (37, 0.23405599314584336)]
[(54, 0.80363636363636348)]
[(5, 0.75505358924840793), (42, 0.20304917755000859)]
[(52, 0.95731225296442679)]
[(34, 0.9553719008264453)]
[(10, 0.71194996476958872), (41, 0.25363445081482505)]
[(11, 0.90181818181818396)]
[(11, 0.94545454545454277)]
[(35, 0.94683836272080779)]
[(41, 0.96614420062695872)]
[(39, 0.56428183437541979), (52, 0.39382093242299931)]
[(25, 0.80363636363636204)]
[(49, 0.85974025974025947)]
[(22, 0.92987012987013096)]
[(3, 0.6898179181284716), (31, 0.25946437852224546)]
[(11, 0.97768595041322526)]
[(11, 0.9181818181818201)]
[(39, 0.93863636363636416)]
[(25, 0.94545454545454366)]
[(38, 0.94224598930481174)]
[(17, 0.95089365076865573)]
[(21, 0.80363636363636182)]
[(25, 0.94832535885167291)]
[(5, 0.75454545454545552)]
[(49, 0.48121065315829947), (52, 0.42242571047806426)]
[(45, 0.85974025974025936)]
[(5, 0.89090909090909354)]
[(25, 0.80083822104856173), (50, 0.16704056683022503)]
[(36, 0.92447552447552517)]
[(22, 0.92447552447552572)]
[(8, 0.52485951104388184), (34, 0.077666442415650613), (35, 0.36697551281612573)]
[(30, 0.92987012987013073)]
[(26, 0.87727272727272632)]
[(19, 0.91074380165289415)]
[(25, 0.91818181816931066)]
[(21, 0.7274924177548765), (25, 0.23061034904354086)]
[(11, 0.73644569006646787), (42, 0.21766686404608557)]
[(4, 0.9244755244755265)]
[(22, 0.94545454545454344)]
[(44, 0.91074380165289293)]
[(11, 0.94832535885167202)]
[(11, 0.9532467532467509)]
[(28, 0.95909090909087258)]
[(36, 0.21719784910659373), (54, 0.7261176589147964)]
[(37, 0.91074380165288793)]
[(11, 0.86514404900753183), (12, 0.020254380906702563), (38, 0.021431580314880558), (50, 0.078451375052271705)]
[(20, 0.95090909090906406)]
[(50, 0.89090909090909098)]
[(37, 0.92447552447552506)]
[(39, 0.89231846181540131), (50, 0.083590629093690236)]
[(11, 0.96363636363333238)]
[(37, 0.83636363636363553)]
[(11, 0.959090909090907)]
[(5, 0.78005266118608019), (43, 0.17614568592135527)]
[(35, 0.95324675324675212)]
[(2, 0.92822426803659763)]
[(51, 0.8363636363636362)]
[(25, 0.89090909089729386)]
[(23, 0.89090909090909265)]
[(50, 0.95909090909090888)]
[(37, 0.9298701298701304)]
[(2, 0.91074380165289481)]
[(31, 0.89090909090055603)]
[(10, 0.95090909090908826)]
[(42, 0.83200703203213433), (54, 0.13093003090492802)]
[(51, 0.92447552447552461)]
[(34, 0.91818181818181899)]
[(9, 0.94224598930480974)]
[(50, 0.94224598930481251)]
[(22, 0.93375669550312479)]
[(49, 0.80363636363636326)]
[(41, 0.89090909090909154)]
[(36, 0.93863636363636427)]
[(11, 0.89090909090909332)]
[(9, 0.92987012986809647)]
[(41, 0.89090909090909154)]
[(4, 0.94545454545454222)]
[(25, 0.37639790020027381), (38, 0.58505664525427115)]
[(8, 0.94832535885167191)]
[(8, 0.92447552447552628)]
[(8, 0.54403310500681923), (38, 0.420276659302945)]
[(10, 0.96223776223776014)]
[(28, 0.96727272727256575)]
[(8, 0.96832844574779886)]
[(28, 0.6694225893050163), (31, 0.29042589554346732)]
[(18, 0.94832535885167235)]
[(31, 0.92447552447552528)]
[(54, 0.93692118383617895)]
[(38, 0.83636363636363553)]
[(4, 0.93454545454545623)]
[(44, 0.87727272727272687)]
[(25, 0.95324675324675168)]
[(17, 0.89090909090909298)]
[(23, 0.89090909090909265)]
[(20, 0.95090909090908893)]
[(43, 0.92987012987013029)]
[(54, 0.89090909090909087)]
[(17, 0.95324675324675123)]
[(50, 0.9454545454545451)]
[(22, 0.96727272727272606)]
[(30, 0.85974025974025869)]
[(45, 0.75454545454545463)]
[(45, 0.9018181818181823)]
[(12, 0.94545454545454277)]
[(25, 0.83636363636363498)]
[(25, 0.95324675324666952)]
[(41, 0.9573122529643967)]
[(3, 0.9244755244755265)]
[(28, 0.95909090909090788)]
[(11, 0.92447552447552617)]
[(43, 0.95090909090909026)]
[(20, 0.92987012987013118)]
[(21, 0.75454545454545519)]
[(31, 0.89090909090909209)]
[(8, 0.015305973930342713), (50, 0.94900379037942151)]
[(34, 0.94545454545454422)]
[(44, 0.85974025974025936)]
[(20, 0.87727272727272598)]
[(26, 0.90181818181818318)]
[(37, 0.96363636363636296)]
[(28, 0.89271630600064422), (45, 0.092226875817538509)]
[(31, 0.87727272727272643)]
[(37, 0.85974025974025903)]
[(34, 0.92987012987012219)]
[(12, 0.45835331713731853), (49, 0.45298586500948118), (54, 0.049266878459261539)]
[(10, 0.24247372010765456), (39, 0.70383462233186467), (49, 0.01732802119684344)]
[(9, 0.75454545454545541)]
[(11, 0.89090909090909132)]
[(31, 0.95909090909085015)]
[(9, 0.93454545454545601)]
[(41, 0.90181818181783635)]
[(20, 0.23016814818861125), (25, 0.7327689147484503)]
[(41, 0.48140886488092599), (45, 0.48004568057361913)]
[(28, 0.8363636363636352)]
[(5, 0.67272727272727384)]
[(52, 0.96072727272727232)]
[(39, 0.91818181817937616)]
[(20, 0.10262373971847001), (34, 0.84384090674617518)]
[(28, 0.93454545454545535)]
[(8, 0.85974025974025781)]
[(9, 0.8363636363636342)]
[(19, 0.85974025974025825)]
[(10, 0.57453963710290967), (42, 0.38691490835163589)]
[(19, 0.92447552446734227)]
[(47, 0.75454545454428545)]
[(50, 0.9244755244755245)]
[(5, 0.93863636363636516)]
[(17, 0.91818181818181843)]
[(17, 0.92447552447552594)]
[(21, 0.18104614442608982), (28, 0.27440355949260148), (39, 0.52525530535774712)]
[(37, 0.78543461138311532), (50, 0.15788089663827529)]
[(41, 0.67272727272727295)]
[(3, 0.97112299465240981)]
[(2, 0.80363636363636082)]
[(50, 0.92447552447552384)]
[(44, 0.96727272726944635)]
[(10, 0.042305721649808371), (22, 0.9275806419865541)]
[(2, 0.67272727272727395)]
[(2, 0.4959661525056106), (50, 0.4810901245506653)]
[(23, 0.96072727272727121)]
[(21, 0.29721539541250758), (47, 0.68088377814121215)]
[(11, 0.92447552447552617)]
[(51, 0.94545454545454533)]
[(45, 0.75454545453345712)]
[(11, 0.97194805194805467)]
[(20, 0.94832535885167257)]
[(10, 0.93956559274654383), (52, 0.029349363265184683)]
[(5, 0.93097988968236489), (20, 0.030474655772178255)]
[(26, 0.97024793388429886)]
[(18, 0.51851284990772972), (31, 0.43768549719970729)]
[(22, 0.95731225296439082)]
[(8, 0.94224598930472026)]
[(37, 0.93454545454545412)]
[(34, 0.87727272727272654)]
[(34, 0.92447552447550063)]
[(17, 0.87727272725406669)]
[(12, 0.92447552447552606)]
[(22, 0.96832844574779875)]
[(21, 0.94545454545454333)]
[(45, 0.9018181818181823)]
[(38, 0.91074380165289326)]
[(11, 0.9573122529642234)]
[(43, 0.94224598930481207)]
[(39, 0.91074380165289326)]
[(11, 0.92652002592880389), (49, 0.051579147624917332)]
[(30, 0.95731225296442557)]
[(37, 0.94545454545454444)]
[(5, 0.23265308025071987), (11, 0.63751608220697153), (51, 0.093467201178670231)]
[(41, 0.95090909090909015)]
[(47, 0.89090909090909121)]
[(3, 0.80363636363636082)]
[(3, 0.92987012987013173)]
[(50, 0.87727272727268346)]
[(35, 0.9184796630031129), (36, 0.045401751561815633)]
[(11, 0.85974025974025792)]
[(20, 0.85974025974025836)]
[(45, 0.91074380165289293)]
[(49, 0.95090909090909059)]
[(42, 0.94832535885167379)]
[(18, 0.85499070148737821), (25, 0.10311206531103895)]
[(52, 0.90181818181818196)]
[(42, 0.91818181818181865)]
[(5, 0.95324675324675057)]
[(9, 0.91905700647887367), (36, 0.039045760319543064)]
[(5, 0.70520645421573858), (34, 0.25624809123880471)]
[(34, 0.93863636363636427)]
[(12, 0.94545454545454266)]
[(23, 0.89090909090909265)]
[(3, 0.93454545454545623)]
[(3, 0.92447552447552639)]
[(8, 0.94795857988388177)]
[(20, 0.95731225296442513)]
[(25, 0.91818181818181932)]
[(8, 0.95090909090908826)]
[(17, 0.94224598930481029)]
[(45, 0.95324675324668318)]
[(44, 0.92447552447552483)]
[(36, 0.89090909090909187)]
[(18, 0.50909090909091004)]
[(54, 0.9244755244755245)]
[(5, 0.93863636363636516)]
[(30, 0.75454545454545507)]
[(4, 0.75454545454545552)]
[(5, 0.91818181818182021)]
[(21, 0.85974025974025836)]
[(51, 0.58458864320999726), (52, 0.37351412358842151)]
[(50, 0.93454545454545457)]
[(3, 0.97194805194805522)]
[(22, 0.93863636363636471)]
[(25, 0.89090909090909243)]
[(11, 0.95731225296442468)]
[(12, 0.8737576752614663), (34, 0.087696870193077259)]
[(25, 0.96493506493506376)]
[(23, 0.87727272727232031)]
[(11, 0.44686049357784929), (21, 0.47901363229627769)]
[(51, 0.93454545454545468)]
[(8, 0.90181818181818418)]
[(21, 0.91656638153425807), (43, 0.039631965573178218)]
[(45, 0.96832844574780019)]
[(11, 0.90181818181818396)]
[(17, 0.90181818181818374)]
[(39, 0.94545454545454444)]
[(17, 0.90181818181818374)]
[(52, 0.92447552447552461)]
[(11, 0.92987012987013151)]
[(10, 0.94224598930480974)]
[(50, 0.94545454545454521)]
[(45, 0.9422459893048063)]
[(20, 0.37490317397468481), (41, 0.58803388896237796)]
[(37, 0.80363636361962731)]
[(8, 0.92987012987013162)]
[(18, 0.93863636363636471)]
[(34, 0.92447552447552517)]
[(44, 0.91074380165289293)]
[(18, 0.77042060861065087), (54, 0.18942787623783225)]
[(47, 0.93863636363636382)]
[(4, 0.8597402597402477)]
[(30, 0.92987012987013073)]
[(18, 0.45355994926200993), (47, 0.52293672479563924)]
[(21, 0.85974025974025836)]
[(8, 0.26980090233664805), (23, 0.68201727948105162)]
[(11, 0.94224598930480996)]
[(34, 0.90181818181818274)]
[(41, 0.95324675324675257)]
[(17, 0.89090909090909298)]
[(25, 0.91818181818181932)]
[(26, 0.92987012987013085)]
[(25, 0.95537190082644474)]
[(51, 0.9107438016528927)]
[(45, 0.92987012987013018)]
[(43, 0.92987012987013029)]
[(47, 0.89090909090909121)]
[(17, 0.77603473721305138), (20, 0.18206802958536586)]
[(42, 0.96363636363636307)]
[(25, 0.22964276866911348), (30, 0.51209974463500796), (42, 0.21715076732828847)]
[(47, 0.95324675324675279)]
[(37, 0.95537190082644541)]
[(43, 0.9345454545454549)]
[(41, 0.93454545454545501)]
[(19, 0.92447552447552583)]
[(34, 0.91074380165289337)]
[(35, 0.93454545454545512)]
[(5, 0.93454545454545612)]
[(36, 0.92447552447550907)]
[(41, 0.9345454545454549)]
[(19, 0.028724293086682055), (52, 0.91774035337796434)]
[(18, 0.93454545454545568)]
[(3, 0.95909090909090655)]
[(21, 0.83636363636363487)]
[(31, 0.85974025974025881)]
[(52, 0.92987012987012996)]
[(21, 0.93454545454545568)]
[(9, 0.66656219991457655), (30, 0.29774756439518579)]
[(35, 0.92447552447552517)]
[(11, 0.95731225296440325)]
[(25, 0.78196177598455219), (37, 0.17423657112288438)]
[(49, 0.93454545454545479)]
[(30, 0.40342139317227887), (37, 0.54304325329236636)]
[(30, 0.36489481131150814), (45, 0.44237791596121839)]
[(26, 0.95537190082644474)]
[(47, 0.52416377774863387), (51, 0.43729076770591141)]
[(25, 0.61224321025676198), (35, 0.34186934385579126)]
[(17, 0.94224274806001096)]
[(45, 0.87727272727272698)]
[(11, 0.58184885685652976), (25, 0.36996932496165086)]
[(25, 0.94224598930481096)]
[(34, 0.93454545454528126)]
[(30, 0.92447552447552517)]
[(2, 0.94224598930480929)]
[(50, 0.85974025974025958)]
[(3, 0.75413014582824911), (17, 0.2183373866392854)]
[(45, 0.94832535885167402)]
[(20, 0.9018181818181833)]
[(34, 0.94832535885167346)]
[(9, 0.46340636988407524), (25, 0.38176120616277276), (54, 0.12106619018691726)]
[(22, 0.76457786150197327), (34, 0.19527062334651019)]
[(22, 0.93454545454545568)]
[(44, 0.89090909090909143)]
[(52, 0.9107438016528927)]
[(39, 0.93454545454545501)]
[(28, 0.93454545454545535)]
[(50, 0.93454545454545457)]
[(2, 0.90527600495791294), (4, 0.057661057979147744)]
[(34, 0.96493506493506409)]
[(2, 0.90181818181818429)]
[(45, 0.96072727272332081)]
[(25, 0.94545454545454366)]
[(21, 0.022959476273629948), (31, 0.72546851509919164), (39, 0.21046528925958841)]
[(5, 0.07379367943320396), (35, 0.88031887467934922)]
[(34, 0.89090909090909187)]
[(28, 0.9532467532467519)]
[(41, 0.39034232335318475), (51, 0.54943040391954234)]
[(23, 0.49079615004856597), (44, 0.44897657722416223)]
[(17, 0.94224598930481029)]
[(25, 0.93454545454545557)]
[(10, 0.91074380165289459)]
[(25, 0.90181818181818318)]
[(17, 0.94795190768709559)]
[(17, 0.13357547760205657), (25, 0.82452728919636098)]
[(30, 0.94545454545454388)]
[(18, 0.92987012987013096)]
[(2, 0.19899731313536798), (9, 0.76658710244904571)]
[(43, 0.91818181818181854)]
[(2, 0.63956343782887148), (5, 0.30690120863577192)]
[(2, 0.87727272727244499)]
[(2, 0.94545454545453866)]
[(43, 0.86663578918594109), (51, 0.097673975123822851)]
[(49, 0.92987012987013007)]
[(28, 0.14597118704046219), (30, 0.82079997283414485)]
[(3, 0.87727272727272543)]
[(11, 0.48609003841637066), (34, 0.49200913513734879)]
[(42, 0.90181818181818241)]
[(35, 0.90181818181818274)]
[(47, 0.75454545454545463)]
[(19, 0.9607272727272711)]
[(12, 0.50333295899872532), (52, 0.40906373521614969)]
[(11, 0.96223776223776036)]
[(5, 0.9107438016528947)]
[(17, 0.36464638640006281), (51, 0.58946616771249205)]
[(11, 0.76800813289007419), (38, 0.19630163141968832)]
[(35, 0.94224598930481152)]
[(47, 0.90181818181818207)]
[(42, 0.9345454545454549)]
[(5, 0.73586988235396966), (11, 0.16707778332658949), (45, 0.076936280160835827)]
[(20, 0.94224598930481052)]
[(10, 0.36238655291432448), (50, 0.53054274001496882)]
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[(8, 0.90181818181818418)]
[(26, 0.96727272727272617)]
[(19, 0.85974025974025714)]
[(45, 0.96223776223213053)]
[(43, 0.91818181818181865)]
[(25, 0.94545454545454366)]
[(36, 0.93454545454414373)]
[(11, 0.96832844574779897)]
[(52, 0.75454545454545452)]
[(39, 0.93863636363636416)]
[(45, 0.93863636363636394)]
[(18, 0.79598273399193742), (28, 0.15048191247270756)]
[(34, 0.91818181818181899)]
[(8, 0.29410564386462396), (36, 0.67147877171979142)]
[(26, 0.83636363636363509)]
[(26, 0.87727272727272632)]
[(34, 0.92447552447552517)]
[(11, 0.95537190082644408)]
[(22, 0.89090909090909265)]
[(2, 0.37151691571170165), (20, 0.59142014722535996)]
[(28, 0.90181818181818307)]
[(18, 0.92447552447552583)]
[(23, 0.086776767890683906), (54, 0.8713259989077351)]
[(35, 0.8772727272727191)]
[(25, 0.91818181818181921)]
[(39, 0.94545454545454444)]
[(22, 0.85974025974018964)]
[(39, 0.93863636363636416)]
[(52, 0.91818181818181832)]
[(39, 0.89090909090909176)]
[(10, 0.95909090909006767)]
[(9, 0.8363636363636342)]
[(11, 0.91074380165226754)]
[(22, 0.92987012987013096)]
[(45, 0.9018181818181823)]
[(22, 0.95324675324675157)]
[(3, 0.67272727272727384)]
[(23, 0.92987012987013073)]
[(50, 0.972727272727273)]
[(9, 0.85974025974025781)]
[(10, 0.67272727272727384)]
[(19, 0.85974025973955925)]
[(52, 0.92987012987012996)]
[(22, 0.89090909090909265)]
[(47, 0.93454545454545479)]
[(3, 0.90181818181817919)]
[(10, 0.91818181818181999)]
[(25, 0.91818181818181932)]
[(5, 0.87727272727272543)]
[(43, 0.24124003910560327), (45, 0.71495830800183424)]
[(37, 0.7447301503097592), (50, 0.22418480570197041)]
[(49, 0.9018181818181783)]
[(28, 0.084698402563357464), (39, 0.58338654887999419), (50, 0.31615747279907308)]
[(17, 0.9553719008264443)]
[(37, 0.85974025974025903)]
[(50, 0.89090909090909098)]
[(22, 0.89090909090909265)]
[(41, 0.95537190082644563)]
[(54, 0.93863636363636371)]
[(38, 0.90181818181818252)]
[(54, 0.89090909090909087)]
[(35, 0.90181818181818274)]
[(45, 0.96727272727272695)]
[(39, 0.95090909090908993)]
[(18, 0.50045280840388062), (30, 0.40318355523248389)]
[(10, 0.89090909090909332)]
[(45, 0.87727272727272687)]
[(19, 0.4099222671290656), (49, 0.53654237933558058)]
[(34, 0.90181818181818274)]
[(20, 0.85974025974025836)]
[(34, 0.92394920715027684), (50, 0.038987855786785319)]
[(50, 0.83636363636245858)]
[(8, 0.91074380165289426)]
[(5, 0.48343018491338929), (8, 0.48736871315823915)]
[(23, 0.85974025974025847)]
[(2, 0.50909090909091048)]
[(11, 0.89090909090908921)]
[(2, 0.95537190082644352)]
[(50, 0.91818181818181832)]
[(31, 0.89090909090909209)]
[(37, 0.92447552447552517)]
[(52, 0.94545454545454533)]
[(10, 0.96072727272727054)]
[(10, 0.43285016797615544), (26, 0.4466952865692983)]
[(38, 0.85974025974025903)]
[(20, 0.89090909090909276)]
[(3, 0.93863636363636527)]
[(22, 0.83636363636363487)]
[(5, 0.91818181818182021)]
[(3, 0.92987012987013173)]
[(10, 0.22917131441456448), (18, 0.72893145238385249)]
[(11, 0.75454545454545541)]
[(26, 0.92987012987011131)]
[(10, 0.91818181818181999)]
[(36, 0.96223776223776136)]
[(12, 0.9345454545454559)]
[(2, 0.26381503108661125), (4, 0.6960334537618712)]
[(45, 0.96363636363636318)]
[(54, 0.953246753241489)]
[(12, 0.92882222671644832), (42, 0.017642419748195349)]
[(26, 0.80363636363636215)]
[(34, 0.92447552447552517)]
[(30, 0.95090909090908948)]
[(47, 0.91818181818161371)]
[(50, 0.95537190082644607)]
[(11, 0.85974025974025792)]
[(20, 0.13072931812447583), (30, 0.67310562974257837), (49, 0.16990242587031915)]
[(26, 0.92447552447552561)]
[(11, 0.96727272727272562)]
[(25, 0.83636363635907307)]
[(5, 0.94224598930480952)]
[(10, 0.87727272727272576)]
[(3, 0.90181818181818429)]
[(2, 0.93454545454545623)]
[(9, 0.92447552447552617)]
[(41, 0.10453094111910731), (49, 0.8584061218179555)]
[(8, 0.93863636363636516)]
[(22, 0.92447552447552572)]
[(22, 0.89090909090909265)]
[(31, 0.94224598930481129)]
[(43, 0.92987012987013029)]
[(38, 0.85974025974005208)]
[(3, 0.89090909090909365)]
[(25, 0.80363636363636193)]
[(28, 0.89090909090909221)]
[(30, 0.94545454545454388)]
[(31, 0.85974025974025881)]
[(47, 0.92987012987013007)]
[(21, 0.92447552447552572)]
[(45, 0.91074380165289293)]
[(38, 0.94545454545454444)]
[(43, 0.9107438016525724)]
[(28, 0.95090909090908948)]
[(9, 0.92987012987013151)]
[(25, 0.93454545454545557)]
[(42, 0.91818181818181865)]
[(2, 0.90181818181818429)]
[(31, 0.17707914588110638), (50, 0.79941752817654355)]
[(19, 0.87727272727272598)]
[(30, 0.94545454545454388)]
[(30, 0.94832535885167313)]
[(20, 0.89090909090909276)]
[(34, 0.92447552447552517)]
[(54, 0.92447552447552439)]
[(9, 0.029413141187528819), (25, 0.95008395745850738)]
[(10, 0.90181818181492779)]
[(44, 0.95324675324675268)]
[(11, 0.96931818181818019)]
[(47, 0.96413737143602252)]
[(3, 0.75454545454545474)]
[(36, 0.97272727272727377)]
[(17, 0.96363636363636207)]
[(50, 0.92447552447552461)]
[(5, 0.26228956298750683), (11, 0.689764260395893), (25, 0.023703752374178419)]
[(28, 0.96614420062695827)]
[(11, 0.96614420062695749)]
[(12, 0.93863636363636493)]
[(5, 0.92447552447552639)]
[(18, 0.93454545454545568)]
[(34, 0.44624907021539928), (36, 0.52621846225213431)]
[(35, 0.94832535885167346)]
[(38, 0.93454545454545501)]
[(25, 0.91818181818181932)]
[(18, 0.96072727272727099)]
[(50, 0.93863636363636371)]
[(22, 0.023873078004466586), (35, 0.90729575316436528)]
[(54, 0.92987012987012985)]
[(10, 0.95537190082644385)]
[(26, 0.91074380165289381)]
[(4, 0.96072727272727032)]
[(22, 0.94545454545454344)]
[(17, 0.95731225296442468)]
[(10, 0.93454545454266535)]
[(11, 0.94832535885167202)]
[(39, 0.91818181817232081)]
[(23, 0.96614420062695727)]
[(36, 0.42116912504749404), (51, 0.44116853729016831)]
[(22, 0.95525094936410992)]
[(11, 0.60710474378481294), (23, 0.35274374106367057)]
[(47, 0.95909090909090866)]
[(19, 0.85974025974025825)]
[(21, 0.90181818181818341)]
[(8, 0.95090909090908826)]
[(21, 0.96493506493506354)]
[(17, 0.91074380165289426)]
[(9, 0.95090909090908826)]
[(31, 0.95324675324675134)]
[(17, 0.93863636363636482)]
[(21, 0.95090909090908893)]
[(5, 0.96072727272727043)]
[(11, 0.96614420062695749)]
[(26, 0.83636363636363509)]
[(28, 0.94832535885167313)]
[(50, 0.92447552447552461)]
[(25, 0.54895649470308283), (34, 0.40032580194763379)]
[(11, 0.91074380165289459)]
[(17, 0.94224598930481029)]
[(11, 0.91818181817770173)]
[(30, 0.8363636363636352)]
[(38, 0.89090909090909176)]
[(50, 0.94832535885167435)]
[(8, 0.95537190082644385)]
[(5, 0.90181818181818418)]
[(44, 0.92987012987013018)]
[(17, 0.96223776223776059)]
[(23, 0.91818181818181954)]
[(39, 0.95731225296429023)]
[(23, 0.9386363636363646)]
[(3, 0.80363636363636082)]
[(52, 0.90181818181818185)]
[(17, 0.83636363636362954)]
[(52, 0.92447552447552439)]
[(35, 0.89090909090909187)]
[(2, 0.92304379603552411), (8, 0.03315455107191128)]
[(11, 0.97346437346437598)]
[(2, 0.90181818181818429)]
[(21, 0.93454545454545557)]
[(37, 0.91818181818181899)]
[(23, 0.92987012987013096)]
[(50, 0.94832535885167435)]
[(21, 0.90181818181818341)]
[(11, 0.74311312436743349), (45, 0.21099942974511912)]
[(35, 0.9553719008264453)]
[(11, 0.63594947204054342), (47, 0.31816308207201122)]
[(20, 0.92987012987013118)]
[(35, 0.39666576016702976), (52, 0.57010539970757868)]
[(21, 0.94545454545454333)]
[(8, 0.58115193715253355), (36, 0.37869654769595101)]
[(45, 0.32199271801825968), (54, 0.62447192844638666)]
[(50, 0.92447552447488102)]
[(28, 0.97272727272727444)]
[(31, 0.93863636363636438)]
[(5, 0.83636363636363409)]
[(50, 0.95537190082644607)]
[(4, 0.94953082794244026)]
[(21, 0.054177644307825581), (34, 0.5431490481050899), (42, 0.35917306177272773), (43, 0.029660354362525759)]
[(12, 0.94224598930474912)]
[(8, 0.9107438016528947)]
[(34, 0.85974025971591828)]
[(44, 0.94224598930481207)]
[(41, 0.90181818181818241)]
[(36, 0.067860484100395288), (37, 0.88961307766263087)]
[(10, 0.94832535885167191)]
[(39, 0.87570598807248556), (42, 0.10135028898379213)]
[(2, 0.91074380165289481)]
[(10, 0.80208384074841177), (35, 0.14438080571623413)]
[(35, 0.95090909090908982)]
[(2, 0.33653905292686098), (45, 0.26872460734811593), (49, 0.32720387219254027)]
[(10, 0.075684351242610803), (12, 0.17398589040520526), (43, 0.72167962061113822)]
[(51, 0.80363636363636337)]
[(11, 0.8297163886988862), (41, 0.14275114376864623)]
[(23, 0.95537190082640122)]
[(22, 0.9386363636363646)]
[(39, 0.82842870292525206), (50, 0.10732887283232416)]
[(50, 0.8363636363636362)]
[(3, 0.97194805194758827)]
[(52, 0.8772727272727272)]
[(34, 0.85974025974025892)]
[(10, 0.87727272727272576)]
[(50, 0.5090909090583331)]
[(25, 0.92447552447552561)]
[(45, 0.87727272727272698)]
[(31, 0.90181818181818285)]
[(45, 0.93454545454545479)]
[(22, 0.9386363636363646)]
[(43, 0.87727272727272676)]
[(47, 0.89090909090909121)]
[(42, 0.95731225296367961)]
[(19, 0.73622751908033968), (34, 0.210237127384306)]
[(3, 0.025980405483524406), (20, 0.9302179416239118)]
[(35, 0.87727272727272654)]
[(22, 0.5227193844536322), (42, 0.42909879736454942)]
[(2, 0.92987012987013173)]
[(41, 0.89090909089558312)]
[(50, 0.97194805194708367)]
[(11, 0.36380144176607382), (12, 0.26228427376903735), (39, 0.14614691649226122), (54, 0.21617645888171963)]
[(45, 0.70429470028245522), (52, 0.25715984517208978)]
[(37, 0.95090909090908948)]
[(51, 0.96614420062695916)]
[(21, 0.90181818181818341)]
[(31, 0.92447552447550418)]
[(10, 0.67272727272727384)]
[(8, 0.92447552447195225)]
[(28, 0.9244755244755255)]
[(42, 0.91818181818181865)]
[(25, 0.91818181818154976)]
[(20, 0.91818181818181965)]
[(26, 0.50909090909090982)]
[(11, 0.91074380165289459)]
[(41, 0.90181818181818241)]
[(47, 0.85974025974025947)]
[(35, 0.9244755244751911)]
[(49, 0.91818181818181843)]
In [12]:
#Compares internal probability distribution pairs
def InterCompare(lists):
#Variable to determine the list split
if lists is not None:
#New Lists
input_size = len(lists)
pvalue = []
count = 0
store = 0
index = 0
value1 = len(lists)
value2 = value1
for length in range(input_size):
value1 = value1 + value2
value2 -=1
#comparing within a list of documents
#while value != "end":
while store != value1:
#bagofwords(Document, Dictionary) converts to token vectors
if index != len(lists)-1:
bow = bagofwords(lists[index],dictionary1)
if count == index:
first = lda1[bow]
index+=1
elif count != index:
second = lda1[bow]
pvalue.append(statistic(first,second)[1])
index+=1
store+=1
else:
count+=1
index = 0
print("Average p-value = ",sum(pvalue)/value1)
print("Maximum p-value = ",max(pvalue))
print("Confidence Interval: ",mean_confidence_interval(pvalue))
else:
print("List is empty.")
InterCompare(name["topic3"].abstract)
Average p-value = 0.999999989923
Maximum p-value = 1.0
Confidence Interval: (0.99999998246203492, 0.9999999973843956)
In [ ]:
In [33]:
#Compares pairs between different topics
def ExterCompare(topic_name1,list1,topic_name2,list2):
#Check if lists are empty ERROR: Empty Value
if len(list1) != 0:
if len(list2) != 0:
#Check if Topic 1's name is empty ERROR: Empty Value
if topic_name1 != "":
##Check if Topic 2's name is empty ERROR: Empty Value
if topic_name2 != "":
#Declare Variables
count = 0
sums = 0
p = 1
#Store Names
name1 = topic_name1
name2 = topic_name2
#Declare list storage
alt_list1 = []
alt_list2 = []
#Store sizes of lists
size1 = len(list1)
size2 = len(list2)
#A: Compare Names ERROR: Same Topic
if topic_name1 == topic_name2:
print("Same topic. Try a different combination")
#A: List names are different
else:
#B: Scenerio 1: same length lists
if size1 == size2:
for items in range(size1):
#Store lists
alt_list1.append(list1[items])
alt_list2.append(list2[items])
#B: Scenerio 2: Different length lists
else:
#C: Scenerio 1: First list size is greater
if size1 > size2:
#calculate the different in sizes
diff = size1 - size2
#declare a new size that is equal to the smaller size
adj_size = size1 - diff
#Store values into lists
for items in range(adj_size):
alt_list1.append(list1[items])
alt_list2.append(list2[items])
#C: Scenerio 2: Second list size is greater
else:
#Calculate different size
diff = size2 - size1
#declare a new size that is equal to the smaller size
adj_size = size2 - diff
#Store values into lists
for items in range(adj_size):
#Store values in lists
alt_list1.append(list1[items])
alt_list2.append(list2[items])
#D: Compare lists and return comparison
for x in range(len(alt_list1)):
vec1 = lda1[bagofwords(alt_list1[x],dictionary1)] #[(1,.0356),(2,.0234),...]
vec2 = lda1[bagofwords(alt_list2[x],dictionary1)] #[(1,.0356),(2,.0234),...]
sums = sums + statistic(vec1,vec2)[p]
count+=1
print("Average P-value= ", sums/count)
else:
print("The second topic entered has no name.")
else:
print("The first topic entered has no name.")
else:
print("List 1 is empty")
else:
print("List 2 is empty")
ExterCompare(name["topic1"].name,name["topic1"].abstract,name["topic2"].name,name["topic2"].abstract)
Average P-value= 0.934766320533
In [33]:
store = []
count = []
for r in range(len(name["topic1"].abstract)):
vec = lda1[bagofwords(name["topic1"].abstract[r],dictionary1)]
x_value = [x[0] for x in vec]
if len(x_value) == 1
if x_value[0] not in store:
store.append(x_value[0])
[12]
[34]
[18]
[17]
[17, 47, 54]
[21]
[2, 25]
[36]
[9]
[37]
[20]
[22]
[2, 11]
[49]
[3]
[34]
[20]
[9]
[9]
[34]
[8]
[19]
[37]
[54]
[17]
[47]
[30]
[25]
[51]
[10]
[23]
[50]
[19]
[17]
[2, 5]
[45]
[21]
[54]
[19]
[42]
[21]
[28]
[17]
[44]
[38]
[11]
[50]
[38]
[8]
[39, 49]
[49, 50]
[45]
[47]
[12, 18]
[8, 50]
[18]
[20, 25]
[38]
[31]
[38]
[5]
[52]
[9]
[11]
[42]
[39]
[26]
[5]
[45]
[3, 28]
[43]
[17]
[39]
[41, 42]
[49]
[9]
[26]
[18]
[8]
[2]
[37]
[39, 51]
[8]
[4]
[2, 45]
[12]
[10]
[2]
[19, 22]
[38]
[44]
[26]
[51]
[21]
[30]
[20]
[19]
[39]
[21]
[42]
[41]
[52]
[10]
[11]
[21]
[45]
[17]
[12]
[31]
[25]
[9]
[8]
[5, 18]
[44]
[3, 22]
[20, 35]
[50]
[9]
[11, 21]
[50]
[26]
[4]
[28]
[45, 50]
[2, 11, 45, 52]
[25]
[8]
[11]
[26]
[39]
[41]
[38]
[31, 52]
[35]
[30]
[11]
[45]
[4]
[37]
[25, 34]
[42]
[52]
[23, 42]
[9]
[3]
[22]
[42]
[10]
[45]
[28]
[8, 12]
[43]
[37]
[22, 42]
[30, 36]
[47]
[34]
[12, 26]
[50]
[28]
[22]
[41]
[5, 31, 45]
In [5]:
40
In [ ]:
Content source: jallen2/Research-Trend
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