<filename>npzigzag_tests/data.py
import numpy as np 

'''
Main testing data
'''

X_test = [0.9929389179352697,
 0.9919961283074186,
 0.9956143331241806,
 1.010904357160328,
 0.9998309516536492,
 1.0013011865519486,
 1.0124507005606833,
 1.0146187718172361,
 1.0298813783222271,
 1.0329600257471772,
 1.0482840651031844,
 1.051107882752645,
 1.0522755154926726,
 1.057657013206609,
 1.0480710969291211,
 1.0433446576067513,
 1.0268755915003145,
 1.051150695889709,
 1.0681354656779922,
 1.068525536322728,
 1.0754389227954455,
 1.0781379671078515,
 1.0848655833030438,
 1.0828836571507445,
 1.0760344389018428,
 1.0808123199871416,
 1.0813206816366525,
 1.0838246129094629,
 1.0830029879120597,
 1.0731930057915398,
 1.0683874287927573,
 1.0710144479504033,
 1.065881034860546,
 1.0655369187088293,
 1.0590156755313462,
 1.0647698717385115,
 1.0691236331897176,
 1.061752705490614,
 1.0702093153805121,
 1.058987817056342,
 1.0640650372875193,
 1.090745634533342,
 1.0956801937639618,
 1.1030202579342105,
 1.1195067974954354,
 1.1124667074425987,
 1.121820860814829,
 1.1208159249959253,
 1.126945141373338,
 1.1408167075880362,
 1.1389667353191235,
 1.141627408533585,
 1.1240082540901637,
 1.096324684580606,
 1.0800142109032431,
 1.0772950191131643,
 1.098386283577075,
 1.0817902798317747,
 1.0765675056837738,
 1.0712938819134574,
 1.0654274951852103,
 1.0531799890461373,
 1.051407104943668,
 1.0686140429646493,
 1.0572123157519426,
 1.0433955348292974,
 1.0472568148978483,
 1.04053697488696,
 1.0340600766565389,
 1.0291164639559711,
 1.0381577717665573,
 1.0328547849146563,
 1.0244799703534542,
 1.0051986859673223,
 1.0062327788051795,
 1.0131377093348444,
 1.00656250469066,
 1.0273072834788137,
 1.0399527696356974,
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 1.0497155161022487,
 1.0345191566639262,
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 1.0418091160740446,
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 1.075767998472293,
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 1.0619048723611806,
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 1.065113185198205,
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 1.0193043390039955,
 1.0240527213167807,
 1.047037628878088,
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 1.0460563424624933,
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 1.0338525192770394,
 1.0326443743585312,
 1.0240491805440168,
 1.032470135732104,
 1.0167012754363818,
 1.0265285778896414,
 1.0394289983665121,
 1.054988726200105,
 1.043819788965867,
 1.0417272500786763,
 1.0360646525817512,
 1.0232551127358924,
 1.0231226781643132,
 1.0447828153520213,
 1.023415299358553,
 1.0131429278771888,
 1.0167105878092462,
 1.0043597935180275,
 0.9963442663043433,
 1.0091486974790163,
 1.0015154872047716,
 0.9974595250969782,
 1.0031040967414462,
 1.0011681533960977,
 0.9975684423391529,
 1.0019107510751726,
 0.9956492942010072,
 0.9969417125279425,
 1.0048932522155498,
 1.0116216706009016,
 1.0014098903608157,
 1.0175357656150728,
 1.0207713984699207,
 1.0106464973518277,
 1.0073194815054853,
 1.0112606378404039,
 1.0119153753434815,
 1.0180205297472413,
 1.0234394939477793,
 1.0109922553574193,
 1.0194056264683506,
 1.0345222655733735,
 1.0369179994089097,
 1.0294628294850032,
 1.0211128907942857,
 1.0230024519635448,
 1.0054224836184593,
 1.00723100286296,
 0.998426835818192,
 0.9817474122053765,
 1.002304471593177,
 0.994916509214354,
 0.9884992835652582,
 0.9797649023097,
 1.012759860958251,
 1.0197092901966813,
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 1.0236168889109751,
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 0.9621094169060026,
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 0.9575738673970743,
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 0.9556286852446895,
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 0.947541269191434,
 0.9533065659436963,
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 0.9611417020227025,
 0.9502531337775103,
 0.9402823743451135,
 0.9378084736223776,
 0.9469622393889608,
 0.9589101374055833,
 0.9504043294516106,
 0.9564615658089461,
 0.9723178988334111,
 0.9678387942015512,
 0.9693817895233943,
 0.982091791106381,
 1.0048701407595844,
 1.0131696227637126,
 1.0253368513034407,
 1.022064331024014,
 1.024323044217045,
 1.044093581461997,
 1.0441236505836022,
 1.050592896487017,
 1.0549179646995135,
 1.056658522440116,
 1.0523396073778066,
 1.045766540414464,
 1.0459742319012704,
 1.036436707069971,
 1.0287158175241407,
 1.0378777940176191,
 1.045805869108319,
 1.053672390262815,
 1.0538037391790382,
 1.0430949590297913,
 1.0377557855073425,
 1.0439455567180342,
 1.033818445292824,
 1.0257691779862037,
 1.037525907278956,
 1.0394493726226754,
 1.0375339305911944,
 1.027382944060993,
 1.0331278239960286,
 1.0400695639247068,
 1.04614550406493,
 1.0357159877624749,
 1.052550593267816,
 1.0616425343897353,
 1.0489964941791359,
 1.06726114685677,
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 1.1077957031390533,
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 1.1155661898582054,
 1.1002688183934526,
 1.110437163817041,
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 1.0883826686586087,
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 1.1032424173108226,
 1.1104770234582935,
 1.106421950710725,
 1.1122432464636582,
 1.0901487121702798,
 1.0645462044444762,
 1.0532125883197734,
 1.060048339486053,
 1.0620622053146107,
 1.0689954873814043,
 1.061084728787173,
 1.0478625955873508,
 1.0447132869460316,
 1.033543480076777,
 1.0533775934459073,
 1.0393240887744655,
 1.0471476416863934,
 1.0332551952417517,
 1.0188285442669611,
 1.02960866380806,
 1.0236378123768342,
 1.0370878944570454,
 1.034563872008873,
 1.0332926352294842,
 1.0211288050757203,
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 1.02652379133174,
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 1.053818116408406,
 1.0411747142322272,
 1.0395251445532865,
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 1.0637455479852227,
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 1.0546538642751497,
 1.041519463757762,
 1.0721802069641548,
 1.0499854503498838,
 1.0628397548256807,
 1.0593031635846903,
 1.0669655438961991,
 1.065438624022075,
 1.0662394113344382,
 1.0785676830814652,
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 1.0589205974614342,
 1.049047879120685,
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 1.0586650715675865,
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 1.0757395764806326,
 1.066824490880527,
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 1.0430738005402393,
 1.0374297985543994,
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 1.0472499137553948,
 1.012459688363481,
 1.0178768570106225,
 1.014407211657858,
 1.0020238175119036,
 0.996568760620016,
 0.9887888906449459,
 0.996742275431464,
 0.9850682805142004,
 0.9831083413838815,
 0.9851646708802607,
 0.9833297863800236,
 0.9785526678244122,
 0.9609215145797618,
 0.9582046268453057,
 0.9493317613113429,
 0.9495755532904616,
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 0.9357916843335797,
 0.9428180177356033,
 0.9405599315738417,
 0.933138149380716,
 0.9210765892235219,
 0.926870257470325,
 0.9239431070538936,
 0.9190046435365433,
 0.9117920513273817,
 0.9203869505683016,
 0.9189167057412203,
 0.9263587451795212,
 0.9323926332761028,
 0.9292484910808688,
 0.9316765362229386,
 0.9343220153380857,
 0.927605073087879,
 0.9140179496000729,
 0.9118885324740436,
 0.9153992707443134,
 0.918451038956838,
 0.9181969283012401,
 0.9140223899410405,
 0.9156986500576224,
 0.9187516864996649,
 0.9137404557536118,
 0.9048586120812175,
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 0.8934518465823328,
 0.8875450953014373,
 0.895595037556334,
 0.8892299399528029,
 0.8857785135660885,
 0.8951912831769755,
 0.91413590031441]

X_test_result = np.array(
     [  0,  13,  16,  44,  73,  78,  85,  93, 110, 113, 118, 123, 128,
       129, 134, 158, 165, 166, 169, 178, 204, 209, 232, 236, 241, 288,
       310, 318, 335, 339, 347, 352, 360, 371, 378, 386, 395, 399, 431,
       445, 456, 459, 468, 482, 493, 504, 513, 535, 546, 556, 561, 567,
       572, 592, 599, 607, 618, 628, 636, 639, 692, 696, 715, 736, 738,
       739, 777, 801, 814, 818, 833, 853, 864, 866, 868, 871, 879, 891,
       902, 911, 994])

<filename>npzigzag_tests/data.py
import numpy as np 

'''
Main testing data
'''

X_test = [0.9929389179352697,
 0.9919961283074186,
 0.9956143331241806,
 1.010904357160328,
 0.9998309516536492,
 1.0013011865519486,
 1.0124507005606833,
 1.0146187718172361,
 1.0298813783222271,
 1.0329600257471772,
 1.0482840651031844,
 1.051107882752645,
 1.0522755154926726,
 1.057657013206609,
 1.0480710969291211,
 1.0433446576067513,
 1.0268755915003145,
 1.051150695889709,
 1.0681354656779922,
 1.068525536322728,
 1.0754389227954455,
 1.0781379671078515,
 1.0848655833030438,
 1.0828836571507445,
 1.0760344389018428,
 1.0808123199871416,
 1.0813206816366525,
 1.0838246129094629,
 1.0830029879120597,
 1.0731930057915398,
 1.0683874287927573,
 1.0710144479504033,
 1.065881034860546,
 1.0655369187088293,
 1.0590156755313462,
 1.0647698717385115,
 1.0691236331897176,
 1.061752705490614,
 1.0702093153805121,
 1.058987817056342,
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 1.0956801937639618,
 1.1030202579342105,
 1.1195067974954354,
 1.1124667074425987,
 1.121820860814829,
 1.1208159249959253,
 1.126945141373338,
 1.1408167075880362,
 1.1389667353191235,
 1.141627408533585,
 1.1240082540901637,
 1.096324684580606,
 1.0800142109032431,
 1.0772950191131643,
 1.098386283577075,
 1.0817902798317747,
 1.0765675056837738,
 1.0712938819134574,
 1.0654274951852103,
 1.0531799890461373,
 1.051407104943668,
 1.0686140429646493,
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 1.0433955348292974,
 1.0472568148978483,
 1.04053697488696,
 1.0340600766565389,
 1.0291164639559711,
 1.0381577717665573,
 1.0328547849146563,
 1.0244799703534542,
 1.0051986859673223,
 1.0062327788051795,
 1.0131377093348444,
 1.00656250469066,
 1.0273072834788137,
 1.0399527696356974,
 1.0304561441640363,
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 1.0299629346411587,
 1.0271677518901958,
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 1.0418091160740446,
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 1.0772954463606292,
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 1.0750661439351408,
 1.0782180931497736,
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 1.075767998472293,
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 1.065113185198205,
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 1.0384359469413817,
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 1.0326443743585312,
 1.0240491805440168,
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 1.0394289983665121,
 1.054988726200105,
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 0.9956492942010072,
 0.9969417125279425,
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 1.0345222655733735,
 1.0369179994089097,
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 1.0054224836184593,
 1.00723100286296,
 0.998426835818192,
 0.9817474122053765,
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 0.994916509214354,
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 1.044093581461997,
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X_test_result = np.array(
     [  0,  13,  16,  44,  73,  78,  85,  93, 110, 113, 118, 123, 128,
       129, 134, 158, 165, 166, 169, 178, 204, 209, 232, 236, 241, 288,
       310, 318, 335, 339, 347, 352, 360, 371, 378, 386, 395, 399, 431,
       445, 456, 459, 468, 482, 493, 504, 513, 535, 546, 556, 561, 567,
       572, 592, 599, 607, 618, 628, 636, 639, 692, 696, 715, 736, 738,
       739, 777, 801, 814, 818, 833, 853, 864, 866, 868, 871, 879, 891,
       902, 911, 994])

Main testing data