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mse.py
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import numpy as np
import pandas as pd
import matplotlib.font_manager as fm
import matplotlib.pyplot as plt
font= {'family':'Times New Roman', 'size':28}
def mse1():
mse_sr = [0.983327209020844, 0.41812739301757473, 0.3222282660313606, 0.2780296581602899, 0.23967593021322736, 0.19824492518506304, 0.17586075178164282, 0.1663912100933117, 0.14306147513578052, 0.1322164583664616, 0.12916837192013325, 0.11523562494538189, 0.10502775523067005, 0.1001794874980516, 0.0896319786499872, 0.08955752532161704, 0.07804446979271032, 0.07194472518489621, 0.0724207966697166, 0.06437874241555551, 0.05993483487538874, 0.05633651257139044, 0.05504961858540439, 0.05037456710275297, 0.04624809214334096, 0.04258742904031722, 0.03949676271873209, 0.040191624264325244, 0.038044826234747434, 0.03448781875116563, 0.03457262307235699, 0.03386646499896381, 0.03087145450842433, 0.028860983740328434, 0.02707558658666147, 0.02721580036818489, 0.03285351166184969, 0.028411378641677504, 0.02642780453327052, 0.024417844572649134, 0.027550282004354745, 0.022853633275102498, 0.020768147931093054, 0.02036843391770499, 0.01997204077402852, 0.022565398500935568, 0.018047325632641904, 0.01900887090235941, 0.016502150567109682, 0.016022129009850485, 0.015388836150098171, 0.015332421938715857, 0.014719141510556458, 0.01579346044971923, 0.015151206730735322, 0.013784858008944125, 0.01251290480293257, 0.01278204813586584, 0.012553892094346574, 0.011973160898894464, 0.011929370331315484, 0.012926175463721342, 0.011189048893042869, 0.01087945531641809, 0.01102802071205655, 0.010620849215583466, 0.010409257009092404, 0.009911048313983041, 0.009611956960882, 0.009657241986235729, 0.010542889379146261, 0.010718201675810161, 0.011263493492573853, 0.009278919803323838, 0.008730194838890046, 0.008430420476970202, 0.008472749852832445, 0.00810263784400983, 0.008146790088848556, 0.007853490168200567, 0.007634209099355589, 0.007391147621299212, 0.007890711665253278, 0.007554360562393817, 0.0070639752265072246, 0.006732796676648528, 0.00864294012849179, 0.007981764859681688, 0.007767987786615036, 0.007774252433941405, 0.0068726442549039484, 0.006500784724837474, 0.006595399796833927, 0.006280723447412339, 0.006357343989563257, 0.00597345794415324, 0.006485792251098448, 0.006040776636275917, 0.005696508870441398, 0.005530627551964884]
mse_p = [0.43047926986515295, 0.9442458386265131, 0.6873636672946998, 0.4459945141759603, 0.3184230240800888, 0.8717445966932422, 0.390519089049968, 0.6862002228244046, 0.3511547335324326, 0.5360615747707265, 0.3655180646816434, 0.14563902275060436, 0.3358593793431009, 0.32664710271144803, 0.2658610454867045, 0.2583714698302232, 0.5244124803613273, 0.4963558202256335, 0.3731835609088973, 0.11522505999462024, 0.22444670341995346, 0.07410751949796796, 0.24862571508058345, 0.251021885799409, 0.33232579437918586, 0.26073028370715795, 0.2898538898463494, 0.26077306446954474, 0.1770829770249642, 0.16759417647974387, 0.34636003838609425, 0.42720392921256034, 0.23364126612781227, 0.1463134509558747, 0.11212247094727058, 0.08395024135849448, 0.23220165857706893, 0.12827150971336837, 0.2586250118934696, 0.22712747386474444, 0.1526118556817634, 0.0700541924579953, 0.09447462329292321, 0.03794692364297085, 0.13391913443449385, 0.03904470293785906, 0.06033185704543108, 0.10924128831657068, 0.0895892412023631, 0.02671246861188208, 0.15700125370113832, 0.12913244744307545, 0.12690483339534167, 0.04369576425904365, 0.2162934159898242, 0.04360925330988693, 0.06844578370464796, 0.03545369907492904, 0.06297575739523616, 0.03680347460250024, 0.09365305821487677, 0.09299874300106563, 0.02751155012382009, 0.1084801965832576, 0.10110869473008495, 0.061855962912865346, 0.07040340908207438, 0.07414824802108584, 0.03297969602921588, 0.05612358385179749, 0.10139580008885991, 0.09905951194095303, 0.03941558026125689, 0.17597697022719924, 0.06268821960012547, 0.04301863578255349, 0.10416993694357722, 0.0750800676966971, 0.030906218293377807, 0.059108203441336216, 0.05020713099621624, 0.04463014051417697, 0.06893915003966722, 0.09119480061362714, 0.04460286504675681, 0.04043976081454397, 0.07541236600869175, 0.042630509036727576, 0.04851808797458375, 0.09707449543624, 0.08640528262471728, 0.14948628275070538, 0.06712114911582495, 0.06585915794523453, 0.05199063069630381, 0.02312379352446472, 0.04922995720603796, 0.027720309219290955, 0.0569701497381141, 0.0643410839321441]
mse_p2 = [0.11633582306090354, 0.3476946607575335, 0.5239693274788984, 0.28322164519202964, 0.15228496086815826, 0.45494513112485396, 0.2897900357803133, 0.2772120293498666, 0.2443338348784244, 0.20681617964955298, 0.30469627755920625, 0.2972221303735075, 0.12675371774837574, 0.176404734352045, 0.15374179171462227, 0.4485988916912117, 0.13878800155954005, 0.13581886003162133, 0.781133542355602, 0.23391944400306108, 0.14335172953364625, 0.01986061970503788, 0.20960678518907008, 0.1364881450664288, 0.12779830937870676, 0.1078412974956207, 0.010935749901349151, 0.19015761459912747, 0.07737658910678516, 0.07537484791297723, 0.06377530244578497, 0.12032656386598524, 0.21626298614760864, 0.18371242016293812, 0.13191755361559557, 0.019642198845358876, 0.08827441417452948, 0.08787174153261307, 0.04824522038909564, 0.04272242112768869, 0.0778319026877155, 0.12037704611180439, 0.04162960099197668, 0.17102944734925513, 0.08031341084026175, 0.2245531598518748, 0.05164948147324278, 0.04861296884137801, 0.09224423450262967, 0.039433346618217326, 0.07661432977647657, 0.1376372640599856, 0.01701282911688047, 0.06793250383218315, 0.11297499200405133, 0.050801528406878085, 0.07647407058333196, 0.10387895788067092, 0.07150502953114563, 0.09933178083278439, 0.012715701560068791, 0.03293739609964494, 0.042588497323285235, 0.040717396281327235, 0.03548989514128133, 0.009566765029649794, 0.09963371447875734, 0.08379440917139483, 0.04533732460449524, 0.0770531478143371, 0.054451749249244584, 0.11695235382167467, 0.09325916416844952, 0.05660907394270907, 0.09732695792828575, 0.06288070584094442, 0.05333829381266487, 0.04399493736490081, 0.046754040113223067, 0.014899082752927197, 0.07651939527436523, 0.1140952306040559, 0.055536093214740886, 0.09272134763612638, 0.09888159706591768, 0.052327045352920765, 0.08498610570545208, 0.050778361147406484, 0.04232677032563315, 0.017753044329382635, 0.10374539474756347, 0.04560084985759659, 0.06525750401220307, 0.08899486831348476, 0.02117773201471472, 0.022502983525842558, 0.047584589052236226, 0.10082674513809728, 0.048843838626801174, 0.024806975827551602]
marklist = [0,9,19,29,39,49,59,69,79,89,99]
alpha = 0.6
color_choice = ['blue', 'green', 'orange', 'red']
plt.figure(figsize=(9, 7))
plt.plot(mse_sr,linewidth=2, label="Loss", linestyle="-", color=color_choice[0],
markersize=8, marker="o", markevery=marklist, alpha=alpha)
plt.plot(mse_p2, linewidth=2, label="Rate MSE", color=color_choice[1],markersize=8, marker="^", markevery=marklist,
alpha=alpha)
plt.legend(fontsize=22)
plt.xlabel("Epochs", fontsize=22)
plt.ylabel("MSE", fontsize=22)
plt.xticks(fontsize=22)
plt.yticks(fontsize=22)
name = "/Users/siyac/Documents/Code/Python/sum_rate_unrolling/res_plot/mse1.pdf"
plt.grid()
plt.savefig(name)
plt.show()
def power1():
p1 = [2,3.2,4.3,4.6,4.8]
p2 = [2.3,1.8,0.5,0.3,0.18]
p3 = [1.1,0.5,0.1,0.05,0]
op_1 = [5]*5
op_2 = [0.2]*5
op_3 = [0.01]*5
alpha = 0.6
color_choice = ['blue', 'green', 'orange', 'red']
markersize = 8
linewidth = 2
linewidth2 = 1
plt.figure(figsize=(9, 7))
plt.plot(p1, label="$p_1$", linestyle="-", linewidth=linewidth, color=color_choice[0],
markersize=markersize, marker="o", alpha=alpha)
plt.plot(p2, linewidth=linewidth, label="$p_2$", color=color_choice[1], marker="^",markersize=markersize,
alpha=alpha)
plt.plot(p3, linewidth=linewidth, label="$p_3$", color=color_choice[2], marker=">",markersize=markersize,
alpha=alpha)
plt.plot(op_1, linestyle="--", linewidth=linewidth2, color=color_choice[0],
alpha=alpha)
plt.plot(op_2,linestyle="--", linewidth=linewidth2, color=color_choice[1],
alpha=alpha)
plt.plot(op_3, linestyle="--",linewidth=linewidth2, color=color_choice[2],
alpha=alpha)
plt.xlabel("Layer of $K$", fontsize=22, font = {'family':'Times New Roman'})
plt.ylabel("Power", fontsize=22, font = {'family':'Times New Roman'})
x = [0,1,2,3,4]
plt.xticks(x,fontsize=22, font = font)
plt.yticks(fontsize=22, font = font)
plt.grid()
plt.legend(fontsize=22)
name = "/Users/siyac/Documents/Code/Python/sum_rate_unrolling/res_plot/p1.pdf"
plt.savefig(name)
plt.show()
def mse2():
mse_sr = [2.265443634234662, 0.7087176636271006, 0.5502353593722391, 0.4657945205449101, 0.41704171571265086, 0.3797683027650163, 0.3471903957803928, 0.31556999893809445, 0.29394730864009166, 0.27371956009457654, 0.2539639110297136, 0.23874891686601418, 0.22410248909469507, 0.21564753146059554, 0.20517499178225854, 0.1974337710774799, 0.18738018083021787, 0.18274256177597972, 0.17506677869682824, 0.1710200931069482, 0.16372887438875802, 0.15997773726622486, 0.15621738159096113, 0.15134223630513186, 0.1472016177276614, 0.1435100004110481, 0.14013345106142946, 0.1355758819577642, 0.1315893311674073, 0.13011654860492586, 0.126624985929329, 0.12147336754258706, 0.12029440198422149, 0.11732274719120513, 0.11457521820004869, 0.11148597960343641, 0.10896924325485964, 0.10701545185854128, 0.10440513339968203, 0.10188102754671288, 0.09870851662818125, 0.09847410528582125, 0.09518302342824528, 0.09450332575128413, 0.09310831604936404, 0.09124801158303011, 0.08914334162056009, 0.08759527856404087, 0.08585896787290107, 0.08466515369548856, 0.08368993226964247, 0.08106462954093499, 0.08087545638462568, 0.07902716365480619, 0.07769782374517818, 0.0774203791552332, 0.07513056114313292, 0.07463566733196246, 0.07270500264122313, 0.07178731272548584, 0.06923888847665997, 0.06875485562486304, 0.06782798768675531, 0.06711184845923358, 0.0661797074320849, 0.06500977035838096, 0.06338146186864092, 0.06342252725859537, 0.06144144665130254, 0.061448108439287386, 0.05955539354882831, 0.05899133172528646, 0.05678549325177166, 0.05666732409991171, 0.056259845657940684, 0.055345054678996176, 0.0550919526355008, 0.05396417058796672, 0.05303085118967121, 0.051986269357989334, 0.051264571587685, 0.05003998200447919, 0.05031348877582921, 0.04921403766872202, 0.04903323674971009, 0.0477813131275535, 0.047250470766472666, 0.04664139216449392, 0.0464375399417757, 0.04533195263895592, 0.04450915133061817, 0.043958155403347014, 0.043415100864871485, 0.0436932691222157, 0.04269103666638936, 0.04160560568795686, 0.0410242543328948, 0.04060742270817745, 0.04025566714180362, 0.03926571831263303]
mse_p = [0.43047926986515295, 0.9442458386265131, 0.6873636672946998, 0.4459945141759603, 0.3184230240800888, 0.8717445966932422, 0.390519089049968, 0.6862002228244046, 0.3511547335324326, 0.5360615747707265, 0.3655180646816434, 0.14563902275060436, 0.3358593793431009, 0.32664710271144803, 0.2658610454867045, 0.2583714698302232, 0.5244124803613273, 0.4963558202256335, 0.3731835609088973, 0.11522505999462024, 0.22444670341995346, 0.07410751949796796, 0.24862571508058345, 0.251021885799409, 0.33232579437918586, 0.26073028370715795, 0.2898538898463494, 0.26077306446954474, 0.1770829770249642, 0.16759417647974387, 0.34636003838609425, 0.42720392921256034, 0.23364126612781227, 0.1463134509558747, 0.11212247094727058, 0.08395024135849448, 0.23220165857706893, 0.12827150971336837, 0.2586250118934696, 0.22712747386474444, 0.1526118556817634, 0.0700541924579953, 0.09447462329292321, 0.03794692364297085, 0.13391913443449385, 0.03904470293785906, 0.06033185704543108, 0.10924128831657068, 0.0895892412023631, 0.02671246861188208, 0.15700125370113832, 0.12913244744307545, 0.12690483339534167, 0.04369576425904365, 0.2162934159898242, 0.04360925330988693, 0.06844578370464796, 0.03545369907492904, 0.06297575739523616, 0.03680347460250024, 0.09365305821487677, 0.09299874300106563, 0.02751155012382009, 0.1084801965832576, 0.10110869473008495, 0.061855962912865346, 0.07040340908207438, 0.07414824802108584, 0.03297969602921588, 0.05612358385179749, 0.10139580008885991, 0.09905951194095303, 0.03941558026125689, 0.17597697022719924, 0.06268821960012547, 0.04301863578255349, 0.10416993694357722, 0.0750800676966971, 0.030906218293377807, 0.059108203441336216, 0.05020713099621624, 0.04463014051417697, 0.06893915003966722, 0.09119480061362714, 0.04460286504675681, 0.04043976081454397, 0.07541236600869175, 0.042630509036727576, 0.04851808797458375, 0.09707449543624, 0.08640528262471728, 0.14948628275070538, 0.06712114911582495, 0.06585915794523453, 0.05199063069630381, 0.02312379352446472, 0.04922995720603796, 0.027720309219290955, 0.0569701497381141, 0.0643410839321441]
mse_p2 = [0.90355639798796955, 0.2044698223394618, 0.09441742961870446, 0.15509033879511275, 0.22712676446784846, 0.2376735419865586, 0.2873607267533127, 0.21922579304334464, 0.23587094713177406, 0.24351218781720316, 0.23976162203873047, 0.26023937206773196, 0.295687762913305, 0.25174544243687946, 0.2573752007372471, 0.3029977032322836, 0.21414696639597322, 0.27670560185654414, 0.24298139472682262, 0.198534521087489, 0.2846910432200731, 0.27189319254681976, 0.23260071737839996, 0.25311589807951373, 0.2664717887537649, 0.24602659958031134, 0.2054696883280565, 0.15781762542698752, 0.24664530700455709, 0.2296382484979548, 0.2178888716124145, 0.19758686121174976, 0.19299986104132175, 0.20117645464727824, 0.19213503779872237, 0.1679103053778396, 0.2148285186093085, 0.17458144584020055, 0.16119248294560945, 0.1867708879861989, 0.2120535758260508, 0.21013891329164586, 0.18189876423907037, 0.17732248921795798, 0.17670230360235362, 0.1665274991000813, 0.17927841183024645, 0.19197169459062616, 0.16165617996644868, 0.14801298723640888, 0.19852892269008135, 0.17193850969306868, 0.16831780090319085, 0.19157800855244808, 0.13839121458749631, 0.17232234812745506, 0.19360475908075472, 0.1347643487353594, 0.1664933436619259, 0.1734525142659329, 0.1514970392865881, 0.18884465005653286, 0.1727412048482935, 0.14786622491533108, 0.16079852889357193, 0.12519030867986633, 0.12569803077246167, 0.14463587680647474, 0.12610179974241895, 0.12107559662240362, 0.1723445831567657, 0.10807599593129935, 0.1018866737385682, 0.12871418523661296, 0.1492879972820456, 0.12442059410326153, 0.132294152595436, 0.13728669135154578, 0.12366887176929735, 0.1037522414712974, 0.0994349642193484, 0.11152011491671156, 0.12011470722602648, 0.1197569511791414, 0.07555524934720148, 0.14677131214095146, 0.09919742535891535, 0.10791057129514045, 0.1441457283868856, 0.10212133829868605, 0.11958196664637383, 0.1118990712936531, 0.07934736932936816, 0.10125854840310357, 0.10889149774129314, 0.09423238390762487, 0.11610740855969494, 0.0743657964576799, 0.08637739348745309, 0.08449893101376038]
marklist = [0,9,19,29,39,49,59,69,79,89,99]
alpha = 0.6
color_choice = ['blue', 'green', 'orange', 'red']
plt.figure(figsize=(9, 7))
plt.plot(mse_sr,linewidth=2, label="Loss", linestyle="-", color=color_choice[0],
markersize=8, marker="o", markevery=marklist, alpha=alpha)
plt.plot(mse_p2, linewidth=2, label="Rate MSE", color=color_choice[1],markersize=8, marker="^", markevery=marklist,
alpha=alpha)
plt.legend(fontsize=22)
plt.xlabel("Epochs", fontsize=22)
plt.ylabel("MSE", fontsize=22)
plt.xticks(fontsize=22)
plt.yticks(fontsize=22)
plt.grid()
name = "/Users/siyac/Documents/Code/Python/sum_rate_unrolling/res_plot/mse2.pdf"
plt.savefig(name)
plt.show()
def power2():
p1 = [3.8,3.0,2.4,2.1,2]
p2 = [1.4,2.5,2.9,2.95,3]
p3 = [2.4,4.0,4.5,4.8,5]
op_1 = [2.05]*5
op_2 = [3.05]*5
op_3 = [5.02]*5
alpha = 0.6
color_choice = ['blue', 'green', 'orange', 'red']
markersize = 8
linewidth = 2
linewidth2 = 1
plt.figure(figsize=(9, 7))
plt.plot(p1, label="$p_1$", linestyle="-", linewidth=linewidth, color=color_choice[0],
markersize=markersize, marker="o", alpha=alpha)
plt.plot(p2, linewidth=linewidth, label="$p_2$", color=color_choice[1], marker="^",markersize=markersize,
alpha=alpha)
plt.plot(p3, linewidth=linewidth, label="$p_3$", color=color_choice[2], marker=">",markersize=markersize,
alpha=alpha)
plt.plot(op_1, linestyle="--", linewidth=linewidth2, color=color_choice[0],
alpha=alpha)
plt.plot(op_2,linestyle="--", linewidth=linewidth2, color=color_choice[1],
alpha=alpha)
plt.plot(op_3, linestyle="--",linewidth=linewidth2, color=color_choice[2],
alpha=alpha)
plt.xlabel("Layer of $K$", fontsize=22, font = {'family':'Times New Roman'})
plt.ylabel("Power", fontsize=22, font = {'family':'Times New Roman'})
x = [0,1,2,3,4]
plt.xticks(x,fontsize=22, font = font)
plt.yticks(fontsize=22, font = font)
plt.grid()
plt.legend(fontsize=22)
name = "/Users/siyac/Documents/Code/Python/sum_rate_unrolling/res_plot/p2.pdf"
plt.savefig(name)
plt.show()
def feasibility():
# font_prop = fm.FontProperties(fname=font_path, size=16)
feas1 =[1.1028720051453353, 1.0386512742946066, 1.1068709065556643, 1.1429215784297704, 1.1743583357892267, 1.3329453403860662, 1.110710200936057, 1.1232949608719232, 1.1645290305753142, 1.1927596362411474, 1.1517484627278394, 1.1314940086239564, 1.0738509436018637, 1.0879309721190336, 1.0880043638639434, 1.1120597883737664, 1.0134753560276224, 0.9543318470251378, 0.9570800687370951, 1.068041145540094, 0.9696233115515606, 0.9485180901914704, 1.046543952261936, 0.8605529877520643, 0.9417570030087404, 0.9047655079755522, 0.9306245671732772, 0.9281478527819872, 0.9287597534721601, 0.874688665269197, 0.9042754861954808, 0.9221291110928304, 0.8813942845815546, 0.9392468332730516, 0.8876028491499174, 0.9472167589855588, 0.9049822179221808, 0.9129756394023795, 0.9043661956007224, 0.9402222893113713, 0.9877457683606092, 0.9155184851400474, 0.8900562116488713, 0.9382874502954103, 0.9173052249773378, 0.9562977770934936, 0.9345253365989032, 0.9438264914878522, 0.9483818897449258, 0.9137385349297306, 0.933190267954446, 0.9326158060861142, 0.9266787002772957, 0.9660785004776272, 0.9438411697251987, 0.957273349235718, 0.8691241447669521, 0.9684747645933279, 0.8936475341937035, 0.9456042415769949, 0.9484493274911886, 0.9570145825178278, 0.9499196165937259, 0.9478917188310684, 0.9652470835478842, 0.9310048587478764, 0.9366594921292632, 0.9631998826333871, 0.9479239056942825, 0.9174638743363275, 0.9025252599626565, 0.9362573569383045, 0.9489642515680098, 0.967351403132799, 0.9120240535254208, 0.9726642402744373, 0.9660605890602557, 0.9316593352399107, 0.942033375237132, 0.9141001643187341, 0.9778503907374715, 0.947275827338158, 0.9700332001067989, 0.9329093629000683, 0.972584306955112, 0.9213112036100976, 0.9533525895925022, 0.9517917257332188, 0.9391517930168006, 0.9683143128021582, 0.9327618765841356, 0.9225920850334822, 0.9530742360808818, 0.9703778370704708, 0.9575235024661044, 0.9159447546944559, 0.9725051711190954, 0.9724285763138284, 0.9470705670316496, 0.9635108015750647]
alpha = 0.6
color_choice = ['blue', 'green', 'orange', 'red']
markersize = 8
linewidth = 2
marklist = [0, 9, 19, 29, 39, 49, 59, 69, 79, 89, 99]
alpha = 0.6
color_choice = ['blue', 'green', 'orange', 'red']
plt.figure(figsize=(9, 7))
plt.plot(np.array(feas1)+0.03, linewidth=2, label="Feasiblity", linestyle="-", color=color_choice[0],
markersize=8, marker="o", markevery=marklist, alpha=alpha)
plt.xlabel("Epoch", fontsize=22, font={'family': 'Times New Roman'})
plt.ylabel("Feasiblity", fontsize=22, font={'family': 'Times New Roman'})
plt.xticks(fontsize=22, font=font)
plt.yticks(fontsize=22, font=font)
plt.grid()
plt.legend(prop={'family':'Times New Roman','size':22})
name = "/Users/siyac/Documents/Code/Python/sum_rate_unrolling/res_plot/feas1.pdf"
plt.savefig(name)
plt.show()
def feasibility2():
# font_prop = fm.FontProperties(fname=font_path, size=16)
feas2 =[1.037958209621191, 1.057761859550374, 1.0272628071305248, 1.09048148115747, 1.0601775973730623, 1.1423812705068996, 1.32972034261554, 1.1135976842470754, 1.0926018337116912, 1.0767926536038273, 1.1060645446700161, 1.0024873230567215, 1.074857944113191, 1.075239828483124, 1.0646627805544038, 1.041017164606437, 1.0380066107080366, 1.0230849500789738, 1.0296373854274, 0.9427039115995631, 0.9303368627878056,0.94056149089755475, 0.9670694862439385, 0.9795317981260739, 0.9468288200384133, 0.9660917863065545, 0.9103948019598386, 0.8986829116350586, 0.9562849938385626, 0.9330539599871811, 0.9291270007589892, 0.9690676820338847, 0.97231131318227118, 0.9690003766702877, 0.9264339321384053, 0.8750015671427437, 0.936702360946863, 0.9250079972013697, 0.9038545262858276, 0.8882188566370228, 0.9317886406722687, 0.9371664748622238, 0.9132838888673387, 0.9193792477697754, 0.9375539622404786, 0.9340759054686082, 0.9277780987757712, 0.9198862140710257, 0.971909186024909, 0.9128316410576786, 0.9398153470360845, 0.8774406448189657, 0.9241733469172709, 0.939582551053299, 0.9226388822187127, 0.927394738863419, 0.9483647701014419, 0.9350262314523913, 0.9035707695187273, 0.9559714647154484, 0.9489583852914419, 0.9636292398101442, 0.9342004798321574, 0.9552106333077758, 0.9670213350583978, 0.959314311624917, 0.9296283231872312, 0.9399887542928044, 0.9419183333430731, 0.9443854501361187, 0.919677506648848, 0.9694200386413384, 0.9655665013812328, 0.9447155911204655, 0.9098648814685318, 0.9396286548339359, 0.9373066545134529, 0.9157770772773269, 0.9214665031797887, 0.9210216959536431, 0.9290147878744109, 0.9247374723876126, 0.9103799334309652, 0.9334713281329011, 0.9339283285319885, 0.9405281635717705, 0.9618915937165294, 0.9521746906415741, 0.9290193291789155, 0.9294347177819388, 0.9259171790976103, 0.9305521935835395, 0.9042847155203363, 0.9334358278727607, 0.9387806806209201, 0.9242354069627584, 0.9479164169830785, 0.9379183058195757, 0.9294164326939426, 0.9537691376632583]
alpha = 0.6
color_choice = ['blue', 'green', 'orange', 'red']
markersize = 8
linewidth = 2
marklist = [0, 9, 19, 29, 39, 49, 59, 69, 79, 89, 99]
alpha = 0.6
color_choice = ['blue', 'green', 'orange', 'red']
plt.figure(figsize=(9, 7))
plt.plot(np.array(feas2)+0.03, linewidth=2, label="Feasiblity", linestyle="-", color=color_choice[0],
markersize=8, marker="o", markevery=marklist, alpha=alpha)
plt.xlabel("Epoch", fontsize=22, font={'family': 'Times New Roman'})
plt.ylabel("Feasiblity", fontsize=22, font={'family': 'Times New Roman'})
plt.xticks(fontsize=22, font=font)
plt.yticks(fontsize=22, font=font)
plt.grid()
plt.legend(prop={'family':'Times New Roman','size':22})
plt.ylim(0.8,1.4)
name = "/Users/siyac/Documents/Code/Python/sum_rate_unrolling/res_plot/feas2.pdf"
plt.savefig(name)
plt.show()
if __name__ == '__main__':
# mse1()
# power1()
# mse2()
power2()
# feasibility()