From 5bfca064004f79a090be861030b022390d20517e Mon Sep 17 00:00:00 2001 From: art Date: Tue, 8 Oct 2019 13:35:32 -0500 Subject: [PATCH] jacob fix poly regression! --- .../__pycache__/analysis.cpython-37.pyc | Bin 20572 -> 20874 bytes data analysis/analysis/analysis.py | 20 +++++++++--------- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/data analysis/analysis/__pycache__/analysis.cpython-37.pyc b/data analysis/analysis/__pycache__/analysis.cpython-37.pyc index 88adc4a7b191c8f8e0843a334bd81922afc783cb..f26d686f7395c0b983838d8f6e8fce1cf6a03713 100644 GIT binary patch delta 3691 zcmb_fdu)`)5&!nP`|jm?`2EKCZ4N&;12%zx@e`=QhCqYi3ULqbVT|w2X73CbnK}U% zs8f*0R8>$&IEND2(nJ-PK%2fvlTaE+NoZ+5NYkoPRgttxsz|9SMd{3*eWr$re|pya zzM0?5&d%=ae0#nfW#gw=MYF?UQ}A=;`pK=IY*CcIkTUggq0ormu<3Y3zedR_9m}Xcs9=&JO20aMR|S`&*NoB887FyG36lh=1V3YnF^jH znOO;D4!27t30`9EQSzzeF3A)o;#Bc!$rL4+8a_`l#R5Gk2FG$aLlf4eNGsf9v=*@hOy#;Mq zci0W^WJlPa;Y{|k>~GML^E69_+d19rV_2GdiEW3^b9W)`$_pc&n>7!jd95gI&L2j6 zH@_co`7FY760-_gar~Bo3g(2df{)l|v7y;1j4g%3g*VtL*iz(XE;v*)I@-eMj9^-d z!KoIjJt~R#A$jrB&JL-ETn6xOc^f0kwwx+K9%LeuHR@C{czo z%TW;39A!d9n=z`6XcLSnyRykXq1?@l?#k{`MpSe$N*7y~f=-4JWrR(rSOY_+NXE%U z1A^PATfaE9zq)C?FVYw46_27tHws_G?>6bSh~F0scXMCFCn#y%PDD3a(KW0aNz<*8 zi}-_sVhDVmv=XYy8rp|U6cmF|wJ{^JF$+s)cGbe1%&EdN#mnH4;)k^)^mq)87x$XJ zf|3}e0Gb`e9wf7%x}-VlaWa$;lp?rQahIn9nH}(4$tD(rPfNBk0gsk$b?l+Z<<1*~ z2YjUq;Gd-hYy?uu%Ge!PRJLH$Z{BI%X^C1V6w%0&Ln=@4+Nc%B6mJqw=Nb64^Gu$F zPls$d#p}c)oy#qFq%C-)U8v=8D{5BMs4?@o12qR~DX7h&rsOu%Qc)|Q_TzTc(ombt zmt%kOBvjKek&I~Ogu)AX5ij0usX z?sPSo_$qA=(^*Jl({4$~A&Cs%2a9_wYueqQ8~Q>KX%dIww7Xn8j9#z6HFpe8`hmF% z*&(<%*P|Um_Eq?B?ml)Y)>~P~*ikrAmCvrgnW_U7&!JH_3ICwq7ZHGp$dgRa7YPmp z#IvZl4dN7ZR#&oXu&;V$GEstti73GtxL%#l-hh8p-(2(z5d{Pd2yT;%B~B*T>7g6R zu8LD+Ix0_06vdm6KJUqeGWG;nC?DM%?&}Q>^znGG=ZT-nisaJ--;yEDpm+;DnAcu0 zJ&|0E8Vc#=4U=JBpaD?=NnvuWdp8Vm(TDNIN(tm|dYA@D(hXN*& z7UxL)E`gkmvJlg$8{-Q<4S$;N(J1Q=A!EV%8kw~WBuB~^a^zVW{_RAxC`s|;H>3D| z?EC`8njV-ujhYet{$=~-gs|IZc4l-5wRdsjs4_Xu80ROB;o*00FD-`b+&7+*&h ziXX$1^`HNLmSLjM78M+I1@P~BJ2)SD`~mCfpj;lb9uw6HUwy9n+@}^-K449zP3Wwu z@b&siIJWrZ75CKV7)?nnd(SZ=PIiH;&*W)2O6)N>vZO$ZqL>ZmmRwy;y)7t^u7xAM zp8oi->2o-}BGcrVBVS|BKp-qmkZ2D1_jCs%d+%zY0=V^XN5*su$vmUzJXx!+p_mr) zEs;v&Hh2!4p*c8FI= zK2I>8;70`b`%Mw62-*pLOz;zepAt~=;%5XumnUr;#YSjvY-gSDjmEAO^umm9uH2$^ zq^>8Rb{Et(g3g?vA_-cNpeEDJ9UI!#41~kt5u(QF1(D%I@d>(*9zwxlHQJ2^qXu7Y zN;j&^WPrU*ui-C;rOjVrr{L-4S!@ejXx?IZ1AXZQ2e~c9tQ(fKJcj0zEjf(Cg_gCJ zUrm`Etp%(Xs#^O}FC!&>O%Na>Rvpf^7Hd&d0&uN0kpBcq;sAnf7#!FmC`8sw{Gsn(r+9H$Qa@}Srape-;gS2 zkdG! zGQ_jIh2kmLy!wPco+Mcs5b&a!4odOj8zGaXInX{Md^{+cbd7#BwavV)-$Bz0(6uI* zCHFwSTx7O#@8~|gwWdU)sa^s@NB?4~4KD4!_g_~B`zG&5U0vTga}J6ZgR>n?7OI__ z4#7tqCF~-kcb1R7g_O8TaE*-VB7@{if-HjD1aA`1vC}PsVqnlOBEw=QaaRcBQS2uv zN+1u&AW2jaL3=4^nX&-!Qrsamn*j7`mq!0^ZGx+v?o}1y7sOs7c#Ytf1my_278(j} z^NExxd2l%XLy@*ZxA;Q7z;L)PENnDjH#DqW-7GJ|*GV@MSO}~HNd$HR2SE+NJOU3v xEdjN%aA8+x!Fy|6_8q7iN0pSRhK9i=BYrhVTbBY=>t?5=rfPTpIY*UE{{fiheL?^L delta 3449 zcmb_edvKIj760yLpUFOw&13V1yxD{7 z*kov@;!Mernm&kuQd@^_XLlOfW{KI7S z_nq@Q_nv#sJ?GxS z8?9Cqd9-FFt#USU$x*1~1Z^R^YG)a=fD09e>PRkj{L+cWG{2D-@QSZ7UddCZl_{nc zNTv{(DxM~p!bN5cw@Ri6W|%upvugNS$*f-Va~JnWreu*>$Gws%U1Vx`on*?o6kg9A z(+YP?>C|%68hAQt>8Mq3=b++Vf6!9vsrA%*yxuzBwG2hs^95gc)E}G}ZAwh&zT||D zvl=r@xW{g$Sz=-elkAV6 z=FKt(e3Cc9{tAQn7uYD|6nqi!;esgQ4T+J$Nt8b-97o(yM0lfM9XwyS0VPjyJI;?5 zS1}trS^P2kcjDNp491#aZuM2R8KNa_W`~m{an=q+r7jpQn=nwT^ugt_QR9A;!~tNn znT9kJ2at3@puEjBNrp0las;W(c(MoopA>SL|j!IF7g%{){*deU%lMNwl&) z&arcXCe`FOjhV*G^OjSJ=;qF_j%TW=Jd0;fD{30g;Vyhyc`nbxr;X?H0({!B28G;= zH85ih9H

3u+eB(orksHq>mWWuUf-dWNT>=0t5ZbqBXjb?MYhR78lubx-%%4w#$mht6#Q?TLIAe+lGu<&kSOYo}QI-_u4)?4AcySJJoaUX39 z6NlwPV_}g?tb{z0$Z!VkuAX)+4NuWh0^yi6i6iiSb*145j`}v-s7_!BU#w|harm&t zYltKJ0_3kf$j&8BtX<8Dk0P;R0nI!z5(o!kBO`CZmAf9S`UVcsjKUxB`(ol4jW3W) z$QKLk3yQ~3ai@q`IOM5e7vMQhS2|IGrisT1=HP+0T*Cm0uR`IvUp0Q6$YKJSs!{qA zCl=YI={u0a4w)s>QQ1r6@Lg#0ezigRe}XI&lV*wrMne+;o^wG9z6$teFNE+W_vfn1KF5KE!!lZihC_6DzkqW%CjH4J!U)Y6fhDSgP9^ECYw!OGx=k-U=l zpn#o!M7GG@)y|9*qML3+?I-99Ktyt zGC1B;1NQZsV9jS9{Xgb&1M^u0-g}PSA*a6oB_|`*OQ(b8#(eNJ)!ZquojCd*l-Tb( zZz<42wB8W{xuu_aP8Qllp6G|*+`YvH+Bqxy`rgaEbnXQ?(hSj>|nv?aZEC;7fAXJ0qvalEWwKe-zE4Sg4-q%B%dXyBlteSE`n}? z9)ceboFjONfPxn<6TBi@Z3@K(IMCVy7h8uq50l{tf!v_&Bn=SIX%}>A1ht%?@(9XD z%p+)~zQK+y6Va&XCu)kWw`?1V5r&^aO(>WxdaFJ~ZvbzW_MYBAo^fjygAtc$)X#~f^$M2!?VAB6x6-^fjEWB$q z-9Y=w)hXs@;p)~_GgTD7PiCQLTN$3|)@_yXpCBbJ5xhmlERjue4uOl{8o`eUs3|pb zL`+2dA~q?;h`UH28+Du{d4lu4bJ@Q= HuI&B~IZiFw diff --git a/data analysis/analysis/analysis.py b/data analysis/analysis/analysis.py index 1ee04992..db6c764d 100644 --- a/data analysis/analysis/analysis.py +++ b/data analysis/analysis/analysis.py @@ -262,26 +262,26 @@ def regression(device, inputs, outputs, args, loss = torch.nn.MSELoss(), _iterat if 'lin' in args: model = Regression.SGDTrain(Regression.LinearRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float).cuda(), torch.tensor([outputs]).to(torch.float).cuda(), iterations=_iterations, learning_rate=lr, return_losses=True) - regressions.append([model[0].parameters, model[1][::-1][0]]) + regressions.append((model[0].parameters, model[1][::-1][0])) if 'log' in args: model = Regression.SGDTrain(Regression.LogRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float).cuda(), torch.tensor(outputs).to(torch.float).cuda(), iterations=_iterations, learning_rate=lr, return_losses=True) - regressions.append([model[0].parameters, model[1][::-1][0]]) + regressions.append((model[0].parameters, model[1][::-1][0])) if 'exp' in args: model = Regression.SGDTrain(Regression.ExpRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float).cuda(), torch.tensor(outputs).to(torch.float).cuda(), iterations=_iterations, learning_rate=lr, return_losses=True) - regressions.append([model[0].parameters, model[1][::-1][0]]) + regressions.append((model[0].parameters, model[1][::-1][0])) - #if 'poly' in args: + #if 'ply' in args: #TODO because Jacob hasnt fixed regression.py if 'sig' in args: model = Regression.SGDTrain(Regression.SigmoidalRegKernelArthur(len(inputs)), torch.tensor(inputs).to(torch.float).cuda(), torch.tensor(outputs).to(torch.float).cuda(), iterations=_iterations, learning_rate=lr, return_losses=True) - regressions.append([model[0].parameters, model[1][::-1][0]]) + regressions.append((model[0].parameters, model[1][::-1][0])) else: @@ -290,26 +290,26 @@ def regression(device, inputs, outputs, args, loss = torch.nn.MSELoss(), _iterat if 'linear' in args: model = Regression.SGDTrain(Regression.LinearRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float), torch.tensor(outputs).to(torch.float), iterations=_iterations, learning_rate=lr, return_losses=True) - regressions.append([model[0].parameters, model[1][::-1][0]]) + regressions.append((model[0].parameters, model[1][::-1][0])) if 'log' in args: model = Regression.SGDTrain(Regression.LogRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float), torch.tensor(outputs).to(torch.float), iterations=_iterations, learning_rate=lr, return_losses=True) - regressions.append([model[0].parameters, model[1][::-1][0]]) + regressions.append((model[0].parameters, model[1][::-1][0])) if 'exp' in args: model = Regression.SGDTrain(Regression.ExpRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float), torch.tensor(outputs).to(torch.float), iterations=_iterations, learning_rate=lr, return_losses=True) - regressions.append([model[0].parameters, model[1][::-1][0]]) + regressions.append((model[0].parameters, model[1][::-1][0])) - #if 'poly' in args: + #if 'ply' in args: #TODO because Jacob hasnt fixed regression.py if 'sig' in args: model = Regression.SGDTrain(Regression.SigmoidalRegKernelArthur(len(inputs)), torch.tensor(inputs).to(torch.float), torch.tensor(outputs).to(torch.float), iterations=_iterations, learning_rate=lr, return_losses=True) - regressions.append([model[0].parameters, model[1][::-1][0]]) + regressions.append((model[0].parameters, model[1][::-1][0])) return regressions