From 392d79f28c02be7944ca8c91dc6d94fea97b9666 Mon Sep 17 00:00:00 2001 From: Cem Date: Wed, 16 Mar 2016 20:47:55 +0200 Subject: [PATCH] Added files via upload --- Assignment1_Cem_Sanal_2015719243.ipynb | 1342 ++++++++++++++++++++++++ 1 file changed, 1342 insertions(+) create mode 100644 Assignment1_Cem_Sanal_2015719243.ipynb diff --git a/Assignment1_Cem_Sanal_2015719243.ipynb b/Assignment1_Cem_Sanal_2015719243.ipynb new file mode 100644 index 0000000..8dc3d72 --- /dev/null +++ b/Assignment1_Cem_Sanal_2015719243.ipynb @@ -0,0 +1,1342 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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119672841941123671600813332399275688939647364123830
219683187681253751896713948434416261647062403333260
319693543981373452054015529486556947852959456853240
419703698081377712091615980521527073060994507061950
519714038801536762238017140570117343368417534964740
619724600871872722555918504627967892074402574768870
719735433182403603005520011710438678080860642077890
819746479473131603412221404810259530986028733895610
91975785920403546406232376398579108381914218450111570
1019769201414888944606625388116861122176969849224145480
1119771042239560424519992709613421313809310212710137181500
1219781142561624438568362855914469514655110989010698208940
131979156640568868761596306341552781570951203781129122875318571
141980169668174225264707327831658211648931379311177724090352427
151981180274277643266514338391722691723721605571245926246382054
161982190192681146569598354321787621807721827951338630160399556
171983204124485635073585384781864271902772173271470533532430563
181984221517491957780697436381981061977212563381631239445463340
191985239135798344487951471192125052054962890521763945561502590
2019862641353108723497917507982247552171113273261944850819565945
21198728872871193021106314535542334802258723698942123655129628787
22198831402651310257112885561722407182341664208892330158300683577
23198933882591434830118026588592485672413924728532506060191728481
24199037506781649879125399637002634072573535319412651963024769456
25199141019751864344133632689732808912734095904882860666981794651
26199245847172181388145312755923081802871606553473115872000828580
27199352506222619852159900842543542903055117433203370379233870559
28199456067122861640166424875453744733137717887863549583072895506
29199559228593058511173051901973977433214218199223727287214937528
30199663057073274156182694949784427883332698541504021295318988142
31199768634623570105197057101896529838353586905121453271071511053381
32199873715413838288211495108361626004371163940935499251179131107457
33199977585114072326221683112186692935378967975746521051209371131626
342000832044944221802358851184547944593942831011284556771291571159070
352001852195645348032393811193068331753964931031221574901310191179068
362002865517046001402417001200978753813990251046907587901330031180127
372003890384347003432453941235009734574050341073415605111379331184256
382004102363575400440318954152712125986764742012186772800401210283
392005111458265772745338539163390147505767692914410663033301247767
402006122273936140992357523175949169562470953518228313426001290679
412007130229456472156372601189128189045972920220034923857301327334
422008137653956796629383548199934206600774421721813833510001358577
432009143167007093964384053201033220495172730223032613410401368032
442010150956037544871386973208510239903872635923894883549201404872
452011160895288113111389435219906261110472845825271903411601466208
462012170334138648875396119235949279460675165026577223307101515421
472013179394479283923421848219885293305075595027228263614801565817
482014188287219857915427264211200306247977372828284664073101626938
4920151988206910509258446822216566323530480261529388214513801687545
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"15 1981 1802742 776432 66514 33839 172269 172372 160557 \n", + "16 1982 1901926 811465 69598 35432 178762 180772 182795 \n", + "17 1983 2041244 856350 73585 38478 186427 190277 217327 \n", + "18 1984 2215174 919577 80697 43638 198106 197721 256338 \n", + "19 1985 2391357 983444 87951 47119 212505 205496 289052 \n", + "20 1986 2641353 1087234 97917 50798 224755 217111 327326 \n", + "21 1987 2887287 1193021 106314 53554 233480 225872 369894 \n", + "22 1988 3140265 1310257 112885 56172 240718 234166 420889 \n", + "23 1989 3388259 1434830 118026 58859 248567 241392 472853 \n", + "24 1990 3750678 1649879 125399 63700 263407 257353 531941 \n", + "25 1991 4101975 1864344 133632 68973 280891 273409 590488 \n", + "26 1992 4584717 2181388 145312 75592 308180 287160 655347 \n", + "27 1993 5250622 2619852 159900 84254 354290 305511 743320 \n", + "28 1994 5606712 2861640 166424 87545 374473 313771 788786 \n", + "29 1995 5922859 3058511 173051 90197 397743 321421 819922 \n", + "30 1996 6305707 3274156 182694 94978 442788 333269 854150 \n", + "31 1997 6863462 3570105 197057 101896 529838 353586 905121 \n", + "32 1998 7371541 3838288 211495 108361 626004 371163 940935 \n", + "33 1999 7758511 4072326 221683 112186 692935 378967 975746 \n", + "34 2000 8320449 4422180 235885 118454 794459 394283 1011284 \n", + "35 2001 8521956 4534803 239381 119306 833175 396493 1031221 \n", + "36 2002 8655170 4600140 241700 120097 875381 399025 1046907 \n", + "37 2003 8903843 4700343 245394 123500 973457 405034 1073415 \n", + "38 2004 10236357 5400440 318954 152712 1259867 647420 1218677 \n", + "39 2005 11145826 5772745 338539 163390 1475057 676929 1441066 \n", + "40 2006 12227393 6140992 357523 175949 1695624 709535 1822831 \n", + "41 2007 13022945 6472156 372601 189128 1890459 729202 2003492 \n", + "42 2008 13765395 6796629 383548 199934 2066007 744217 2181383 \n", + "43 2009 14316700 7093964 384053 201033 2204951 727302 2303261 \n", + "44 2010 15095603 7544871 386973 208510 2399038 726359 2389488 \n", + "45 2011 16089528 8113111 389435 219906 2611104 728458 2527190 \n", + "46 2012 17033413 8648875 396119 235949 2794606 751650 2657722 \n", + "47 2013 17939447 9283923 421848 219885 2933050 755950 2722826 \n", + "48 2014 18828721 9857915 427264 211200 3062479 773728 2828466 \n", + "49 2015 19882069 10509258 446822 216566 3235304 802615 2938821 \n", + "\n", + " SpecialVehicles Machinery Tractor \n", + "0 3610 2452 0 \n", + "1 3641 2383 0 \n", + "2 4033 3326 0 \n", + "3 4568 5324 0 \n", + "4 5070 6195 0 \n", + "5 5349 6474 0 \n", + "6 5747 6887 0 \n", + "7 6420 7789 0 \n", + "8 7338 9561 0 \n", + "9 8450 11157 0 \n", + "10 9224 14548 0 \n", + "11 10137 18150 0 \n", + "12 10698 20894 0 \n", + "13 11291 22875 318571 \n", + "14 11777 24090 352427 \n", + "15 12459 26246 382054 \n", + "16 13386 30160 399556 \n", + "17 14705 33532 430563 \n", + "18 16312 39445 463340 \n", + "19 17639 45561 502590 \n", + "20 19448 50819 565945 \n", + "21 21236 55129 628787 \n", + "22 23301 58300 683577 \n", + "23 25060 60191 728481 \n", + "24 26519 63024 769456 \n", + "25 28606 66981 794651 \n", + "26 31158 72000 828580 \n", + "27 33703 79233 870559 \n", + "28 35495 83072 895506 \n", + "29 37272 87214 937528 \n", + "30 40212 95318 988142 \n", + "31 45327 107151 1053381 \n", + "32 49925 117913 1107457 \n", + "33 52105 120937 1131626 \n", + "34 55677 129157 1159070 \n", + "35 57490 131019 1179068 \n", + "36 58790 133003 1180127 \n", + "37 60511 137933 1184256 \n", + "38 28004 0 1210283 \n", + "39 30333 0 1247767 \n", + "40 34260 0 1290679 \n", + "41 38573 0 1327334 \n", + "42 35100 0 1358577 \n", + "43 34104 0 1368032 \n", + "44 35492 0 1404872 \n", + "45 34116 0 1466208 \n", + "46 33071 0 1515421 \n", + "47 36148 0 1565817 \n", + "48 40731 0 1626938 \n", + "49 45138 0 1687545 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Cem Şanal\n", + "#I changed Null datas to 0.\n", + "%matplotlib inline\n", + "\n", + "import scipy as sc\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib as mpl\n", + "import matplotlib.pylab as plt\n", + "\n", + "df_arac = pd.read_csv(u'data/arac.csv',sep=';')\n", + "df_arac" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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FxTWFKSKN2cCBMGwYvPoqHHNM0tE0uNLSUkpLSxts/+kM1L8LPAxMIwyWA/FM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\nfbqZDQHGRwliI3Cpu79pZjM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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# More than half of the graphs are can't shown with fitting AR Model.\n", + "# We can't obtain effective results with using AR model. Therefore, I prefer Polynomial. \n", + "\n", + "BaseYear = 1966\n", + "x = np.matrix(df_arac.Year[0:]).T-BaseYear\n", + "y = np.matrix(df_arac.Car[0:]).T/1000000.0\n", + "z = np.matrix(df_arac.Minibus[0:]).T/1000000.0\n", + "t = np.matrix(df_arac.Bus[0:]).T/1000000.0\n", + "c = np.matrix(df_arac.SmallTruck[0:]).T/1000000.0\n", + "tr = np.matrix(df_arac.Truck[0:]).T/1000000.0\n", + "mot = np.matrix(df_arac.Motorcycle[0:]).T/1000000.0\n", + "sv = np.matrix(df_arac.SpecialVehicles[0:]).T/1000000.0\n", + "mac = np.matrix(df_arac.Machinery[0:]).T/1000000.0\n", + "trac = np.matrix(df_arac.Tractor[0:]).T/1000000.0\n", + "\n", + "#Polynomial\n", + "# Setup the vandermonde matrix\n", + "N = len(x)\n", + "degree = 3\n", + "\n", + "A = np.hstack((np.power(x,i) for i in range(degree+1)))\n", + "xx = np.matrix(np.linspace(-2, 100)).T\n", + "A2 = np.hstack((np.power(xx,i) for i in range(degree+1)))\n", + "\n", + "#plt.imshow(A, interpolation='nearest')\n", + "# Solve the least squares problem\n", + "w_ls,E,rank,sigma = np.linalg.lstsq(A, y)\n", + "\n", + "f = A2*w_ls\n", + "plt.plot(x+BaseYear, y, 'o-')\n", + "plt.plot(xx+BaseYear, f, 'r')\n", + "\n", + "\n", + "plt.xlabel('Years')\n", + "plt.ylabel('Number of Cars')\n", + "\n", + "plt.show()\n", + "\n", + "w_ls,E,rank,sigma = np.linalg.lstsq(A, z)\n", + "\n", + "f = A2*w_ls\n", + "plt.plot(x+BaseYear, z, 'o-')\n", + "plt.plot(xx+BaseYear, f, 'r')\n", + "\n", + "\n", + "plt.xlabel('Years')\n", + "plt.ylabel('Number of Minibuses')\n", + "\n", + "plt.show()\n", + "\n", + "w_ls,E,rank,sigma = np.linalg.lstsq(A, t)\n", + "\n", + "f = A2*w_ls\n", + "plt.plot(x+BaseYear, t, 'o-')\n", + "plt.plot(xx+BaseYear, f, 'r')\n", + "\n", + "\n", + "plt.xlabel('Years')\n", + "plt.ylabel('Number of Buses')\n", + "\n", + "plt.show()\n", + "\n", + "w_ls,E,rank,sigma = np.linalg.lstsq(A, c)\n", + "\n", + "f = A2*w_ls\n", + "plt.plot(x+BaseYear, c, 'o-')\n", + "plt.plot(xx+BaseYear, f, 'r')\n", + "\n", + "\n", + "plt.xlabel('Years')\n", + "plt.ylabel('Number of SmallTrucks')\n", + "\n", + "plt.show()\n", + "\n", + "w_ls,E,rank,sigma = np.linalg.lstsq(A, tr)\n", + "\n", + "f = A2*w_ls\n", + "plt.plot(x+BaseYear, tr, 'o-')\n", + "plt.plot(xx+BaseYear, f, 'r')\n", + "\n", + "\n", + "plt.xlabel('Years')\n", + "plt.ylabel('Number of Trucks')\n", + "\n", + "plt.show()\n", + "\n", + "w_ls,E,rank,sigma = np.linalg.lstsq(A, mot)\n", + "\n", + "f = A2*w_ls\n", + "plt.plot(x+BaseYear, mot, 'o-')\n", + "plt.plot(xx+BaseYear, f, 'r')\n", + "\n", + "\n", + "plt.xlabel('Years')\n", + "plt.ylabel('Number of Motorcycle')\n", + "\n", + "plt.show()\n", + "\n", + "w_ls,E,rank,sigma = np.linalg.lstsq(A, sv)\n", + "\n", + "f = A2*w_ls\n", + "plt.plot(x+BaseYear, sv, 'o-')\n", + "plt.plot(xx+BaseYear, f, 'r')\n", + "\n", + "\n", + "plt.xlabel('Years')\n", + "plt.ylabel('Number of SpecialVehicles')\n", + "\n", + "plt.show()\n", + "\n", + "w_ls,E,rank,sigma = np.linalg.lstsq(A, mac)\n", + "\n", + "f = A2*w_ls\n", + "plt.plot(x+BaseYear, mac, 'o-')\n", + "plt.plot(xx+BaseYear, f, 'r')\n", + "\n", + "\n", + "plt.xlabel('Years')\n", + "plt.ylabel('Number of Machineries')\n", + "\n", + "plt.show()\n", + "\n", + "w_ls,E,rank,sigma = np.linalg.lstsq(A, trac)\n", + "\n", + "f = A2*w_ls\n", + "plt.plot(x+BaseYear, trac, 'o-')\n", + "plt.plot(xx+BaseYear, f, 'r')\n", + "\n", + "\n", + "plt.xlabel('Years')\n", + "plt.ylabel('Number of Tractors')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Mngz/3c4xs2Min9W7/qWVPe/LF3AUcCCwIHLsz8BR4eNm4PLw8QDgaeCA8PkI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\nSUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "BaseYear = 1966\n", + "\n", + "#Auto Regressive\n", + "# Prediction\n", + "N = len(x)\n", + "degree = 2\n", + "\n", + "A = np.hstack((np.power(x,i) for i in range(degree+1)))\n", + "TargetYear = np.arange(2016, 2020,1/12.0)\n", + "x_test = np.matrix(TargetYear - BaseYear).T\n", + "A2 = np.hstack((np.power(x_test, i)) for i in range(degree+1))\n", + "\n", + "w_ls = (A.T*A).I*A.T*x\n", + "\n", + "f = A2*w_ls\n", + "plt.plot(x+BaseYear, x, 'o-')\n", + "plt.plot(x_test+BaseYear, f, 'r')\n", + "\n", + "plt.xlabel('Years')\n", + "plt.ylabel('Number of Cars')\n", + "\n", + "plt.show()\n", + "\n", + "w_ls = (A.T*A).I*A.T*z\n", + "\n", + "f = A2*w_ls\n", + "plt.plot(x+BaseYear, z, 'o-')\n", + "plt.plot(x_test+BaseYear, f, 'r')\n", + "\n", + "plt.xlabel('Years')\n", + "plt.ylabel('Number of Minibuses')\n", + "\n", + "plt.show()\n", + "\n", + "w_ls = (A.T*A).I*A.T*t\n", + "\n", + "f = A2*w_ls\n", + "plt.plot(x+BaseYear, t, 'o-')\n", + "plt.plot(x_test+BaseYear, f, 'r')\n", + "\n", + "\n", + "plt.xlabel('Years')\n", + "plt.ylabel('Number of Buses')\n", + "\n", + "plt.show()\n", + "\n", + "w_ls = (A.T*A).I*A.T*c\n", + "\n", + "f = A2*w_ls\n", + "plt.plot(x+BaseYear, c, 'o-')\n", + "plt.plot(x_test+BaseYear, f, 'r')\n", + "\n", + "plt.xlabel('Years')\n", + "plt.ylabel('Number of SmallTrucks')\n", + "\n", + "plt.show()\n", + "\n", + "w_ls = (A.T*A).I*A.T*tr\n", + "\n", + "f = A2*w_ls\n", + "plt.plot(x+BaseYear, tr, 'o-')\n", + "plt.plot(x_test+BaseYear, f, 'r')\n", + "\n", + "plt.xlabel('Years')\n", + "plt.ylabel('Number of Trucks')\n", + "\n", + "plt.show()\n", + "\n", + "w_ls = (A.T*A).I*A.T*mot\n", + "\n", + "f = A2*w_ls\n", + "plt.plot(x+BaseYear, mot, 'o-')\n", + "plt.plot(x_test+BaseYear, f, 'r')\n", + "\n", + "plt.xlabel('Years')\n", + "plt.ylabel('Number of Motorcycle')\n", + "\n", + "plt.show()\n", + "\n", + "w_ls = (A.T*A).I*A.T*sv\n", + "\n", + "f = A2*w_ls\n", + "plt.plot(x+BaseYear, sv, 'o-')\n", + "plt.plot(x_test+BaseYear, f, 'r')\n", + "\n", + "\n", + "plt.xlabel('Years')\n", + "plt.ylabel('Number of SpecialVehicles')\n", + "\n", + "plt.show()\n", + "\n", + "w_ls = (A.T*A).I*A.T*mac\n", + "\n", + "f = A2*w_ls\n", + "plt.plot(x+BaseYear, mac, 'o-')\n", + "plt.plot(x_test+BaseYear, f, 'r')\n", + "\n", + "\n", + "plt.xlabel('Years')\n", + "plt.ylabel('Number of Machineries')\n", + "\n", + "plt.show()\n", + "\n", + "w_ls = (A.T*A).I*A.T*trac\n", + "\n", + "f = A2*w_ls\n", + "plt.plot(x+BaseYear, trac, 'o-')\n", + "plt.plot(x_test+BaseYear, f, 'r')\n", + "\n", + "plt.xlabel('Years')\n", + "plt.ylabel('Number of Tractors')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +}