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Added homeworks.
This commit is contained in:
Adamo
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1adb3b03a3
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hw1/hw1.pdf
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hw2/answers.pdf
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mpg cylinders displacement horsepower weight acceleration year origin name
|
||||
18.0 8 307.0 130.0 3504. 12.0 70 1 "chevrolet chevelle malibu"
|
||||
15.0 8 350.0 165.0 3693. 11.5 70 1 "buick skylark 320"
|
||||
18.0 8 318.0 150.0 3436. 11.0 70 1 "plymouth satellite"
|
||||
16.0 8 304.0 150.0 3433. 12.0 70 1 "amc rebel sst"
|
||||
17.0 8 302.0 140.0 3449. 10.5 70 1 "ford torino"
|
||||
15.0 8 429.0 198.0 4341. 10.0 70 1 "ford galaxie 500"
|
||||
14.0 8 454.0 220.0 4354. 9.0 70 1 "chevrolet impala"
|
||||
14.0 8 440.0 215.0 4312. 8.5 70 1 "plymouth fury iii"
|
||||
14.0 8 455.0 225.0 4425. 10.0 70 1 "pontiac catalina"
|
||||
15.0 8 390.0 190.0 3850. 8.5 70 1 "amc ambassador dpl"
|
||||
15.0 8 383.0 170.0 3563. 10.0 70 1 "dodge challenger se"
|
||||
14.0 8 340.0 160.0 3609. 8.0 70 1 "plymouth 'cuda 340"
|
||||
15.0 8 400.0 150.0 3761. 9.5 70 1 "chevrolet monte carlo"
|
||||
14.0 8 455.0 225.0 3086. 10.0 70 1 "buick estate wagon (sw)"
|
||||
24.0 4 113.0 95.00 2372. 15.0 70 3 "toyota corona mark ii"
|
||||
22.0 6 198.0 95.00 2833. 15.5 70 1 "plymouth duster"
|
||||
18.0 6 199.0 97.00 2774. 15.5 70 1 "amc hornet"
|
||||
21.0 6 200.0 85.00 2587. 16.0 70 1 "ford maverick"
|
||||
27.0 4 97.00 88.00 2130. 14.5 70 3 "datsun pl510"
|
||||
26.0 4 97.00 46.00 1835. 20.5 70 2 "volkswagen 1131 deluxe sedan"
|
||||
25.0 4 110.0 87.00 2672. 17.5 70 2 "peugeot 504"
|
||||
24.0 4 107.0 90.00 2430. 14.5 70 2 "audi 100 ls"
|
||||
25.0 4 104.0 95.00 2375. 17.5 70 2 "saab 99e"
|
||||
26.0 4 121.0 113.0 2234. 12.5 70 2 "bmw 2002"
|
||||
21.0 6 199.0 90.00 2648. 15.0 70 1 "amc gremlin"
|
||||
10.0 8 360.0 215.0 4615. 14.0 70 1 "ford f250"
|
||||
10.0 8 307.0 200.0 4376. 15.0 70 1 "chevy c20"
|
||||
11.0 8 318.0 210.0 4382. 13.5 70 1 "dodge d200"
|
||||
9.0 8 304.0 193.0 4732. 18.5 70 1 "hi 1200d"
|
||||
27.0 4 97.00 88.00 2130. 14.5 71 3 "datsun pl510"
|
||||
28.0 4 140.0 90.00 2264. 15.5 71 1 "chevrolet vega 2300"
|
||||
25.0 4 113.0 95.00 2228. 14.0 71 3 "toyota corona"
|
||||
25.0 4 98.00 ? 2046. 19.0 71 1 "ford pinto"
|
||||
19.0 6 232.0 100.0 2634. 13.0 71 1 "amc gremlin"
|
||||
16.0 6 225.0 105.0 3439. 15.5 71 1 "plymouth satellite custom"
|
||||
17.0 6 250.0 100.0 3329. 15.5 71 1 "chevrolet chevelle malibu"
|
||||
19.0 6 250.0 88.00 3302. 15.5 71 1 "ford torino 500"
|
||||
18.0 6 232.0 100.0 3288. 15.5 71 1 "amc matador"
|
||||
14.0 8 350.0 165.0 4209. 12.0 71 1 "chevrolet impala"
|
||||
14.0 8 400.0 175.0 4464. 11.5 71 1 "pontiac catalina brougham"
|
||||
14.0 8 351.0 153.0 4154. 13.5 71 1 "ford galaxie 500"
|
||||
14.0 8 318.0 150.0 4096. 13.0 71 1 "plymouth fury iii"
|
||||
12.0 8 383.0 180.0 4955. 11.5 71 1 "dodge monaco (sw)"
|
||||
13.0 8 400.0 170.0 4746. 12.0 71 1 "ford country squire (sw)"
|
||||
13.0 8 400.0 175.0 5140. 12.0 71 1 "pontiac safari (sw)"
|
||||
18.0 6 258.0 110.0 2962. 13.5 71 1 "amc hornet sportabout (sw)"
|
||||
22.0 4 140.0 72.00 2408. 19.0 71 1 "chevrolet vega (sw)"
|
||||
19.0 6 250.0 100.0 3282. 15.0 71 1 "pontiac firebird"
|
||||
18.0 6 250.0 88.00 3139. 14.5 71 1 "ford mustang"
|
||||
23.0 4 122.0 86.00 2220. 14.0 71 1 "mercury capri 2000"
|
||||
28.0 4 116.0 90.00 2123. 14.0 71 2 "opel 1900"
|
||||
30.0 4 79.00 70.00 2074. 19.5 71 2 "peugeot 304"
|
||||
30.0 4 88.00 76.00 2065. 14.5 71 2 "fiat 124b"
|
||||
31.0 4 71.00 65.00 1773. 19.0 71 3 "toyota corolla 1200"
|
||||
35.0 4 72.00 69.00 1613. 18.0 71 3 "datsun 1200"
|
||||
27.0 4 97.00 60.00 1834. 19.0 71 2 "volkswagen model 111"
|
||||
26.0 4 91.00 70.00 1955. 20.5 71 1 "plymouth cricket"
|
||||
24.0 4 113.0 95.00 2278. 15.5 72 3 "toyota corona hardtop"
|
||||
25.0 4 97.50 80.00 2126. 17.0 72 1 "dodge colt hardtop"
|
||||
23.0 4 97.00 54.00 2254. 23.5 72 2 "volkswagen type 3"
|
||||
20.0 4 140.0 90.00 2408. 19.5 72 1 "chevrolet vega"
|
||||
21.0 4 122.0 86.00 2226. 16.5 72 1 "ford pinto runabout"
|
||||
13.0 8 350.0 165.0 4274. 12.0 72 1 "chevrolet impala"
|
||||
14.0 8 400.0 175.0 4385. 12.0 72 1 "pontiac catalina"
|
||||
15.0 8 318.0 150.0 4135. 13.5 72 1 "plymouth fury iii"
|
||||
14.0 8 351.0 153.0 4129. 13.0 72 1 "ford galaxie 500"
|
||||
17.0 8 304.0 150.0 3672. 11.5 72 1 "amc ambassador sst"
|
||||
11.0 8 429.0 208.0 4633. 11.0 72 1 "mercury marquis"
|
||||
13.0 8 350.0 155.0 4502. 13.5 72 1 "buick lesabre custom"
|
||||
12.0 8 350.0 160.0 4456. 13.5 72 1 "oldsmobile delta 88 royale"
|
||||
13.0 8 400.0 190.0 4422. 12.5 72 1 "chrysler newport royal"
|
||||
19.0 3 70.00 97.00 2330. 13.5 72 3 "mazda rx2 coupe"
|
||||
15.0 8 304.0 150.0 3892. 12.5 72 1 "amc matador (sw)"
|
||||
13.0 8 307.0 130.0 4098. 14.0 72 1 "chevrolet chevelle concours (sw)"
|
||||
13.0 8 302.0 140.0 4294. 16.0 72 1 "ford gran torino (sw)"
|
||||
14.0 8 318.0 150.0 4077. 14.0 72 1 "plymouth satellite custom (sw)"
|
||||
18.0 4 121.0 112.0 2933. 14.5 72 2 "volvo 145e (sw)"
|
||||
22.0 4 121.0 76.00 2511. 18.0 72 2 "volkswagen 411 (sw)"
|
||||
21.0 4 120.0 87.00 2979. 19.5 72 2 "peugeot 504 (sw)"
|
||||
26.0 4 96.00 69.00 2189. 18.0 72 2 "renault 12 (sw)"
|
||||
22.0 4 122.0 86.00 2395. 16.0 72 1 "ford pinto (sw)"
|
||||
28.0 4 97.00 92.00 2288. 17.0 72 3 "datsun 510 (sw)"
|
||||
23.0 4 120.0 97.00 2506. 14.5 72 3 "toyouta corona mark ii (sw)"
|
||||
28.0 4 98.00 80.00 2164. 15.0 72 1 "dodge colt (sw)"
|
||||
27.0 4 97.00 88.00 2100. 16.5 72 3 "toyota corolla 1600 (sw)"
|
||||
13.0 8 350.0 175.0 4100. 13.0 73 1 "buick century 350"
|
||||
14.0 8 304.0 150.0 3672. 11.5 73 1 "amc matador"
|
||||
13.0 8 350.0 145.0 3988. 13.0 73 1 "chevrolet malibu"
|
||||
14.0 8 302.0 137.0 4042. 14.5 73 1 "ford gran torino"
|
||||
15.0 8 318.0 150.0 3777. 12.5 73 1 "dodge coronet custom"
|
||||
12.0 8 429.0 198.0 4952. 11.5 73 1 "mercury marquis brougham"
|
||||
13.0 8 400.0 150.0 4464. 12.0 73 1 "chevrolet caprice classic"
|
||||
13.0 8 351.0 158.0 4363. 13.0 73 1 "ford ltd"
|
||||
14.0 8 318.0 150.0 4237. 14.5 73 1 "plymouth fury gran sedan"
|
||||
13.0 8 440.0 215.0 4735. 11.0 73 1 "chrysler new yorker brougham"
|
||||
12.0 8 455.0 225.0 4951. 11.0 73 1 "buick electra 225 custom"
|
||||
13.0 8 360.0 175.0 3821. 11.0 73 1 "amc ambassador brougham"
|
||||
18.0 6 225.0 105.0 3121. 16.5 73 1 "plymouth valiant"
|
||||
16.0 6 250.0 100.0 3278. 18.0 73 1 "chevrolet nova custom"
|
||||
18.0 6 232.0 100.0 2945. 16.0 73 1 "amc hornet"
|
||||
18.0 6 250.0 88.00 3021. 16.5 73 1 "ford maverick"
|
||||
23.0 6 198.0 95.00 2904. 16.0 73 1 "plymouth duster"
|
||||
26.0 4 97.00 46.00 1950. 21.0 73 2 "volkswagen super beetle"
|
||||
11.0 8 400.0 150.0 4997. 14.0 73 1 "chevrolet impala"
|
||||
12.0 8 400.0 167.0 4906. 12.5 73 1 "ford country"
|
||||
13.0 8 360.0 170.0 4654. 13.0 73 1 "plymouth custom suburb"
|
||||
12.0 8 350.0 180.0 4499. 12.5 73 1 "oldsmobile vista cruiser"
|
||||
18.0 6 232.0 100.0 2789. 15.0 73 1 "amc gremlin"
|
||||
20.0 4 97.00 88.00 2279. 19.0 73 3 "toyota carina"
|
||||
21.0 4 140.0 72.00 2401. 19.5 73 1 "chevrolet vega"
|
||||
22.0 4 108.0 94.00 2379. 16.5 73 3 "datsun 610"
|
||||
18.0 3 70.00 90.00 2124. 13.5 73 3 "maxda rx3"
|
||||
19.0 4 122.0 85.00 2310. 18.5 73 1 "ford pinto"
|
||||
21.0 6 155.0 107.0 2472. 14.0 73 1 "mercury capri v6"
|
||||
26.0 4 98.00 90.00 2265. 15.5 73 2 "fiat 124 sport coupe"
|
||||
15.0 8 350.0 145.0 4082. 13.0 73 1 "chevrolet monte carlo s"
|
||||
16.0 8 400.0 230.0 4278. 9.50 73 1 "pontiac grand prix"
|
||||
29.0 4 68.00 49.00 1867. 19.5 73 2 "fiat 128"
|
||||
24.0 4 116.0 75.00 2158. 15.5 73 2 "opel manta"
|
||||
20.0 4 114.0 91.00 2582. 14.0 73 2 "audi 100ls"
|
||||
19.0 4 121.0 112.0 2868. 15.5 73 2 "volvo 144ea"
|
||||
15.0 8 318.0 150.0 3399. 11.0 73 1 "dodge dart custom"
|
||||
24.0 4 121.0 110.0 2660. 14.0 73 2 "saab 99le"
|
||||
20.0 6 156.0 122.0 2807. 13.5 73 3 "toyota mark ii"
|
||||
11.0 8 350.0 180.0 3664. 11.0 73 1 "oldsmobile omega"
|
||||
20.0 6 198.0 95.00 3102. 16.5 74 1 "plymouth duster"
|
||||
21.0 6 200.0 ? 2875. 17.0 74 1 "ford maverick"
|
||||
19.0 6 232.0 100.0 2901. 16.0 74 1 "amc hornet"
|
||||
15.0 6 250.0 100.0 3336. 17.0 74 1 "chevrolet nova"
|
||||
31.0 4 79.00 67.00 1950. 19.0 74 3 "datsun b210"
|
||||
26.0 4 122.0 80.00 2451. 16.5 74 1 "ford pinto"
|
||||
32.0 4 71.00 65.00 1836. 21.0 74 3 "toyota corolla 1200"
|
||||
25.0 4 140.0 75.00 2542. 17.0 74 1 "chevrolet vega"
|
||||
16.0 6 250.0 100.0 3781. 17.0 74 1 "chevrolet chevelle malibu classic"
|
||||
16.0 6 258.0 110.0 3632. 18.0 74 1 "amc matador"
|
||||
18.0 6 225.0 105.0 3613. 16.5 74 1 "plymouth satellite sebring"
|
||||
16.0 8 302.0 140.0 4141. 14.0 74 1 "ford gran torino"
|
||||
13.0 8 350.0 150.0 4699. 14.5 74 1 "buick century luxus (sw)"
|
||||
14.0 8 318.0 150.0 4457. 13.5 74 1 "dodge coronet custom (sw)"
|
||||
14.0 8 302.0 140.0 4638. 16.0 74 1 "ford gran torino (sw)"
|
||||
14.0 8 304.0 150.0 4257. 15.5 74 1 "amc matador (sw)"
|
||||
29.0 4 98.00 83.00 2219. 16.5 74 2 "audi fox"
|
||||
26.0 4 79.00 67.00 1963. 15.5 74 2 "volkswagen dasher"
|
||||
26.0 4 97.00 78.00 2300. 14.5 74 2 "opel manta"
|
||||
31.0 4 76.00 52.00 1649. 16.5 74 3 "toyota corona"
|
||||
32.0 4 83.00 61.00 2003. 19.0 74 3 "datsun 710"
|
||||
28.0 4 90.00 75.00 2125. 14.5 74 1 "dodge colt"
|
||||
24.0 4 90.00 75.00 2108. 15.5 74 2 "fiat 128"
|
||||
26.0 4 116.0 75.00 2246. 14.0 74 2 "fiat 124 tc"
|
||||
24.0 4 120.0 97.00 2489. 15.0 74 3 "honda civic"
|
||||
26.0 4 108.0 93.00 2391. 15.5 74 3 "subaru"
|
||||
31.0 4 79.00 67.00 2000. 16.0 74 2 "fiat x1.9"
|
||||
19.0 6 225.0 95.00 3264. 16.0 75 1 "plymouth valiant custom"
|
||||
18.0 6 250.0 105.0 3459. 16.0 75 1 "chevrolet nova"
|
||||
15.0 6 250.0 72.00 3432. 21.0 75 1 "mercury monarch"
|
||||
15.0 6 250.0 72.00 3158. 19.5 75 1 "ford maverick"
|
||||
16.0 8 400.0 170.0 4668. 11.5 75 1 "pontiac catalina"
|
||||
15.0 8 350.0 145.0 4440. 14.0 75 1 "chevrolet bel air"
|
||||
16.0 8 318.0 150.0 4498. 14.5 75 1 "plymouth grand fury"
|
||||
14.0 8 351.0 148.0 4657. 13.5 75 1 "ford ltd"
|
||||
17.0 6 231.0 110.0 3907. 21.0 75 1 "buick century"
|
||||
16.0 6 250.0 105.0 3897. 18.5 75 1 "chevroelt chevelle malibu"
|
||||
15.0 6 258.0 110.0 3730. 19.0 75 1 "amc matador"
|
||||
18.0 6 225.0 95.00 3785. 19.0 75 1 "plymouth fury"
|
||||
21.0 6 231.0 110.0 3039. 15.0 75 1 "buick skyhawk"
|
||||
20.0 8 262.0 110.0 3221. 13.5 75 1 "chevrolet monza 2+2"
|
||||
13.0 8 302.0 129.0 3169. 12.0 75 1 "ford mustang ii"
|
||||
29.0 4 97.00 75.00 2171. 16.0 75 3 "toyota corolla"
|
||||
23.0 4 140.0 83.00 2639. 17.0 75 1 "ford pinto"
|
||||
20.0 6 232.0 100.0 2914. 16.0 75 1 "amc gremlin"
|
||||
23.0 4 140.0 78.00 2592. 18.5 75 1 "pontiac astro"
|
||||
24.0 4 134.0 96.00 2702. 13.5 75 3 "toyota corona"
|
||||
25.0 4 90.00 71.00 2223. 16.5 75 2 "volkswagen dasher"
|
||||
24.0 4 119.0 97.00 2545. 17.0 75 3 "datsun 710"
|
||||
18.0 6 171.0 97.00 2984. 14.5 75 1 "ford pinto"
|
||||
29.0 4 90.00 70.00 1937. 14.0 75 2 "volkswagen rabbit"
|
||||
19.0 6 232.0 90.00 3211. 17.0 75 1 "amc pacer"
|
||||
23.0 4 115.0 95.00 2694. 15.0 75 2 "audi 100ls"
|
||||
23.0 4 120.0 88.00 2957. 17.0 75 2 "peugeot 504"
|
||||
22.0 4 121.0 98.00 2945. 14.5 75 2 "volvo 244dl"
|
||||
25.0 4 121.0 115.0 2671. 13.5 75 2 "saab 99le"
|
||||
33.0 4 91.00 53.00 1795. 17.5 75 3 "honda civic cvcc"
|
||||
28.0 4 107.0 86.00 2464. 15.5 76 2 "fiat 131"
|
||||
25.0 4 116.0 81.00 2220. 16.9 76 2 "opel 1900"
|
||||
25.0 4 140.0 92.00 2572. 14.9 76 1 "capri ii"
|
||||
26.0 4 98.00 79.00 2255. 17.7 76 1 "dodge colt"
|
||||
27.0 4 101.0 83.00 2202. 15.3 76 2 "renault 12tl"
|
||||
17.5 8 305.0 140.0 4215. 13.0 76 1 "chevrolet chevelle malibu classic"
|
||||
16.0 8 318.0 150.0 4190. 13.0 76 1 "dodge coronet brougham"
|
||||
15.5 8 304.0 120.0 3962. 13.9 76 1 "amc matador"
|
||||
14.5 8 351.0 152.0 4215. 12.8 76 1 "ford gran torino"
|
||||
22.0 6 225.0 100.0 3233. 15.4 76 1 "plymouth valiant"
|
||||
22.0 6 250.0 105.0 3353. 14.5 76 1 "chevrolet nova"
|
||||
24.0 6 200.0 81.00 3012. 17.6 76 1 "ford maverick"
|
||||
22.5 6 232.0 90.00 3085. 17.6 76 1 "amc hornet"
|
||||
29.0 4 85.00 52.00 2035. 22.2 76 1 "chevrolet chevette"
|
||||
24.5 4 98.00 60.00 2164. 22.1 76 1 "chevrolet woody"
|
||||
29.0 4 90.00 70.00 1937. 14.2 76 2 "vw rabbit"
|
||||
33.0 4 91.00 53.00 1795. 17.4 76 3 "honda civic"
|
||||
20.0 6 225.0 100.0 3651. 17.7 76 1 "dodge aspen se"
|
||||
18.0 6 250.0 78.00 3574. 21.0 76 1 "ford granada ghia"
|
||||
18.5 6 250.0 110.0 3645. 16.2 76 1 "pontiac ventura sj"
|
||||
17.5 6 258.0 95.00 3193. 17.8 76 1 "amc pacer d/l"
|
||||
29.5 4 97.00 71.00 1825. 12.2 76 2 "volkswagen rabbit"
|
||||
32.0 4 85.00 70.00 1990. 17.0 76 3 "datsun b-210"
|
||||
28.0 4 97.00 75.00 2155. 16.4 76 3 "toyota corolla"
|
||||
26.5 4 140.0 72.00 2565. 13.6 76 1 "ford pinto"
|
||||
20.0 4 130.0 102.0 3150. 15.7 76 2 "volvo 245"
|
||||
13.0 8 318.0 150.0 3940. 13.2 76 1 "plymouth volare premier v8"
|
||||
19.0 4 120.0 88.00 3270. 21.9 76 2 "peugeot 504"
|
||||
19.0 6 156.0 108.0 2930. 15.5 76 3 "toyota mark ii"
|
||||
16.5 6 168.0 120.0 3820. 16.7 76 2 "mercedes-benz 280s"
|
||||
16.5 8 350.0 180.0 4380. 12.1 76 1 "cadillac seville"
|
||||
13.0 8 350.0 145.0 4055. 12.0 76 1 "chevy c10"
|
||||
13.0 8 302.0 130.0 3870. 15.0 76 1 "ford f108"
|
||||
13.0 8 318.0 150.0 3755. 14.0 76 1 "dodge d100"
|
||||
31.5 4 98.00 68.00 2045. 18.5 77 3 "honda accord cvcc"
|
||||
30.0 4 111.0 80.00 2155. 14.8 77 1 "buick opel isuzu deluxe"
|
||||
36.0 4 79.00 58.00 1825. 18.6 77 2 "renault 5 gtl"
|
||||
25.5 4 122.0 96.00 2300. 15.5 77 1 "plymouth arrow gs"
|
||||
33.5 4 85.00 70.00 1945. 16.8 77 3 "datsun f-10 hatchback"
|
||||
17.5 8 305.0 145.0 3880. 12.5 77 1 "chevrolet caprice classic"
|
||||
17.0 8 260.0 110.0 4060. 19.0 77 1 "oldsmobile cutlass supreme"
|
||||
15.5 8 318.0 145.0 4140. 13.7 77 1 "dodge monaco brougham"
|
||||
15.0 8 302.0 130.0 4295. 14.9 77 1 "mercury cougar brougham"
|
||||
17.5 6 250.0 110.0 3520. 16.4 77 1 "chevrolet concours"
|
||||
20.5 6 231.0 105.0 3425. 16.9 77 1 "buick skylark"
|
||||
19.0 6 225.0 100.0 3630. 17.7 77 1 "plymouth volare custom"
|
||||
18.5 6 250.0 98.00 3525. 19.0 77 1 "ford granada"
|
||||
16.0 8 400.0 180.0 4220. 11.1 77 1 "pontiac grand prix lj"
|
||||
15.5 8 350.0 170.0 4165. 11.4 77 1 "chevrolet monte carlo landau"
|
||||
15.5 8 400.0 190.0 4325. 12.2 77 1 "chrysler cordoba"
|
||||
16.0 8 351.0 149.0 4335. 14.5 77 1 "ford thunderbird"
|
||||
29.0 4 97.00 78.00 1940. 14.5 77 2 "volkswagen rabbit custom"
|
||||
24.5 4 151.0 88.00 2740. 16.0 77 1 "pontiac sunbird coupe"
|
||||
26.0 4 97.00 75.00 2265. 18.2 77 3 "toyota corolla liftback"
|
||||
25.5 4 140.0 89.00 2755. 15.8 77 1 "ford mustang ii 2+2"
|
||||
30.5 4 98.00 63.00 2051. 17.0 77 1 "chevrolet chevette"
|
||||
33.5 4 98.00 83.00 2075. 15.9 77 1 "dodge colt m/m"
|
||||
30.0 4 97.00 67.00 1985. 16.4 77 3 "subaru dl"
|
||||
30.5 4 97.00 78.00 2190. 14.1 77 2 "volkswagen dasher"
|
||||
22.0 6 146.0 97.00 2815. 14.5 77 3 "datsun 810"
|
||||
21.5 4 121.0 110.0 2600. 12.8 77 2 "bmw 320i"
|
||||
21.5 3 80.00 110.0 2720. 13.5 77 3 "mazda rx-4"
|
||||
43.1 4 90.00 48.00 1985. 21.5 78 2 "volkswagen rabbit custom diesel"
|
||||
36.1 4 98.00 66.00 1800. 14.4 78 1 "ford fiesta"
|
||||
32.8 4 78.00 52.00 1985. 19.4 78 3 "mazda glc deluxe"
|
||||
39.4 4 85.00 70.00 2070. 18.6 78 3 "datsun b210 gx"
|
||||
36.1 4 91.00 60.00 1800. 16.4 78 3 "honda civic cvcc"
|
||||
19.9 8 260.0 110.0 3365. 15.5 78 1 "oldsmobile cutlass salon brougham"
|
||||
19.4 8 318.0 140.0 3735. 13.2 78 1 "dodge diplomat"
|
||||
20.2 8 302.0 139.0 3570. 12.8 78 1 "mercury monarch ghia"
|
||||
19.2 6 231.0 105.0 3535. 19.2 78 1 "pontiac phoenix lj"
|
||||
20.5 6 200.0 95.00 3155. 18.2 78 1 "chevrolet malibu"
|
||||
20.2 6 200.0 85.00 2965. 15.8 78 1 "ford fairmont (auto)"
|
||||
25.1 4 140.0 88.00 2720. 15.4 78 1 "ford fairmont (man)"
|
||||
20.5 6 225.0 100.0 3430. 17.2 78 1 "plymouth volare"
|
||||
19.4 6 232.0 90.00 3210. 17.2 78 1 "amc concord"
|
||||
20.6 6 231.0 105.0 3380. 15.8 78 1 "buick century special"
|
||||
20.8 6 200.0 85.00 3070. 16.7 78 1 "mercury zephyr"
|
||||
18.6 6 225.0 110.0 3620. 18.7 78 1 "dodge aspen"
|
||||
18.1 6 258.0 120.0 3410. 15.1 78 1 "amc concord d/l"
|
||||
19.2 8 305.0 145.0 3425. 13.2 78 1 "chevrolet monte carlo landau"
|
||||
17.7 6 231.0 165.0 3445. 13.4 78 1 "buick regal sport coupe (turbo)"
|
||||
18.1 8 302.0 139.0 3205. 11.2 78 1 "ford futura"
|
||||
17.5 8 318.0 140.0 4080. 13.7 78 1 "dodge magnum xe"
|
||||
30.0 4 98.00 68.00 2155. 16.5 78 1 "chevrolet chevette"
|
||||
27.5 4 134.0 95.00 2560. 14.2 78 3 "toyota corona"
|
||||
27.2 4 119.0 97.00 2300. 14.7 78 3 "datsun 510"
|
||||
30.9 4 105.0 75.00 2230. 14.5 78 1 "dodge omni"
|
||||
21.1 4 134.0 95.00 2515. 14.8 78 3 "toyota celica gt liftback"
|
||||
23.2 4 156.0 105.0 2745. 16.7 78 1 "plymouth sapporo"
|
||||
23.8 4 151.0 85.00 2855. 17.6 78 1 "oldsmobile starfire sx"
|
||||
23.9 4 119.0 97.00 2405. 14.9 78 3 "datsun 200-sx"
|
||||
20.3 5 131.0 103.0 2830. 15.9 78 2 "audi 5000"
|
||||
17.0 6 163.0 125.0 3140. 13.6 78 2 "volvo 264gl"
|
||||
21.6 4 121.0 115.0 2795. 15.7 78 2 "saab 99gle"
|
||||
16.2 6 163.0 133.0 3410. 15.8 78 2 "peugeot 604sl"
|
||||
31.5 4 89.00 71.00 1990. 14.9 78 2 "volkswagen scirocco"
|
||||
29.5 4 98.00 68.00 2135. 16.6 78 3 "honda accord lx"
|
||||
21.5 6 231.0 115.0 3245. 15.4 79 1 "pontiac lemans v6"
|
||||
19.8 6 200.0 85.00 2990. 18.2 79 1 "mercury zephyr 6"
|
||||
22.3 4 140.0 88.00 2890. 17.3 79 1 "ford fairmont 4"
|
||||
20.2 6 232.0 90.00 3265. 18.2 79 1 "amc concord dl 6"
|
||||
20.6 6 225.0 110.0 3360. 16.6 79 1 "dodge aspen 6"
|
||||
17.0 8 305.0 130.0 3840. 15.4 79 1 "chevrolet caprice classic"
|
||||
17.6 8 302.0 129.0 3725. 13.4 79 1 "ford ltd landau"
|
||||
16.5 8 351.0 138.0 3955. 13.2 79 1 "mercury grand marquis"
|
||||
18.2 8 318.0 135.0 3830. 15.2 79 1 "dodge st. regis"
|
||||
16.9 8 350.0 155.0 4360. 14.9 79 1 "buick estate wagon (sw)"
|
||||
15.5 8 351.0 142.0 4054. 14.3 79 1 "ford country squire (sw)"
|
||||
19.2 8 267.0 125.0 3605. 15.0 79 1 "chevrolet malibu classic (sw)"
|
||||
18.5 8 360.0 150.0 3940. 13.0 79 1 "chrysler lebaron town @ country (sw)"
|
||||
31.9 4 89.00 71.00 1925. 14.0 79 2 "vw rabbit custom"
|
||||
34.1 4 86.00 65.00 1975. 15.2 79 3 "maxda glc deluxe"
|
||||
35.7 4 98.00 80.00 1915. 14.4 79 1 "dodge colt hatchback custom"
|
||||
27.4 4 121.0 80.00 2670. 15.0 79 1 "amc spirit dl"
|
||||
25.4 5 183.0 77.00 3530. 20.1 79 2 "mercedes benz 300d"
|
||||
23.0 8 350.0 125.0 3900. 17.4 79 1 "cadillac eldorado"
|
||||
27.2 4 141.0 71.00 3190. 24.8 79 2 "peugeot 504"
|
||||
23.9 8 260.0 90.00 3420. 22.2 79 1 "oldsmobile cutlass salon brougham"
|
||||
34.2 4 105.0 70.00 2200. 13.2 79 1 "plymouth horizon"
|
||||
34.5 4 105.0 70.00 2150. 14.9 79 1 "plymouth horizon tc3"
|
||||
31.8 4 85.00 65.00 2020. 19.2 79 3 "datsun 210"
|
||||
37.3 4 91.00 69.00 2130. 14.7 79 2 "fiat strada custom"
|
||||
28.4 4 151.0 90.00 2670. 16.0 79 1 "buick skylark limited"
|
||||
28.8 6 173.0 115.0 2595. 11.3 79 1 "chevrolet citation"
|
||||
26.8 6 173.0 115.0 2700. 12.9 79 1 "oldsmobile omega brougham"
|
||||
33.5 4 151.0 90.00 2556. 13.2 79 1 "pontiac phoenix"
|
||||
41.5 4 98.00 76.00 2144. 14.7 80 2 "vw rabbit"
|
||||
38.1 4 89.00 60.00 1968. 18.8 80 3 "toyota corolla tercel"
|
||||
32.1 4 98.00 70.00 2120. 15.5 80 1 "chevrolet chevette"
|
||||
37.2 4 86.00 65.00 2019. 16.4 80 3 "datsun 310"
|
||||
28.0 4 151.0 90.00 2678. 16.5 80 1 "chevrolet citation"
|
||||
26.4 4 140.0 88.00 2870. 18.1 80 1 "ford fairmont"
|
||||
24.3 4 151.0 90.00 3003. 20.1 80 1 "amc concord"
|
||||
19.1 6 225.0 90.00 3381. 18.7 80 1 "dodge aspen"
|
||||
34.3 4 97.00 78.00 2188. 15.8 80 2 "audi 4000"
|
||||
29.8 4 134.0 90.00 2711. 15.5 80 3 "toyota corona liftback"
|
||||
31.3 4 120.0 75.00 2542. 17.5 80 3 "mazda 626"
|
||||
37.0 4 119.0 92.00 2434. 15.0 80 3 "datsun 510 hatchback"
|
||||
32.2 4 108.0 75.00 2265. 15.2 80 3 "toyota corolla"
|
||||
46.6 4 86.00 65.00 2110. 17.9 80 3 "mazda glc"
|
||||
27.9 4 156.0 105.0 2800. 14.4 80 1 "dodge colt"
|
||||
40.8 4 85.00 65.00 2110. 19.2 80 3 "datsun 210"
|
||||
44.3 4 90.00 48.00 2085. 21.7 80 2 "vw rabbit c (diesel)"
|
||||
43.4 4 90.00 48.00 2335. 23.7 80 2 "vw dasher (diesel)"
|
||||
36.4 5 121.0 67.00 2950. 19.9 80 2 "audi 5000s (diesel)"
|
||||
30.0 4 146.0 67.00 3250. 21.8 80 2 "mercedes-benz 240d"
|
||||
44.6 4 91.00 67.00 1850. 13.8 80 3 "honda civic 1500 gl"
|
||||
40.9 4 85.00 ? 1835. 17.3 80 2 "renault lecar deluxe"
|
||||
33.8 4 97.00 67.00 2145. 18.0 80 3 "subaru dl"
|
||||
29.8 4 89.00 62.00 1845. 15.3 80 2 "vokswagen rabbit"
|
||||
32.7 6 168.0 132.0 2910. 11.4 80 3 "datsun 280-zx"
|
||||
23.7 3 70.00 100.0 2420. 12.5 80 3 "mazda rx-7 gs"
|
||||
35.0 4 122.0 88.00 2500. 15.1 80 2 "triumph tr7 coupe"
|
||||
23.6 4 140.0 ? 2905. 14.3 80 1 "ford mustang cobra"
|
||||
32.4 4 107.0 72.00 2290. 17.0 80 3 "honda accord"
|
||||
27.2 4 135.0 84.00 2490. 15.7 81 1 "plymouth reliant"
|
||||
26.6 4 151.0 84.00 2635. 16.4 81 1 "buick skylark"
|
||||
25.8 4 156.0 92.00 2620. 14.4 81 1 "dodge aries wagon (sw)"
|
||||
23.5 6 173.0 110.0 2725. 12.6 81 1 "chevrolet citation"
|
||||
30.0 4 135.0 84.00 2385. 12.9 81 1 "plymouth reliant"
|
||||
39.1 4 79.00 58.00 1755. 16.9 81 3 "toyota starlet"
|
||||
39.0 4 86.00 64.00 1875. 16.4 81 1 "plymouth champ"
|
||||
35.1 4 81.00 60.00 1760. 16.1 81 3 "honda civic 1300"
|
||||
32.3 4 97.00 67.00 2065. 17.8 81 3 "subaru"
|
||||
37.0 4 85.00 65.00 1975. 19.4 81 3 "datsun 210 mpg"
|
||||
37.7 4 89.00 62.00 2050. 17.3 81 3 "toyota tercel"
|
||||
34.1 4 91.00 68.00 1985. 16.0 81 3 "mazda glc 4"
|
||||
34.7 4 105.0 63.00 2215. 14.9 81 1 "plymouth horizon 4"
|
||||
34.4 4 98.00 65.00 2045. 16.2 81 1 "ford escort 4w"
|
||||
29.9 4 98.00 65.00 2380. 20.7 81 1 "ford escort 2h"
|
||||
33.0 4 105.0 74.00 2190. 14.2 81 2 "volkswagen jetta"
|
||||
34.5 4 100.0 ? 2320. 15.8 81 2 "renault 18i"
|
||||
33.7 4 107.0 75.00 2210. 14.4 81 3 "honda prelude"
|
||||
32.4 4 108.0 75.00 2350. 16.8 81 3 "toyota corolla"
|
||||
32.9 4 119.0 100.0 2615. 14.8 81 3 "datsun 200sx"
|
||||
31.6 4 120.0 74.00 2635. 18.3 81 3 "mazda 626"
|
||||
28.1 4 141.0 80.00 3230. 20.4 81 2 "peugeot 505s turbo diesel"
|
||||
30.7 6 145.0 76.00 3160. 19.6 81 2 "volvo diesel"
|
||||
25.4 6 168.0 116.0 2900. 12.6 81 3 "toyota cressida"
|
||||
24.2 6 146.0 120.0 2930. 13.8 81 3 "datsun 810 maxima"
|
||||
22.4 6 231.0 110.0 3415. 15.8 81 1 "buick century"
|
||||
26.6 8 350.0 105.0 3725. 19.0 81 1 "oldsmobile cutlass ls"
|
||||
20.2 6 200.0 88.00 3060. 17.1 81 1 "ford granada gl"
|
||||
17.6 6 225.0 85.00 3465. 16.6 81 1 "chrysler lebaron salon"
|
||||
28.0 4 112.0 88.00 2605. 19.6 82 1 "chevrolet cavalier"
|
||||
27.0 4 112.0 88.00 2640. 18.6 82 1 "chevrolet cavalier wagon"
|
||||
34.0 4 112.0 88.00 2395. 18.0 82 1 "chevrolet cavalier 2-door"
|
||||
31.0 4 112.0 85.00 2575. 16.2 82 1 "pontiac j2000 se hatchback"
|
||||
29.0 4 135.0 84.00 2525. 16.0 82 1 "dodge aries se"
|
||||
27.0 4 151.0 90.00 2735. 18.0 82 1 "pontiac phoenix"
|
||||
24.0 4 140.0 92.00 2865. 16.4 82 1 "ford fairmont futura"
|
||||
36.0 4 105.0 74.00 1980. 15.3 82 2 "volkswagen rabbit l"
|
||||
37.0 4 91.00 68.00 2025. 18.2 82 3 "mazda glc custom l"
|
||||
31.0 4 91.00 68.00 1970. 17.6 82 3 "mazda glc custom"
|
||||
38.0 4 105.0 63.00 2125. 14.7 82 1 "plymouth horizon miser"
|
||||
36.0 4 98.00 70.00 2125. 17.3 82 1 "mercury lynx l"
|
||||
36.0 4 120.0 88.00 2160. 14.5 82 3 "nissan stanza xe"
|
||||
36.0 4 107.0 75.00 2205. 14.5 82 3 "honda accord"
|
||||
34.0 4 108.0 70.00 2245 16.9 82 3 "toyota corolla"
|
||||
38.0 4 91.00 67.00 1965. 15.0 82 3 "honda civic"
|
||||
32.0 4 91.00 67.00 1965. 15.7 82 3 "honda civic (auto)"
|
||||
38.0 4 91.00 67.00 1995. 16.2 82 3 "datsun 310 gx"
|
||||
25.0 6 181.0 110.0 2945. 16.4 82 1 "buick century limited"
|
||||
38.0 6 262.0 85.00 3015. 17.0 82 1 "oldsmobile cutlass ciera (diesel)"
|
||||
26.0 4 156.0 92.00 2585. 14.5 82 1 "chrysler lebaron medallion"
|
||||
22.0 6 232.0 112.0 2835 14.7 82 1 "ford granada l"
|
||||
32.0 4 144.0 96.00 2665. 13.9 82 3 "toyota celica gt"
|
||||
36.0 4 135.0 84.00 2370. 13.0 82 1 "dodge charger 2.2"
|
||||
27.0 4 151.0 90.00 2950. 17.3 82 1 "chevrolet camaro"
|
||||
27.0 4 140.0 86.00 2790. 15.6 82 1 "ford mustang gl"
|
||||
44.0 4 97.00 52.00 2130. 24.6 82 2 "vw pickup"
|
||||
32.0 4 135.0 84.00 2295. 11.6 82 1 "dodge rampage"
|
||||
28.0 4 120.0 79.00 2625. 18.6 82 1 "ford ranger"
|
||||
31.0 4 119.0 82.00 2720. 19.4 82 1 "chevy s-10"
|
||||
BIN
hw2/fit_quality.pdf
Executable file
BIN
hw2/fit_quality.pdf
Executable file
Binary file not shown.
BIN
hw2/mpg_horsepower_regression.pdf
Executable file
BIN
hw2/mpg_horsepower_regression.pdf
Executable file
Binary file not shown.
462
hw3/Rhistory
Normal file
462
hw3/Rhistory
Normal file
@ -0,0 +1,462 @@
|
||||
auto = read.table("auto.data",header=T,na.strings="?")
|
||||
length(x=auto$mpg)
|
||||
glm
|
||||
glm.pred
|
||||
help(rep)
|
||||
glm.pred=rep(FALSE,397)
|
||||
glm.pred
|
||||
medium(auto$mpg)
|
||||
median(auto$mpg)
|
||||
glm.pred[auto$mpg>median(auto$mpg)]=T
|
||||
glm.pred
|
||||
contour(auto)
|
||||
contour(glm.pred ~ auto$mpg)
|
||||
contour(glm.pred,auto$mpg)
|
||||
help(contour)
|
||||
contour(auto$mpg,auto$horsepower,glm.pred)
|
||||
glm.pred
|
||||
length(glm.pred)
|
||||
table(glm.pred,auto$mpg)
|
||||
table(glm.pred,auto$mpg,auto$horsepower)
|
||||
glm.pred=rep(0,397)
|
||||
glm.pred[auto$mpg>median(auto$mpg)]=1
|
||||
glm.pred
|
||||
auto$mpg01=rep(0,397)
|
||||
auto$mpg01[auto$mpg>median(auto$mpg)]=1
|
||||
auto$mpg01
|
||||
auto$mpg01
|
||||
auto$mpg01
|
||||
plots(auto)
|
||||
plot(auto)
|
||||
boxplot(auto)
|
||||
boxplot.matrix(auto)
|
||||
help(boxplot)
|
||||
boxplot(auto$mpg01,auto)
|
||||
boxplot(auto$mpg,auto)
|
||||
boxplot(auto$mpg)
|
||||
boxplot(auto)
|
||||
boxplot(mpg01 ~ auto)
|
||||
boxplot(mpg01 ~)
|
||||
boxplot(auto$mpg01 ~ auto)
|
||||
attach(auto)
|
||||
boxplot(mpg01)
|
||||
boxplot(mpg01 ~ auto)
|
||||
boxplot(mpg01 ~ auto,auto)
|
||||
boxplot(mpg01 ~ auto,data = auto)
|
||||
help(plot.table)
|
||||
plot.table(auto)
|
||||
help(plot.table)
|
||||
plot(auto)
|
||||
plot(auto,t="box")
|
||||
help(plot.table)
|
||||
help(plot.table,plot.frame=1)
|
||||
help(plot.table)
|
||||
help(plot.table,frame.plot=1)
|
||||
help(plot.table)
|
||||
help(plot.table,frame.plot=is.num)
|
||||
help(plot.table)
|
||||
plot(auto,t="box",frame.plot=1)
|
||||
plot(auto,frame.plot=1)
|
||||
plot(auto,frame.plot=1)
|
||||
plot(auto,frame.plot=is.num)
|
||||
plot(auto,frame.plot=0)
|
||||
plot(auto,frame.plot="0")
|
||||
plot(auto,frame.plot="1")
|
||||
plot(auto,frame.plot=TRUE)
|
||||
plot(auto,frame.plot=FALSE)
|
||||
plot(auto,frame.plot=TRUE)
|
||||
plot(auto,frame.plot=T)
|
||||
plot(auto,frame.plot=1)
|
||||
boxplot(mpg~mpg01,auto)
|
||||
boxplot(mpg01 ~ mpg,auto)
|
||||
boxplot(mpg01 ~ *,auto)
|
||||
boxplot(mpg01 ~ ,auto)
|
||||
boxplot(mpg01 ~ auto,auto)
|
||||
boxplot(mpg01,auto)
|
||||
boxplot(auto)
|
||||
boxplot(auto,y=mpg01)
|
||||
boxplot(auto,y=mpg)
|
||||
boxplot(data = auto)
|
||||
boxplot(auto)
|
||||
help(for)
|
||||
plot(auto,frame.plot=1)
|
||||
plot(auto)
|
||||
names(auto)
|
||||
auto$name
|
||||
help(sample)
|
||||
x <- 1:12
|
||||
x
|
||||
sample(x)
|
||||
help(sample)
|
||||
sample(x,replace=T)
|
||||
sample(x,replace=T)
|
||||
sample(x,replace=F)
|
||||
c
|
||||
x
|
||||
sample(x,replace=T)
|
||||
x
|
||||
help(sample)
|
||||
sample(x[x>9])
|
||||
sample(x[x>8])
|
||||
help(sample)
|
||||
x <- 1:10
|
||||
sample(x[x>8])
|
||||
sample(x[x>])
|
||||
help(sample)
|
||||
help(sample)
|
||||
help(sample)
|
||||
sample(auto,size=length(mpg01)/2)
|
||||
x <- length(mpg01)
|
||||
sample(x,size=length(mpg01)/2)
|
||||
auto[sample(x,size=length(mpg01)/2)]
|
||||
auto$mpg[sample(x,size=length(mpg01)/2)]
|
||||
help(data.frame)
|
||||
data.frame(
|
||||
help(data.frame)
|
||||
auto[sample(x,size=length(mpg01)/2)]
|
||||
train = sample(x,size=length(mpg01)/2)
|
||||
train =
|
||||
auto[train]
|
||||
auto$mpg[train]
|
||||
auto$mpg[train,]
|
||||
auto$mpg[train]
|
||||
auto$mpg[23]
|
||||
auto$mpg[228]
|
||||
auto$mpg[391]
|
||||
auto.test=auto[!train]
|
||||
auto.train=auto[train]
|
||||
auto.test
|
||||
summary(auto.test)
|
||||
train=(mpg<15)
|
||||
train
|
||||
train = (sample(x,size=length(mpg01)/2))
|
||||
train
|
||||
head(auto)
|
||||
auto[,train[
|
||||
auto[,train]
|
||||
train
|
||||
help(contains)
|
||||
auto[1,train]
|
||||
train
|
||||
auto[[,train]]
|
||||
auto[[1,train]]
|
||||
autoi
|
||||
head(auto)
|
||||
head(auto[sample(nrow(auto),397/2)])
|
||||
head(auto[sample(nrow(auto),3)])
|
||||
data = data.frame(auto)
|
||||
data
|
||||
head(data[sample(nrow(data),3)])
|
||||
nrow(data)
|
||||
head(data[sample(ncol(data),3)])
|
||||
head(data[sample(ncol(data),397/2)])
|
||||
head(data[sample(ncol(data),3)])
|
||||
head(data[sample(ncol(data),3)])
|
||||
head(data[sample(ncol(data),3)])
|
||||
head(data[sample(ncol(data),3)])
|
||||
head(data[,sample(ncol(data),3)])
|
||||
head(data[,sample(ncol(data),3)])
|
||||
head(data[,sample(ncol(data),3)])
|
||||
head(data[,sample(ncol(data),3)])
|
||||
head(data[,sample(ncol(data),3)])
|
||||
head(data[sample(ncol(data),3),])
|
||||
head(data[sample(ncol(data),3),])
|
||||
head(data[sample(ncol(data),3),])
|
||||
head(data[sample(nrow(data),3),])
|
||||
head(data[sample(nrow(data),397/2),])
|
||||
head(data[sample(nrow(data),397/2),])
|
||||
head(data[sample(nrow(data),397/2),])
|
||||
head(data[sample(nrow(data),397/2),])
|
||||
head(data[sample(nrow(data),397/2),])
|
||||
head(auto[sample(nrow(auto),397/2),])
|
||||
head(auto[sample(nrow(auto),397/2),])
|
||||
head(auto[sample(nrow(auto),397/2),])
|
||||
head(auto[sample(nrow(auto),397/2),])
|
||||
head(auto[sample(nrow(auto),397/2),])
|
||||
head(auto[sample(nrow(auto),397/2),])
|
||||
head(auto[sample(nrow(auto),397/2),])
|
||||
train = auto[sample(nrow(auto),397/2),]
|
||||
[sample(nrow(auto),397/2),]
|
||||
sample(nrow(auto),397/2)
|
||||
train sample(nrow(auto),397/2)
|
||||
train = sample(nrow(auto),397/2)
|
||||
autp[train,]
|
||||
auto[train,]
|
||||
train = sample(nrow(auto),397/2)
|
||||
head(auto[train,])
|
||||
head(auto[!train,])
|
||||
traindata = auto[train,]
|
||||
testdata = auto[!train,]
|
||||
testdata
|
||||
traindata
|
||||
length(traindata)
|
||||
length(traindata$mpg)
|
||||
198*2
|
||||
summary(testdata)
|
||||
testdata = auto[!train]
|
||||
testdata
|
||||
testdata = auto[!train,]
|
||||
train
|
||||
summary(train)
|
||||
names(train)
|
||||
head(traindata)
|
||||
testdata = auto[!train,]
|
||||
testdata
|
||||
!train
|
||||
train
|
||||
?sample
|
||||
sort(train)
|
||||
train_vals = train
|
||||
train = rep(false,397)
|
||||
train = rep(F,397)
|
||||
train
|
||||
help for
|
||||
?for
|
||||
?for
|
||||
help)for)
|
||||
help(for)
|
||||
help(for)
|
||||
help lapply()
|
||||
?lapply
|
||||
sapply(train,
|
||||
?sapply
|
||||
sapply(train,
|
||||
?sapply
|
||||
train[train_vals]=T
|
||||
train
|
||||
traindata = auto[train,]
|
||||
traindata
|
||||
length(auto)
|
||||
length(traindata)
|
||||
length(traindata$mpg)
|
||||
testdata=auto[!train,]
|
||||
length(testdate$mpg)
|
||||
length(testdata$mpg)
|
||||
training_indices = sample(nrow(auto),397/2)
|
||||
train_bools = rep(F,length(auto$mpg))
|
||||
train_bools[training_indices]=T
|
||||
head(train_bools)
|
||||
length(train_bools)
|
||||
train_data = auto[train_bools,]
|
||||
test_data = auto[!train_bools,]
|
||||
summary(train_data)
|
||||
summary(test_data)
|
||||
lda.fit
|
||||
library(MASS)
|
||||
lda.fit
|
||||
lda()
|
||||
detach(auto)
|
||||
mpg01
|
||||
mpg
|
||||
attach(test_data)
|
||||
mpg01
|
||||
names()
|
||||
names(test_data)
|
||||
ldf.fit=lda(mpg01 ~ horsepower + weight + acceleration + displacement,data=test_data)
|
||||
detach(test_data)
|
||||
ldf.fit=lda(mpg01 ~ horsepower + weight + acceleration + displacement,data=test_data)
|
||||
lda.fit
|
||||
lda.fit=lda(mpg01 ~ horsepower + weight + acceleration + displacement,data=test_data)
|
||||
lda.fit
|
||||
summary(lda.fit)
|
||||
coefficients(lda.fit)
|
||||
plot(lda.fit)
|
||||
lda.pred=predict(lda.fit,test_data)
|
||||
lda.pred=predict(lda.fit, !training_bools)
|
||||
lda.pred=predict(lda.fit, !training_indices)
|
||||
test_data
|
||||
lda.pred=predict(lda.fit, test_data)
|
||||
lda.pred
|
||||
plot(lda.pred)
|
||||
names(lda.pred)
|
||||
lda.class=lda.pres$class
|
||||
lda.class=lda.pred$class
|
||||
table(lda.class,testdata)
|
||||
table(lda.class,test_data)
|
||||
length(lda.class)
|
||||
length(test_data)
|
||||
table(lda.class,test_data$mpg01)
|
||||
mean(lda.class==test_data$mpg01)
|
||||
sum(lda.pred$posterior[,1]>=.5)
|
||||
sum(lda.pred$posterior[,1]<.5)
|
||||
lda.pred$posterior[,1]
|
||||
sum(lda.pred$posterior<.5)
|
||||
lda.pred$posterior
|
||||
lda.pred$posterior<5
|
||||
lda.pred$posterior<.5
|
||||
sum(lda.pred$posterior<.5)
|
||||
sum(lda.pred$posterior<.5[,1])
|
||||
sum(lda.pred$posterior<.5[1])
|
||||
sum(lda.pred$posterior<.5[2])
|
||||
lda.pred$posterior<.5[2]
|
||||
lda.pred$posterior<.5
|
||||
lda.pred$posterior
|
||||
lda.pred$posterior[,1]
|
||||
lda.pred$posterior[1,]
|
||||
lda.pred$posterior[,2]
|
||||
lda.pred$posterior[,1]
|
||||
lda.pred$posterior[,1]>.5
|
||||
sum(lda.pred$posterior[,1]>.5)
|
||||
sum.bool(lda.pred$posterior[,1]>.5)
|
||||
?sum
|
||||
sum.bool(lda.pred$posterior[,1]>.5,na.rm=T)
|
||||
sum(lda.pred$posterior[,1]>.5,na.rm=T)
|
||||
sum(lda.pred$posterior[,1]>.5)
|
||||
sum(lda.pred$posterior[,1]>.5,na.rm=T)
|
||||
sum(lda.pred$posterior[,1]>=.5,na.rm=T)
|
||||
sum(lda.pred$posterior[,1]<.5,na.rm=T)
|
||||
mean(lda.pred$[,1]==test_data,na.rm=T)
|
||||
lda.pred
|
||||
lda.pred$class
|
||||
lda.pred$class==test_data$mpg01
|
||||
mean(lda.pred$class==test_data$mpg01,na.rm=T)
|
||||
mean(lda.pred$class!=test_data$mpg01,na.rm=T)
|
||||
lda.fit=lda(mpg01 ~ horsepower + weight + acceleration + displacement,data=train_data)
|
||||
lda.fit
|
||||
mean(lda.pred$class==test_data$mpg01,na.rm=T)
|
||||
lda.pred=predict(lda.fit, test_data)
|
||||
mean(lda.pred$class==test_data$mpg01,na.rm=T)
|
||||
mean(lda.pred$class!=test_data$mpg01,na.rm=T)
|
||||
train_data == test_data
|
||||
train_data$mpg01 == test_data$mpg01
|
||||
lda.fit=lda(mpg01 ~ horsepower + weight + acceleration + displacement,data=train_data)
|
||||
lda.pred=predict(lda.fit, test_data)
|
||||
mean(lda.pred$class!=test_data$mpg01,na.rm=T)
|
||||
lda.pred
|
||||
lda.pred$posterior[,1]
|
||||
summary(lda.fit)
|
||||
lda.fit
|
||||
lda.fit=lda(mpg01 ~ horsepower + weight + acceleration + displacement,data=test_data)
|
||||
lda.fit
|
||||
mean(lda.pred$class!=test_data$mpg01,na.rm=T)
|
||||
lda.pred=predict(lda.fit, test_data)
|
||||
mean(lda.pred$class!=test_data$mpg01,na.rm=T)
|
||||
head(lda.pred)
|
||||
lda.fit=lda(mpg01 ~ horsepower + weight + acceleration + displacement,data=train_data)
|
||||
lda.pred=predict(lda.fit, test_data)
|
||||
head(lda.pred)
|
||||
mean(lda.pred$class!=test_data$mpg01,na.rm=T)
|
||||
qda.fit=qda(mpg01 ~ horsepower + weight + acceleration + displacement,data=train_data)
|
||||
qda.fit
|
||||
qda.class=predict(qda.fit,test_data)$class
|
||||
qda.class=predict(qda.fit,test_data,na.rm=T)$class
|
||||
qda.class=predict(qda.fit,test_data)$class
|
||||
qda.class
|
||||
mean(qda.pred$class!=test_data$mpg01,na.rm=T)
|
||||
qda.pred=predict(qda.fit,test_data)
|
||||
qda.pred=predict(qda.fit,test_data,na.rm=T)
|
||||
mean(qda.pred$class!=test_data$mpg01,na.rm=T)
|
||||
glm.fit=glm(mpg01 ~ horsepower + weight + acceleration + displacement,data=train_data,family=binomial)
|
||||
glm.probs=predict(glm.fit,test_data,type="response")
|
||||
glm.pred=rep(0,199)
|
||||
glm.pred[glm.probs>.5]=1
|
||||
table(glm.pred,test_data$mpg01)
|
||||
mean(glm.pred!=test_data$mpg01)
|
||||
library(class)
|
||||
?cbind
|
||||
?knn
|
||||
knn.fit = knn(train_data,test_data,auto$mpg01[training_indices])
|
||||
knn.fit = knn(train_data,test_data,auto$mpg01[training_indices],k=1)
|
||||
knn.fit = knn(train_data,test_data,auto$mpg01[training_indices],k=1)
|
||||
?knn
|
||||
training_indices
|
||||
train_bools
|
||||
knn.fit = knn(train_data,test_data,auto$mpg01[train_bools],k=1)
|
||||
sdf = (mpg01<1)
|
||||
sdf = (auto$mpg01<1)
|
||||
sdf
|
||||
train_bools
|
||||
cbind(horsepower,displacement)
|
||||
cbind(train_data$horsepower,displacement)
|
||||
cbind(train_data$horsepower,train_data$displacement)
|
||||
cbind(auto$horsepower,auto$displacement)[train_bools]
|
||||
cbind(auto$horsepower,auto$displacement)[train_bools,]
|
||||
cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,]
|
||||
cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,]
|
||||
train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,]
|
||||
test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,]
|
||||
train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,]
|
||||
test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,]
|
||||
train.mpg01 = auto[train_bools]
|
||||
train.mpg01 = auto$mpg01[train_bools]
|
||||
test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,]
|
||||
train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,]
|
||||
test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,]
|
||||
train.mpg01 = auto$mpg01[train_bools]
|
||||
set.seed(56)
|
||||
knn.pred = knn(train.X,test.X,train.mpg01,k=1)
|
||||
?cbind
|
||||
?Knn
|
||||
?knn
|
||||
train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,]
|
||||
test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,]
|
||||
train.mpg01 = auto$mpg01[train_bools]
|
||||
train.X = train.X[!is.na(train.X)]
|
||||
test.X = data.frame(test.X,
|
||||
train.mpg01 = train.mpg01[!is.na(train.mpg01)]
|
||||
knn.pred = knn(train.X,test.X,train.mpg01,k=1)
|
||||
length(train.mpg01)
|
||||
length(test.X)
|
||||
text.X
|
||||
test.X
|
||||
test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,]
|
||||
length(test.X)
|
||||
test.X
|
||||
knn.pred = knn(train.X,test.X,train.mpg01,k=1)
|
||||
train.X
|
||||
train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,]
|
||||
train.X
|
||||
test.X
|
||||
knn.pred = knn(train.X,test.X,train.mpg01,k=1)
|
||||
?knn
|
||||
length(train.X)
|
||||
length(train.X[1,])
|
||||
length(train.X[,1])
|
||||
?knn
|
||||
plot(auto)
|
||||
train.X = cbind(auto$horsepower,auto$displacement)[train_bools,]
|
||||
test.X = cbind(auto$horsepower,auto$displacement)[!train_bools,]
|
||||
train.mpg01 = auto$mpg01[train_bools]
|
||||
knn.pred = knn(train.X,test.X,train.mpg01,k=1)
|
||||
train.X
|
||||
test.X
|
||||
train.mpg01
|
||||
knn.pred = knn(train.X,test.X,train.mpg01,k=1)
|
||||
q()
|
||||
train
|
||||
train.X
|
||||
train.X
|
||||
test.X
|
||||
p1 = seq(1:1))
|
||||
p1 = 1:10
|
||||
p1
|
||||
p1 = ,1:10
|
||||
p2 = 10:20
|
||||
p2
|
||||
cbind(p1,p2)
|
||||
p3=c(1,2,3,4,5,7,9,8,10)
|
||||
p4=c(10,11,12,13,14,15,16,17,18,29,20)
|
||||
px = cbind(p1,p2)
|
||||
py = cbind(p3,p4)
|
||||
py
|
||||
?formula
|
||||
test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,]
|
||||
train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,]
|
||||
train.mpg01 = auto$mpg01[train_bools]
|
||||
knn.pred = knn(train.X,test.X,train.mpg01,k=1)
|
||||
import(library)
|
||||
library(MASS)
|
||||
knn.pred = knn(train.X,test.X,train.mpg01,k=1)
|
||||
library(library)
|
||||
library(class)
|
||||
knn.pred = knn(train.X,test.X,train.mpg01,k=1)
|
||||
knn.pred
|
||||
summary(test_data)
|
||||
summary(train_data)
|
||||
fix(train_data)
|
||||
?fix
|
||||
fix(test_data)
|
||||
test_data.fix
|
||||
test_data.fix()
|
||||
fix(test_data)
|
||||
q()
|
||||
3137
hw3/answers.ps
Normal file
3137
hw3/answers.ps
Normal file
File diff suppressed because it is too large
Load Diff
398
hw3/auto.data
Executable file
398
hw3/auto.data
Executable file
@ -0,0 +1,398 @@
|
||||
mpg cylinders displacement horsepower weight acceleration year origin name
|
||||
18.0 8 307.0 130.0 3504. 12.0 70 1 "chevrolet chevelle malibu"
|
||||
15.0 8 350.0 165.0 3693. 11.5 70 1 "buick skylark 320"
|
||||
18.0 8 318.0 150.0 3436. 11.0 70 1 "plymouth satellite"
|
||||
16.0 8 304.0 150.0 3433. 12.0 70 1 "amc rebel sst"
|
||||
17.0 8 302.0 140.0 3449. 10.5 70 1 "ford torino"
|
||||
15.0 8 429.0 198.0 4341. 10.0 70 1 "ford galaxie 500"
|
||||
14.0 8 454.0 220.0 4354. 9.0 70 1 "chevrolet impala"
|
||||
14.0 8 440.0 215.0 4312. 8.5 70 1 "plymouth fury iii"
|
||||
14.0 8 455.0 225.0 4425. 10.0 70 1 "pontiac catalina"
|
||||
15.0 8 390.0 190.0 3850. 8.5 70 1 "amc ambassador dpl"
|
||||
15.0 8 383.0 170.0 3563. 10.0 70 1 "dodge challenger se"
|
||||
14.0 8 340.0 160.0 3609. 8.0 70 1 "plymouth 'cuda 340"
|
||||
15.0 8 400.0 150.0 3761. 9.5 70 1 "chevrolet monte carlo"
|
||||
14.0 8 455.0 225.0 3086. 10.0 70 1 "buick estate wagon (sw)"
|
||||
24.0 4 113.0 95.00 2372. 15.0 70 3 "toyota corona mark ii"
|
||||
22.0 6 198.0 95.00 2833. 15.5 70 1 "plymouth duster"
|
||||
18.0 6 199.0 97.00 2774. 15.5 70 1 "amc hornet"
|
||||
21.0 6 200.0 85.00 2587. 16.0 70 1 "ford maverick"
|
||||
27.0 4 97.00 88.00 2130. 14.5 70 3 "datsun pl510"
|
||||
26.0 4 97.00 46.00 1835. 20.5 70 2 "volkswagen 1131 deluxe sedan"
|
||||
25.0 4 110.0 87.00 2672. 17.5 70 2 "peugeot 504"
|
||||
24.0 4 107.0 90.00 2430. 14.5 70 2 "audi 100 ls"
|
||||
25.0 4 104.0 95.00 2375. 17.5 70 2 "saab 99e"
|
||||
26.0 4 121.0 113.0 2234. 12.5 70 2 "bmw 2002"
|
||||
21.0 6 199.0 90.00 2648. 15.0 70 1 "amc gremlin"
|
||||
10.0 8 360.0 215.0 4615. 14.0 70 1 "ford f250"
|
||||
10.0 8 307.0 200.0 4376. 15.0 70 1 "chevy c20"
|
||||
11.0 8 318.0 210.0 4382. 13.5 70 1 "dodge d200"
|
||||
9.0 8 304.0 193.0 4732. 18.5 70 1 "hi 1200d"
|
||||
27.0 4 97.00 88.00 2130. 14.5 71 3 "datsun pl510"
|
||||
28.0 4 140.0 90.00 2264. 15.5 71 1 "chevrolet vega 2300"
|
||||
25.0 4 113.0 95.00 2228. 14.0 71 3 "toyota corona"
|
||||
25.0 4 98.00 ? 2046. 19.0 71 1 "ford pinto"
|
||||
19.0 6 232.0 100.0 2634. 13.0 71 1 "amc gremlin"
|
||||
16.0 6 225.0 105.0 3439. 15.5 71 1 "plymouth satellite custom"
|
||||
17.0 6 250.0 100.0 3329. 15.5 71 1 "chevrolet chevelle malibu"
|
||||
19.0 6 250.0 88.00 3302. 15.5 71 1 "ford torino 500"
|
||||
18.0 6 232.0 100.0 3288. 15.5 71 1 "amc matador"
|
||||
14.0 8 350.0 165.0 4209. 12.0 71 1 "chevrolet impala"
|
||||
14.0 8 400.0 175.0 4464. 11.5 71 1 "pontiac catalina brougham"
|
||||
14.0 8 351.0 153.0 4154. 13.5 71 1 "ford galaxie 500"
|
||||
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|
||||
22.3 4 140.0 88.00 2890. 17.3 79 1 "ford fairmont 4"
|
||||
20.2 6 232.0 90.00 3265. 18.2 79 1 "amc concord dl 6"
|
||||
20.6 6 225.0 110.0 3360. 16.6 79 1 "dodge aspen 6"
|
||||
17.0 8 305.0 130.0 3840. 15.4 79 1 "chevrolet caprice classic"
|
||||
17.6 8 302.0 129.0 3725. 13.4 79 1 "ford ltd landau"
|
||||
16.5 8 351.0 138.0 3955. 13.2 79 1 "mercury grand marquis"
|
||||
18.2 8 318.0 135.0 3830. 15.2 79 1 "dodge st. regis"
|
||||
16.9 8 350.0 155.0 4360. 14.9 79 1 "buick estate wagon (sw)"
|
||||
15.5 8 351.0 142.0 4054. 14.3 79 1 "ford country squire (sw)"
|
||||
19.2 8 267.0 125.0 3605. 15.0 79 1 "chevrolet malibu classic (sw)"
|
||||
18.5 8 360.0 150.0 3940. 13.0 79 1 "chrysler lebaron town @ country (sw)"
|
||||
31.9 4 89.00 71.00 1925. 14.0 79 2 "vw rabbit custom"
|
||||
34.1 4 86.00 65.00 1975. 15.2 79 3 "maxda glc deluxe"
|
||||
35.7 4 98.00 80.00 1915. 14.4 79 1 "dodge colt hatchback custom"
|
||||
27.4 4 121.0 80.00 2670. 15.0 79 1 "amc spirit dl"
|
||||
25.4 5 183.0 77.00 3530. 20.1 79 2 "mercedes benz 300d"
|
||||
23.0 8 350.0 125.0 3900. 17.4 79 1 "cadillac eldorado"
|
||||
27.2 4 141.0 71.00 3190. 24.8 79 2 "peugeot 504"
|
||||
23.9 8 260.0 90.00 3420. 22.2 79 1 "oldsmobile cutlass salon brougham"
|
||||
34.2 4 105.0 70.00 2200. 13.2 79 1 "plymouth horizon"
|
||||
34.5 4 105.0 70.00 2150. 14.9 79 1 "plymouth horizon tc3"
|
||||
31.8 4 85.00 65.00 2020. 19.2 79 3 "datsun 210"
|
||||
37.3 4 91.00 69.00 2130. 14.7 79 2 "fiat strada custom"
|
||||
28.4 4 151.0 90.00 2670. 16.0 79 1 "buick skylark limited"
|
||||
28.8 6 173.0 115.0 2595. 11.3 79 1 "chevrolet citation"
|
||||
26.8 6 173.0 115.0 2700. 12.9 79 1 "oldsmobile omega brougham"
|
||||
33.5 4 151.0 90.00 2556. 13.2 79 1 "pontiac phoenix"
|
||||
41.5 4 98.00 76.00 2144. 14.7 80 2 "vw rabbit"
|
||||
38.1 4 89.00 60.00 1968. 18.8 80 3 "toyota corolla tercel"
|
||||
32.1 4 98.00 70.00 2120. 15.5 80 1 "chevrolet chevette"
|
||||
37.2 4 86.00 65.00 2019. 16.4 80 3 "datsun 310"
|
||||
28.0 4 151.0 90.00 2678. 16.5 80 1 "chevrolet citation"
|
||||
26.4 4 140.0 88.00 2870. 18.1 80 1 "ford fairmont"
|
||||
24.3 4 151.0 90.00 3003. 20.1 80 1 "amc concord"
|
||||
19.1 6 225.0 90.00 3381. 18.7 80 1 "dodge aspen"
|
||||
34.3 4 97.00 78.00 2188. 15.8 80 2 "audi 4000"
|
||||
29.8 4 134.0 90.00 2711. 15.5 80 3 "toyota corona liftback"
|
||||
31.3 4 120.0 75.00 2542. 17.5 80 3 "mazda 626"
|
||||
37.0 4 119.0 92.00 2434. 15.0 80 3 "datsun 510 hatchback"
|
||||
32.2 4 108.0 75.00 2265. 15.2 80 3 "toyota corolla"
|
||||
46.6 4 86.00 65.00 2110. 17.9 80 3 "mazda glc"
|
||||
27.9 4 156.0 105.0 2800. 14.4 80 1 "dodge colt"
|
||||
40.8 4 85.00 65.00 2110. 19.2 80 3 "datsun 210"
|
||||
44.3 4 90.00 48.00 2085. 21.7 80 2 "vw rabbit c (diesel)"
|
||||
43.4 4 90.00 48.00 2335. 23.7 80 2 "vw dasher (diesel)"
|
||||
36.4 5 121.0 67.00 2950. 19.9 80 2 "audi 5000s (diesel)"
|
||||
30.0 4 146.0 67.00 3250. 21.8 80 2 "mercedes-benz 240d"
|
||||
44.6 4 91.00 67.00 1850. 13.8 80 3 "honda civic 1500 gl"
|
||||
40.9 4 85.00 ? 1835. 17.3 80 2 "renault lecar deluxe"
|
||||
33.8 4 97.00 67.00 2145. 18.0 80 3 "subaru dl"
|
||||
29.8 4 89.00 62.00 1845. 15.3 80 2 "vokswagen rabbit"
|
||||
32.7 6 168.0 132.0 2910. 11.4 80 3 "datsun 280-zx"
|
||||
23.7 3 70.00 100.0 2420. 12.5 80 3 "mazda rx-7 gs"
|
||||
35.0 4 122.0 88.00 2500. 15.1 80 2 "triumph tr7 coupe"
|
||||
23.6 4 140.0 ? 2905. 14.3 80 1 "ford mustang cobra"
|
||||
32.4 4 107.0 72.00 2290. 17.0 80 3 "honda accord"
|
||||
27.2 4 135.0 84.00 2490. 15.7 81 1 "plymouth reliant"
|
||||
26.6 4 151.0 84.00 2635. 16.4 81 1 "buick skylark"
|
||||
25.8 4 156.0 92.00 2620. 14.4 81 1 "dodge aries wagon (sw)"
|
||||
23.5 6 173.0 110.0 2725. 12.6 81 1 "chevrolet citation"
|
||||
30.0 4 135.0 84.00 2385. 12.9 81 1 "plymouth reliant"
|
||||
39.1 4 79.00 58.00 1755. 16.9 81 3 "toyota starlet"
|
||||
39.0 4 86.00 64.00 1875. 16.4 81 1 "plymouth champ"
|
||||
35.1 4 81.00 60.00 1760. 16.1 81 3 "honda civic 1300"
|
||||
32.3 4 97.00 67.00 2065. 17.8 81 3 "subaru"
|
||||
37.0 4 85.00 65.00 1975. 19.4 81 3 "datsun 210 mpg"
|
||||
37.7 4 89.00 62.00 2050. 17.3 81 3 "toyota tercel"
|
||||
34.1 4 91.00 68.00 1985. 16.0 81 3 "mazda glc 4"
|
||||
34.7 4 105.0 63.00 2215. 14.9 81 1 "plymouth horizon 4"
|
||||
34.4 4 98.00 65.00 2045. 16.2 81 1 "ford escort 4w"
|
||||
29.9 4 98.00 65.00 2380. 20.7 81 1 "ford escort 2h"
|
||||
33.0 4 105.0 74.00 2190. 14.2 81 2 "volkswagen jetta"
|
||||
34.5 4 100.0 ? 2320. 15.8 81 2 "renault 18i"
|
||||
33.7 4 107.0 75.00 2210. 14.4 81 3 "honda prelude"
|
||||
32.4 4 108.0 75.00 2350. 16.8 81 3 "toyota corolla"
|
||||
32.9 4 119.0 100.0 2615. 14.8 81 3 "datsun 200sx"
|
||||
31.6 4 120.0 74.00 2635. 18.3 81 3 "mazda 626"
|
||||
28.1 4 141.0 80.00 3230. 20.4 81 2 "peugeot 505s turbo diesel"
|
||||
30.7 6 145.0 76.00 3160. 19.6 81 2 "volvo diesel"
|
||||
25.4 6 168.0 116.0 2900. 12.6 81 3 "toyota cressida"
|
||||
24.2 6 146.0 120.0 2930. 13.8 81 3 "datsun 810 maxima"
|
||||
22.4 6 231.0 110.0 3415. 15.8 81 1 "buick century"
|
||||
26.6 8 350.0 105.0 3725. 19.0 81 1 "oldsmobile cutlass ls"
|
||||
20.2 6 200.0 88.00 3060. 17.1 81 1 "ford granada gl"
|
||||
17.6 6 225.0 85.00 3465. 16.6 81 1 "chrysler lebaron salon"
|
||||
28.0 4 112.0 88.00 2605. 19.6 82 1 "chevrolet cavalier"
|
||||
27.0 4 112.0 88.00 2640. 18.6 82 1 "chevrolet cavalier wagon"
|
||||
34.0 4 112.0 88.00 2395. 18.0 82 1 "chevrolet cavalier 2-door"
|
||||
31.0 4 112.0 85.00 2575. 16.2 82 1 "pontiac j2000 se hatchback"
|
||||
29.0 4 135.0 84.00 2525. 16.0 82 1 "dodge aries se"
|
||||
27.0 4 151.0 90.00 2735. 18.0 82 1 "pontiac phoenix"
|
||||
24.0 4 140.0 92.00 2865. 16.4 82 1 "ford fairmont futura"
|
||||
36.0 4 105.0 74.00 1980. 15.3 82 2 "volkswagen rabbit l"
|
||||
37.0 4 91.00 68.00 2025. 18.2 82 3 "mazda glc custom l"
|
||||
31.0 4 91.00 68.00 1970. 17.6 82 3 "mazda glc custom"
|
||||
38.0 4 105.0 63.00 2125. 14.7 82 1 "plymouth horizon miser"
|
||||
36.0 4 98.00 70.00 2125. 17.3 82 1 "mercury lynx l"
|
||||
36.0 4 120.0 88.00 2160. 14.5 82 3 "nissan stanza xe"
|
||||
36.0 4 107.0 75.00 2205. 14.5 82 3 "honda accord"
|
||||
34.0 4 108.0 70.00 2245 16.9 82 3 "toyota corolla"
|
||||
38.0 4 91.00 67.00 1965. 15.0 82 3 "honda civic"
|
||||
32.0 4 91.00 67.00 1965. 15.7 82 3 "honda civic (auto)"
|
||||
38.0 4 91.00 67.00 1995. 16.2 82 3 "datsun 310 gx"
|
||||
25.0 6 181.0 110.0 2945. 16.4 82 1 "buick century limited"
|
||||
38.0 6 262.0 85.00 3015. 17.0 82 1 "oldsmobile cutlass ciera (diesel)"
|
||||
26.0 4 156.0 92.00 2585. 14.5 82 1 "chrysler lebaron medallion"
|
||||
22.0 6 232.0 112.0 2835 14.7 82 1 "ford granada l"
|
||||
32.0 4 144.0 96.00 2665. 13.9 82 3 "toyota celica gt"
|
||||
36.0 4 135.0 84.00 2370. 13.0 82 1 "dodge charger 2.2"
|
||||
27.0 4 151.0 90.00 2950. 17.3 82 1 "chevrolet camaro"
|
||||
27.0 4 140.0 86.00 2790. 15.6 82 1 "ford mustang gl"
|
||||
44.0 4 97.00 52.00 2130. 24.6 82 2 "vw pickup"
|
||||
32.0 4 135.0 84.00 2295. 11.6 82 1 "dodge rampage"
|
||||
28.0 4 120.0 79.00 2625. 18.6 82 1 "ford ranger"
|
||||
31.0 4 119.0 82.00 2720. 19.4 82 1 "chevy s-10"
|
||||
BIN
hw4/.RData
Normal file
BIN
hw4/.RData
Normal file
Binary file not shown.
249
hw4/.Rhistory
Normal file
249
hw4/.Rhistory
Normal file
@ -0,0 +1,249 @@
|
||||
auto = read.table("auto.data",header=T,na.strings="?")
|
||||
auto$mpg01=rep(0,397)
|
||||
auto$mpg01[auto$mpg>median(auto$mpg)]=1
|
||||
library(ISLR)
|
||||
library(MASS)
|
||||
library(class)
|
||||
train_bools <- (auto$year %% 2 == 0)
|
||||
train_data = auto[train_bools,]
|
||||
test_data = auto[!train_bools,]
|
||||
help(knn)
|
||||
help(knn)
|
||||
train <- rbind(iris3[1:25,,1], iris3[1:25,,2], iris3[1:25,,3])
|
||||
test <- rbind(iris3[26:50,,1], iris3[26:50,,2], iris3[26:50,,3])
|
||||
train
|
||||
test
|
||||
?knn
|
||||
cl <- factor(c(rep("s",25), rep("c",25), rep("v",25)))
|
||||
cl
|
||||
length(cl)
|
||||
length(train)
|
||||
nrows(train)
|
||||
nrow(train)
|
||||
train.X
|
||||
train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,]
|
||||
train.X
|
||||
test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,]
|
||||
test.X
|
||||
train.X
|
||||
train.mpg01 = auto$mpg01[train_bools]
|
||||
train.mpg01
|
||||
length(train.mpg01)
|
||||
nrow(train.X)
|
||||
knn(train.X,train.Y,train.mpg01,K=1)
|
||||
knn(train.X,train.Y,train.mpg01,k=1)
|
||||
knn(train.X,test.X,train.mpg01,k=1)
|
||||
train.X
|
||||
na.omit(train.X)
|
||||
?na.omit
|
||||
na.omit(train.X)
|
||||
na.omit(train.X)
|
||||
knn(na.omit(train.X),test.X,train.mpg01,k=1)
|
||||
knn(na.omit(train.X),test.X,na.omit(train.mpg01),k=1)
|
||||
knn(na.omit(train.X),na.omit(test.X),na.omit(train.mpg01),k=1)
|
||||
train.mpg012 = na.omit(auto$mpg01)[train_bools]
|
||||
train.mpg012
|
||||
train.mpg01
|
||||
nrow(train)
|
||||
na.omit(auto)
|
||||
auto
|
||||
na.omit(auto)
|
||||
summary(auto)
|
||||
summary(na.omit(auto))
|
||||
Auto = na.omit(auto)
|
||||
auto = na.omit(auto)
|
||||
ncol(auto)
|
||||
nrow(auto)
|
||||
auto <- na.omit(auto)
|
||||
train_bools <- (auto$year %% 2 == 0)
|
||||
train_data = auto[train_bools,]
|
||||
test_data = auto[!train_bools,]
|
||||
train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,]
|
||||
test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,]
|
||||
train.mpg01 = auto$mpg01[train_bools]
|
||||
knn.pred = knn(train.X,test.X,train.mpg01,k=1)
|
||||
mean(knn.pred != auto$mpg01)
|
||||
mean(knn.pred != test_data$mpg01)
|
||||
knn.pred = knn(train.X,test.X,train.mpg01,k=2)
|
||||
mean(knn.pred != test_data$mpg01)
|
||||
knn.pred = knn(train.X,test.X,train.mpg01,k=3)
|
||||
mean(knn.pred != test_data$mpg01)
|
||||
knn.pred = knn(train.X,test.X,train.mpg01,k=4)
|
||||
mean(knn.pred != test_data$mpg0)
|
||||
knn.pred
|
||||
length(knn.pred)
|
||||
dim(knn.pred)
|
||||
length(test_data)
|
||||
ncol(test_data)
|
||||
nrow(test_data)
|
||||
q()
|
||||
qda.fit
|
||||
fit.qda
|
||||
qda.fit
|
||||
auto
|
||||
qda.fit = qda(mpg01 ~ horsepower + weight + cylinders + displacement, data = train_data)
|
||||
import(MASS)
|
||||
qda.fit = qda(mpg01 ~ horsepower + weight + cylinders + displacement, data = train_data)
|
||||
import(class)
|
||||
library(MASS)
|
||||
qda.fit = qda(mpg01 ~ horsepower + weight + cylinders + displacement, data = train_data)
|
||||
qda.fit
|
||||
> mean(qda.pred$class!=test_data$mpg01,na.rm=T)
|
||||
mean(qda.pred$class!=test_data$mpg01,na.rm=T)
|
||||
qda.fit=qda(mpg01 ~ horsepower + weight + acceleration + displacement,data=train_data)
|
||||
qda.pred=predict(qda.fit,test_data,na.rm=T)
|
||||
mean(qda.pred$class!=test_data$mpg01,na.rm=T)
|
||||
qda.fit=qda(mpg01 ~ horsepower + weight + cylinders + displacement,data=train_data)
|
||||
qda.pred=predict(qda.fit,test_data,na.rm=T)
|
||||
mean(qda.pred$class!=test_data$mpg01,na.rm=T)
|
||||
qda.fit
|
||||
qda.pred=predict(qda.fit,test_data,na.rm=T)
|
||||
mean(qda.pred$class!=test_data$mpg01,na.rm=T)
|
||||
glm.fit=glm(mpg01 ~ horsepower + weight + cylinders + displacement,data=train_data,family=binomial)
|
||||
glm.probs=predict(glm.fit,test_data,type="response")
|
||||
glm.pred=rep(0,199)
|
||||
glm.pred[glm.probs>.5]=1
|
||||
mean(glm.pred!=test_data$mpg01)
|
||||
glm.fit=glm(mpg01 ~ horsepower + weight + cylinders + displacement,data=train_data,family=binomial)
|
||||
glm.probs=predict(glm.fit,test_data,type="response")
|
||||
glm.pred=rep(0,length(test_data)
|
||||
glm.pred[glm.probs>.5]=1
|
||||
mean(glm.pred!=test_data$mpg01)
|
||||
glm.fit=glm(mpg01 ~ horsepower + weight + cylinders + displacement,data=train_data,family=binomial)
|
||||
glm.probs=predict(glm.fit,test_data,type="response")
|
||||
glm.pred=rep(0,length(test_data))
|
||||
glm.pred[glm.probs>.5]=1
|
||||
mean(glm.pred!=test_data$mpg01)
|
||||
glm.fit=glm(mpg01 ~ horsepower + weight + cylinders + displacement,data=train_data,family=binomial)
|
||||
glm.probs=predict(glm.fit,test_data,type="response")
|
||||
glm.pred=rep(0,length(test_data))
|
||||
glm.pred[glm.probs>.5]=1
|
||||
mean(glm.pred!=test_data$mpg01)
|
||||
glm.pred
|
||||
glm.pred=rep(0,length(test_data))
|
||||
glm.pred
|
||||
test_data
|
||||
glm.fit=glm(mpg01 ~ horsepower + weight + cylinders + displacement,data=train_data,family=binomial)
|
||||
glm.probs=predict(glm.fit,test_data,type="response")
|
||||
glm.pred=rep(0,nrow(test_data))
|
||||
glm.pred[glm.probs>.5]=1
|
||||
mean(glm.pred!=test_data$mpg01)
|
||||
set.seed(1)
|
||||
auto <- na.omit(auto)
|
||||
train_bools <- (auto$year %% 2 == 0)
|
||||
train_data = auto[train_bools,]
|
||||
test_data = auto[!train_bools,]
|
||||
train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$cylinders)[train_bools,]
|
||||
test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$cylinders)[!train_bools,]
|
||||
train.mpg01 = auto$mpg01[train_bools]
|
||||
knn.pred = knn(train.X,test.X,train.mpg01,k=1)
|
||||
mean(knn.pred != test_data$mpg01)
|
||||
knn.pred = knn(train.X,test.X,train.mpg01,k=2)
|
||||
mean(knn.pred != test_data$mpg01)
|
||||
knn.pred = knn(train.X,test.X,train.mpg01,k=3)
|
||||
mean(knn.pred != test_data$mpg01)
|
||||
knn.pred = knn(train.X,test.X,train.mpg01,k=4)
|
||||
mean(knn.pred != test_data$mpg0)
|
||||
import(class)
|
||||
library(class)
|
||||
knn.pred = knn(train.X,test.X,train.mpg01,k=1)
|
||||
mean(knn.pred != test_data$mpg01)
|
||||
knn.pred = knn(train.X,test.X,train.mpg01,k=2)
|
||||
mean(knn.pred != test_data$mpg01)
|
||||
knn.pred = knn(train.X,test.X,train.mpg01,k=3)
|
||||
mean(knn.pred != test_data$mpg01)
|
||||
knn.pred = knn(train.X,test.X,train.mpg01,k=4)
|
||||
mean(knn.pred != test_data$mpg0)
|
||||
q()
|
||||
library(boot)
|
||||
library(MASS)
|
||||
library(ISLr)
|
||||
library(ISLR)
|
||||
data(Default)
|
||||
set.seed(45)
|
||||
fit.glm = glm(default ~ income + balance,Default)
|
||||
fit.glm = glm(default ~ income + balance,Default,family="binomial)
|
||||
fit.glm = glm(default ~ income + balance,Default,family="binomial")
|
||||
summary(glm)
|
||||
summary(fit.glm)
|
||||
fit.glm = glm(default ~ income + balance,Default)
|
||||
fit.glm = glm(default ~ income + balance,data = Default)
|
||||
fit.glm = glm(default ~ income + balance,Default,family="binomial")
|
||||
length(Default)
|
||||
length(Default$student)
|
||||
train.default1 = default[1:6001,]
|
||||
train.default1 = Default[1:6001,]
|
||||
train.default2 = Default[1:5001,]
|
||||
test.default1 = Default[!1:6001,]
|
||||
test.default1
|
||||
head(test.default1)
|
||||
test.default1$student
|
||||
test.default1 = Default[6002:10000,]
|
||||
head(test.default1)
|
||||
test.default2 = Default[5002:10000,]
|
||||
fit.glm.default1 = glm.fit(default ~ income + balance,data=Default,family="binomial")
|
||||
fit.glm.default1 = glm.fit(default ~ income + balance,Default,family="binomial")
|
||||
fit.glm = glm(default ~ income + balance,Default,family="binomial")
|
||||
fit.glm.default1 = glm(default ~ income + balance,Default,family="binomial")
|
||||
fit.glm.default1 = glm(default ~ income + balance,train.default1,family="binomial")
|
||||
summary(fit.glm.default1)
|
||||
fit.glm.default2 = glm(default ~ income + balance,train.default2,family="binomial")
|
||||
fit.glm.default1 = glm(default ~ income + balance,train.default1,family="binomial")
|
||||
fit.glm.default2 = glm(default ~ income + balance,train.default2,family="binomial")
|
||||
fit.glm.default1.prob = predict(fit.glm.default1,test.default1,type="response")
|
||||
fit.glm.default1.prob
|
||||
fit.glm.default1.pred = rep("No",nrow(test.default1))
|
||||
fit.glm.default1.pred
|
||||
fit.glm.default1.pred[fit.glm.default1.prob>0.5] = "Yes"
|
||||
fit.glm.default1.pred
|
||||
fit.glm.default1.pred[fit.glm.default1.prob>0.5] = "Yes"fit.glm.default1.pred = rep("No",nrow(test.default1))
|
||||
table(fit.glm.default1.pred,test.default1)
|
||||
fit.glm.default1.pred[fit.glm.default1.prob>0.5] = "Yes"
|
||||
fit.glm.default1.pred
|
||||
test.default1
|
||||
length(test.default1$student)
|
||||
length(fit.glm.default1.pred)
|
||||
table(fit.glm.default1.pred,test.default1$default)
|
||||
1 - (3851+44)/(3851+90+14+44)
|
||||
fit.glm.default2.prob = predict(fit.glm.default2,test.default2,type="response")
|
||||
fit.glm.default2.pred = rep("No",nrow(test.default2))
|
||||
fit.glm.default2.pred[fit.glm.default2.prob > 0.5] = "Yes"
|
||||
table(fit.glm.default.pred,test.default2$default)
|
||||
table(fit.glm.default2.pred,test.default2$default)
|
||||
1 - (4818+52)/(4818+106+23+52)
|
||||
summary(fit.glm.default2)
|
||||
summary(fit.glm.default1)
|
||||
summary(fit.glm.default2)
|
||||
coefficients(fit.glm.default1)
|
||||
fit.glm.default1$coefficients
|
||||
fit.glm.default1$coefficients[1,2]
|
||||
fit.glm.default1$coefficients[1:2]
|
||||
fit.glm.default1$coefficients[2:3]
|
||||
boot.fn = function(Default,index){
|
||||
model = glm(default ~ income + balance,Default,family="binomial",subset=index)
|
||||
fit.glm.default1$coefficients[2:3]
|
||||
}
|
||||
boot.fn(Default,c(14,5,79))
|
||||
boot.fn(Default,c(14,5,79,324,6435,234))
|
||||
boot.fn(Default,seq(15:7000))
|
||||
boot.fn = function(Default,index){
|
||||
model = glm(default ~ income + balance,Default,family="binomial",subset=index)
|
||||
model$coefficients[2:3]
|
||||
}
|
||||
boot.fn = function(Default,index){
|
||||
boot.fn = function(Default,index){
|
||||
model = glm(default ~ income + balance,Default,family="binomial",subset=index)
|
||||
model$coefficients[2:3]
|
||||
}
|
||||
boot.fn(Default,seq(15:7000))
|
||||
boot.fn(Default,seq(15:3000))
|
||||
boot.fn(Default,seq(15:3050))
|
||||
boot.fn(Default,seq(15:3500))
|
||||
set.seed(56)
|
||||
?boot
|
||||
boot(Default,boot.fn,c(1:1000))
|
||||
?boot
|
||||
boot(Default,boot.fn,1000)
|
||||
4.68/7.06
|
||||
2.32/3.232
|
||||
q()
|
||||
140
hw4/answers
Normal file
140
hw4/answers
Normal file
@ -0,0 +1,140 @@
|
||||
We now review k-fold cross-validation.
|
||||
|
||||
──────────────────────────────────────────────────────────────────────────
|
||||
(a) Explain how k-fold cross-validation is implemented.
|
||||
|
||||
The data are split into k groups of about equal size. The
|
||||
first group is set aside to be used as a validation set. The
|
||||
model is trained on the remaining sets, and an MSE is
|
||||
computed using the validation set. This process is repeated
|
||||
using the next set as a validation set, producing a second
|
||||
MSE. The process is performed on each of the k groups the
|
||||
data were split into, producing k MSEs, one for each
|
||||
validation set. Given a good value of k, this mean squared
|
||||
error is a good approximation for the minimum point along
|
||||
the flexibility axis that allows us to judge the appropriate
|
||||
flexibility to model some data, i.e., to estimate the right
|
||||
bias-variance tradeoff for a particular model.
|
||||
|
||||
──────────────────────────────────────────────────────────────────────────
|
||||
(b) What are the advantages and disadvantages of k-fold cross-
|
||||
validation relative to:
|
||||
|
||||
i. The validation set approach?
|
||||
|
||||
The validation set approach is basically k-fold with k=2.
|
||||
This gives a biased measure of the test error rate. However,
|
||||
it's very quick to compute. Of course, the MSE here will
|
||||
have no variance because we are only taking a single MSE
|
||||
from a single test set: so this gives large bias of the MSE
|
||||
but no variance.
|
||||
|
||||
ii. LOOCV?
|
||||
|
||||
LOOCV is on the other end of the spectrum from this. It
|
||||
gives a very unbiased MSE for each validation set because
|
||||
each validation set is a single datum and is being tested
|
||||
against a nearly-full training set, compared to the overall
|
||||
data set. However, the MSEs will be highly-correlated
|
||||
because the training sets are almost completely the same,
|
||||
and this guarantees a high variance.
|
||||
|
||||
K-fold cross-validation occupies the space in-between, where
|
||||
it has some bias and some variance of the MSE. Empirically,
|
||||
we know that k=5 and k=10 are two values that often work
|
||||
well to provide good estimates of these MSEs, because they
|
||||
provide balanced bias and variance.
|
||||
|
||||
|
||||
|
||||
|
||||
6. We continue to consider the use of a logistic regression model to
|
||||
predict the probability of default using income and balance on
|
||||
the Default data set. In particular, we will now compute
|
||||
estimates for the standard errors of the income and balance
|
||||
logistic regression co- efficients in two different ways: (1)
|
||||
using the bootstrap, and (2) using the standard formula for
|
||||
computing the standard errors in the glm() function. Do not
|
||||
forget to set a random seed before beginning your analysis.
|
||||
|
||||
──────────────────────────────────────────────────────────────────────────
|
||||
(a) Using the summary() and glm() functions, determine the esti-
|
||||
mated standard errors for the coefficients associated with
|
||||
income and balance in a multiple logistic regression model that
|
||||
uses both predictors.
|
||||
|
||||
|
||||
10,000 entries in the table, so testing 2 ways to separate the data,
|
||||
|
||||
> train.default1 = Default[1:6001,]
|
||||
> train.default2 = Default[1:5001,]
|
||||
> test.default1 = Default[6002:10000,]
|
||||
> test.default2 = Default[5002:10000,]
|
||||
|
||||
> fit.glm.default1 = glm(default ~ income + balance,train.default1, family="binomial")
|
||||
> fit.glm.default2 = glm(default ~ income + balance,train.default2, family="binomial")
|
||||
|
||||
*** from summary(fit.glm.default1), we get std. error
|
||||
|
||||
Coefficients:
|
||||
Estimate Std. Error
|
||||
(Intercept) -1.156e+01 5.622e-01
|
||||
income 2.237e-05 6.491e-06
|
||||
balance 5.649e-03 2.916e-04
|
||||
|
||||
|
||||
*** from summary(fit.glm.default2), we get std. error
|
||||
|
||||
Coefficients:
|
||||
Estimate Std. Error
|
||||
(Intercept) -1.198e+01 6.279e-01
|
||||
income 2.885e-05 7.060e-06
|
||||
balance 5.822e-03 3.232e-04
|
||||
|
||||
──────────────────────────────────────────────────────────────────────────
|
||||
(b) Write a function, boot.fn() , that takes as input the\
|
||||
Default data set as well as an index of the observations, and
|
||||
that outputs the coefficient estimates for income and balance in
|
||||
the multiple logistic regression model.
|
||||
|
||||
boot.fn = function(Default,index){
|
||||
model = glm(default ~ income + balance,Default,family="binomial",subset=index)
|
||||
model$coefficients[2:3]
|
||||
}
|
||||
|
||||
──────────────────────────────────────────────────────────────────────────
|
||||
(c) Use the boot() function together with your boot.fn()
|
||||
function to estimate the standard errors of the logistic
|
||||
regression coefficients for income and balance .
|
||||
|
||||
> set.seed(56)
|
||||
> boot(Default,boot.fn,1000)
|
||||
|
||||
Bootstrap Statistics :
|
||||
original bias std. error
|
||||
t1* 2.080898e-05 1.436086e-07 4.679106e-06
|
||||
t2* 5.647103e-03 1.876364e-05 2.320547e-04
|
||||
|
||||
***
|
||||
The std. error in t1 above is the std. error for the income
|
||||
coefficient, and under t2, the std. error for the balance
|
||||
coefficient.
|
||||
|
||||
──────────────────────────────────────────────────────────────────────────
|
||||
(d) Comment on the estimated standard errors obtained using the
|
||||
glm() function and using your bootstrap function.
|
||||
|
||||
|
||||
The bootstrap method uses random sampling to approximate
|
||||
sampling a real response. This gives a stronger σ²
|
||||
(variance) estimate than the formulaic approach used to
|
||||
calculate the std err for a linear regression model. The std
|
||||
err estimates from the bootstrap method are therefore more
|
||||
reliable than those computed earlier in this exercise.
|
||||
|
||||
The std err from bootstrap for the income coefficient is
|
||||
about 66% of that computed from the linear regression model
|
||||
directly. This is 72% for the coefficient of balance. These
|
||||
smaller values should be considered reliable, that is, we
|
||||
can believe the smaller errors compared to the standard
|
||||
meothods, because of the reason I explained above.
|
||||
BIN
hw4/answers.pdf
Normal file
BIN
hw4/answers.pdf
Normal file
Binary file not shown.
2793
hw4/answers.ps
Normal file
2793
hw4/answers.ps
Normal file
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Load Diff
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Reference in New Issue
Block a user