PolynomialRegression - fdymmxauc Eiméggma TV(7...

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Unformatted text preview: fdymmxauc .. Eiméggma, TV (7 if? \AJLQWQ \Jf? g §r+ 671 (M, v \f{ [.w6+6%3 QQ a. gym/VT L/V’é’ TLH} ("(7 ma €K“}Kw§/'0m (:3? [ff—WHY gflwwsa >/7-‘ 40+€m<+42xa fanrame‘ +69 Légwmw +0 m w 01m xv (f (7 91/ £6?: gcyf 40"?“ ’WQXf’ “”flijT U15 U96 ‘Hé (WW/”<37 WYOKKva/rf legfitr Kg) Mgémam 3 2:51— , ’Qégy, ’40 ”47.x! «42x11 ”an,m\ 9&0 fa? / gage? g3 @10ng +4; ZXf+Mqflm§XW13 g\ 3 fl0£><f + 4! éXf+ 9°©+amfxf O fXI'yIa WM 55? ,2‘ i X” y}. évrw (Qmm l/ M »§ 2» 7r V' / V w M 6+“ \NLM \m «9 me 590% a? m VQIVMvar'ceLWC m m we, Wm 6Q am W1 6% Qk’l’ :9? 9M) QYQKV FAY 4'0 Jflfi 994a q]: angqgof 43:1,CE69?' 9& fl? (6"??57JF gfwww gl/WDYW {TC fgflwafi'fi“ :"§ 4 .. :2 , fi .fi \/« mam x at gags @27ij M my S {M 5+ 1‘ é(%mw>/-)Qr 0/0“?)94” ()4 .. flaijr My + ()3 (>732 T262 :1 2 . : (3%!“ (25.9392) +(Z7w 232333) in” +— M», 2.s.,wa)2: 25"! 3,511ij :3 Q a»: :0: ~ » «do» I» 2 T. (391 , 203 gay/(mi QESCfflwaz‘rQwrmS?) 4, QVLrM’fiBQ‘Jr (797 « (,wwmf’i 293592305 ’ a + “9+(é/‘I ._ Z¢%60)[5)2fl Q éMQWB) -- 578.90%?) “~./ 70 .0. f(w) 60 40 20 W +gx, a + gxmg » 62/, éthio+£><hWB + fiXH‘Xng: éxzi’yf’ Q EXQFWOJFgXH'XQMI +£X2f 42 : fngX; \Je Ina/we. " 6 Mo S m» a0 “5% /&S 75:25 #g a. ;:2¢%5 [q Q4; 3% ”a 200 \/§€ 4:31» 99‘ * é/I'W’M‘LVW +67 gm?) @:;5 fl}~;: Cr V522 3' "—3 y:[email protected]%.ngg \,/’ i:= 0,1..400 x2: ~10,—9.9.. 10 f(x) := (x + 5)(x — 1)(x — 7) f(x):=x3—3-x2—33-x+ 35 g(x) := f(x) + md(100) — so i v‘:= ———-10 x] [20 J v-'— i 10 Y1- g 20 r := regress (vx, vy , 3) ff(x) := interp(r,vx, vy ,x) 3 3 3 r = 30.628 —3 l .94 —2.922 0.979 Linear regression 600 .417.084. 400 200 fix) Wi " 200 ' 400 ‘ 600 .:- 941.3193 1000'10 '8 ‘6 '4 ‘2 0 2 4 6 8 10 .— to. x, vxi .10. 2nd order regression 600 317.0844 400 200 Hit) “’1' ' 200 " 400 ' 600 .— 941.319., 1000—10 -s ‘6 —4 -2 o 2 4 6 s 10 :10. x, vxi \‘J" 9 .411084. 317.084, |. 600 400 200 ff X) '200 ' 400 “ 600 ' 800 941.319: 1000_ 10 I-_ 10a 600 400 200 EX) Wi "200 " 400 "‘ 600 " 800 941.319., ”)0qu -10 L a '8 '8 3rd order regression '4 '2 0 x,vxi 4th order regression The function that generated the 400 points. y = x3 — 3x2 — 33x + 35 + RandomUniform(-SO,50) The linear regression pl = 27.069x — 67.256 The 2nd order polynomial regression p2 = —2.922x2 + 27.069x + 30.628 The 3rd order polynomial regression p3 = .979x3 — 2.922):2 — 31.94x + 30.628 The 4th order polynomial regression p4 = .0016964 + .9791:3 — 3.06x2 - 31.94.76 + 32.013 The 5th order polynomial regression p5 = .0002118x5 +0016):4 +.955x3 —3.06x2 —31.431x+32.013 ...
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