# Learning Clojure

## Wednesday, February 16, 2011

### Clojure Dojo 3: From Heron to Newton-Raphson

```
;; We've so far written a square root solver using Heron's method

(defn average [a b]
(/ (+ a b) 2))

(defn abs[x]
(if (< x 0) (- x) x))

(defn make-good-enough? [n]
(fn [guess] (< (abs (- n (* guess guess))) 1e-6)))

(defn make-improver [n]
(fn [guess] (average guess (/ n guess))))

(defn iterative-improve [x improve good?]
(if (good? x) x
(iterative-improve (improve x) improve good?)))

(defn square-root [n]
(iterative-improve 1.0 (make-improver n) (make-good-enough? n)))

;; Now in fact, Heron's method turns out to be only a simple example of a more general method of root finding known as Newton-Raphson.
;; Their great idea was to use the derivative of a function to help find its roots.

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Finding Derivatives (by cheating)
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

;; Suppose we've got a function, say cube

(defn cube [x] (* x x x))

;; And we want to find out what its derivative is, i.e. how much it changes when you change the argument.

(cube 4)        ; i s 64
(cube 4.000001) ; is 64.000048000012

;; In other words, if we add 0.000001 to x, then (cube x) goes up by 0.00004800..... which is about 48 times as much.

;; We say that the derivative of cube at 4 is (about) 48.

;; It's different in different places.

(cube 3)        ; is 27
(cube 3.000001) ; is 27.000027000009005

;; So the rate of change of cube at 3 is (about) 27.

;;In general, we want a function that takes a function, and gives back a function that tells us how much it changes if you add a tiny bit to its argument.

(defn deriv [f]
(fn [x] (/
(-
(f (+ x 0.000001))
(f x))
0.000001)))

;; Let's try that out:

((deriv cube) 4)       48.00001200067072
((deriv cube) 3)       27.00000900546229

;; Notice what we did here! This is a function which takes a function and gives back a function.

;; We could name the answer!
(def dcube (deriv cube))

(map dcube (range 10))        ;(1.0E-12 3.0000029997978572 12.000006002210739 27.00000900546229 48.00001200067072 75.00001501625775 108.00001800248538 147.00002100198617 192.00002384422987 243.0000268986987)

;; So now we know how to calculate (a good approximation to) the derivative of any function.

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Newton Raphson
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

;; Imagine that we wanted to find the cube root of 100.

;; That's the same as finding a number which, when you cube it and subtract 100, gives you zero

;; So we should take as our equation to solve: (cube x) - 100 = 0

;; or (f x) = 0 where f is

(defn f[x] (- (* x x x) 100))

;; Isaac Newton told us that if you have an equation like that, and you have a guess at a number
;; which will make it zero, then you can find a much better guess if you know the derivative.

(def df (deriv f))

;; Newton said: Suppose that I guess that 4 is the cube root of 100.

;; Then I try it (f 4) = -36

;; That's not so close. What's the derivative there?

(df 4) 48.00001200067072

;; What that means is that when we raise x by a tiny amount, say 4.0000001, then (f x) goes up by about 48 times that.

;; So Newton tells us:

;; The function is too low by 36.
;; If we make the guess a bit larger, then the function will go up by ~ 48 times as much.
;; We should try adding 36/48 as our next guess.

;; Let's try

(+ 4.0 (/ 36 48)) ;4.75

(f 4.75)          ;7.171875

;; ok, better. We were under by 36, now we're over by 7. 4.75 is too high!

;; Take the derivative again:

(df 4.75) ;67.68751426022845

;;Divide the amount we're off by the derivative
(/ (f 4.75) (df 4.75)) ;0.10595565634789487

;; So our next guess should be (- 4.75 0.10595565634789487) , which is 4.644044343652105

;; Let's try 4.644 :

(f 4.644) ;0.15592198400001678 ...homing in...

(- 4.644 (/ (f 4.644) (df 4.644)))  ;4.641590085793267

(f 4.64159) ;7.538717167676623E-5 ...very good...

;; Obviously we've got another guess-improving function here

(defn improve-cbrt100 [guess]
(- guess (/ (f guess) (df guess))))

(improve-cbrt100 4.0)     4.749999812489567
(improve-cbrt100 (improve-cbrt100 4.0)) 4.644044335287887

(take 10 (iterate improve-cbrt100 4.0))   ; (4.0 4.749999812489567 4.644044335287887 4.6415901322397985 4.641588833613422 4.641588833612778 4.641588833612778 4.641588833612778 4.641588833612778 4.641588833612778)

;; Let's try 4.641588833612778, which seems to be about as good as floating point arithmetic can get us.

(f 4.641588833612778)      ; -2.8421709430404007E-14

(cube 4.641588833612778)   ; 99.99999999999997

;; Pretty good for only 5 steps!

;; What about a good-enough function to tell us when to stop iterating?

(defn good-enough-cbrt100? [x]
(< (abs (f x)) 0.0000001))

;; Now we can plug the improver and the good enough function into iterative-improve, as before. We'll use 1.0 as our initial guess again.
(iterative-improve 1.0 improve-cbrt100 good-enough-cbrt100?) ;; 4.641588833613406

(cube 4.641588833613406)   ;; 100.00000000004056

;; But of course, nothing we did depended on (f x) being (- (cube x) 100)
;; We can make a guess-improver for any function

(defn make-improver [f]
(fn [guess] (- guess (/ (f guess) ((deriv f) guess)))))

;; and a function to tell us whether it's good enough

(defn make-good-enough [f]
(fn [guess] (< (abs (f guess)) 0.0000001)))

;; What if we'd like to solve the equation x^3 + x^2 + x + 1 = 0 ?

(defn solve [f guess]
(iterative-improve guess (make-improver f) (make-good-enough f)))

(solve (fn [x] (+ (* x x x) (* x x) x 1)) 1.0) ;  -1.0000000235152005

;; Let's see how good an answer that is.

((fn [x] (+ (* x x x) (* x x) x 1)) -1.0000000235152005) ; -4.703040201725628E-8

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Newton Raphson Solver
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

;; Here's the whole of our general function solver, but let's make the two 'magic numbers' into variables too.

(defn deriv [f dx]
(fn [x] (/  (- (f (+ x dx)) (f x) )  dx)))

(defn make-improver [f dx]
(fn [guess] (- guess (/ (f guess) ((deriv f dx) guess)))))

(defn make-good-enough [f tolerance]
(fn [guess] (< (abs (f guess)) tolerance)))

(defn iterative-improve [x improve good?]
(if (good? x) x
(iterative-improve (improve x) improve good?)))

(defn solve [f guess dx tolerance]
(iterative-improve guess (make-improver f dx) (make-good-enough f tolerance)))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Some applications
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

;;usually we'll want an initial guess of 1.0, a tolerance of 0.0000001 and a dx of 0.0000001
(defn solve-it [f]
(solve f 1.0 0.000001 0.000001))

;; Square roots are the solutions of (- (* x x) n) = 0

(defn sqrt [n]
(solve-it
(fn [x] (- (* x x) n))))

(sqrt 2)

;; Cube roots are the solutions of (- (* x x x) n) = 0

(defn cubert [n]
(solve-it
(fn[x] (- (* x x x) n))))

(cubert 2)

;; General positive integer powers can be expressed recursively:

(defn pow [x m]
(if (= m 0) 1
(* x (pow x (- m 1)))))

(pow 2 3) ;8

;; And if we can express them, we can use our method to find mth roots.

(defn mthrt [m n]
(solve-it
(fn[x] (- (pow x m) n))))

(mthrt 4 10000) ; 10.0
(mthrt 123 234) ; 1.045350466874579

(pow 1.045350466874579 123) ;; 234.0000000243792

;; But of course, we can solve any equation which is amenable to Newton's method.
;; What number, when multiplied by itself 5 times, and added to its cube, is equal to 700?

(solve-it (fn[x] (+ (* x x x x x) (* x x x) (- 700))))         ; 3.653807138770812

((fn[x] (+ (* x x x x x) (* x x x))) 3.653807138770812) ; 700.0000003041337

;; And if we'd like a better answer, we can tighten the tolerances

(solve (fn[x] (+ (* x x x x x) (* x x x) (- 700))) 1.0 0.0001 0.0000000001) ; 3.6538071384442974

((fn[x] (+ (* x x x x x) (* x x x))) 3.6538071384442974) ;  700.0000000000817

;; In theory, we'd be able to use exact arithmetic to get arbitrarily accurate answers.
;; Unfortunately, there's a problem:

(def it (make-improver (fn[x] (+ (* x x x x x) (* x x x) (- 700))) 1/1000000)) ; let's use an exact number for the derivative approximation

(it 1)                  ; 706000013000011000005000001/8000013000011000005000001
(it (it 1))             ; 701635321996143769309418549344377227910245191235491382603948489939699061403896688219959595828904782223381164841011935230760200930031084004228000441000030000001/9938320550977024134920249980653903204519665284470123223646489755187942893061019955878983167997749088761731121640594804980166030029688004228000441000030000001

(it (it (it 1)))        ; 680237917441885524336418138840317390387477847859858876595840234272149425820646224687954143292874376454521646334951672503154114880181300843385953617515081285781202235479686701822851428206060076054501437170597117295185786622448092483300621863099620331252416621986363001610323019591307968502301991515214451683404838437583955509995832608492690263029459069929976980341191436712932448855080982518511841862770021155980758185192008036141486759432138585947862423162360655529584841649322416573707879742014567586254212003318865179086757633089843219756812844011861068141391297639719428726706532258163565342026653477095733524189143027946838723155848407864793520497990564324241350635343653518356286709120797176348156180962741126448716721627967605789740379911361208362221973935469409915879343921377079168493057613428011966000155000001/12044286332140129752156903683583126447244938902908926261607579552042467607850219073475448382767207875539137993119265514088232278316554495937166615076210884321449749330293196083213705419806298445939325718843197812663753008605821508903073125871316024961234609794639656768170967982087039663880189123415565181043003755509161080647377737131911463013608843269269085395872146930064532858794789612245264182978492519500766787287053880913766446555513734299742826803443919934807497308923876636662216418140448109767596616697192628702623509472332595967524822599329261416093615390398256303064181652715052013431614546617380753560488219680455031256525883523608904103537508019124887595921116604264464741508409082509232101707795378534258208496424946224563887570504744604783840489200142804159760458106765068490963613428011966000155000001
(it (it (it (it 1))))   ; 582683064888886254627237413128914355133205070107730345306566818667169839077043977063385150567627868898954627396888803873298542915735291068231854737660749871284970969691761755237023440170586132711096078205774661890184691373273510273283202351760392701768411497903999874829223861118399238720882465581354352630000971969184626801423877016182068328118391265021597001152296290812122250720956031547021906861678543752400823983366892551426008467510890046723277161664312028285174276774217375277065253742530859064530016440400316790801769530544371284081707010989839631883556283241113998175234840943170722268328720701199347867588322060049770935643979286527454236006759544836737208537749694757872416290454093930058675287658912516799102545283953465977040744182800487171767210596289815719837983663631950117171893161135122746529146479448859227394441049923190055342857801930221659120398700765917475396756356933748228771216329690971598121024906393909199740001655437226675292089740865401850681717862593295044249444227096530596676969537355119944076825364494231692463504463385356777816358752007019352340833078969061391374565063342370784189339508167803312120387247644380450529051088582048118902223778854166538058459895429188150443034952695968773967230092326545545925098065274390529306120532407597280407465373342806740914557404279803404281875098412568052206856189504731510965158306039056809060766012400914292618679128113015139253847973084750652345634913827827043851034605686643123187076375915598639958363937697829956243245104853978610467265918061529051311071288413580756443693449482971629647374397753310557728689142976074021187362385338932147567642210185904671961035565315840293902065425141448705637965586698203041117408261444705945865047531954486514780505078832025358426644461672866572253641788067682868313500867194340509941792198538459647737009443599755837217723308620719398896201880698984796565219264280137596609642845400547125754456209756196169964659473873586279764214453671645956003619308063592648337612774637171241884690676604002029522916589191903605327335495103912144305652194435971484948567480613108531306049822517644426959900990535402937677569195079220088350105434599994075059763835418492356744894787315782423704801923419774013400657508864679447262609755156219146757585536473633170206959340454211578712959760853352548696411404380901268846622462357056396277653709161131992015278410666198586576834004214031859566710336893361077448333057320403538099709665120727817043295276197596679826722665947871368777705493013687853852488123574246109063974638016285172124627788918704893034469219280568313768497634816004319733836637800496141268248807282891107795078010343054429751145414810367778479411850027960387501665140659520089944949565082934980430105203072555206594274562501052867096198848952152330124216130716659045181152784334507386057959224529758674635647399911089583876402241119812642070687710563501331168402312038757841360479166547376873053789334438351535437225196907596309808445013472858435271020713482531473249425954713138213993853690048279402486411516169465400907740591254918376665681348518024822387460412485718766760961958943780464278973812982576607564820484862661878406223946061213224303753767486290224002094709777464746472645414893385625996401380435392614667079096134453611792569645198318837308794387253480934803497220222891193495355138449277282306273087395040974247699578508944806995861199096983093778899088019827135297023837526798957092191839173362464128894165682017815738236599955940542605196688759536725013516191327597134380086354086023426735965448551045630879574037331690144148829216502025494635660367861837832650027912461998002111883455796634997105423180569440930861025594379890711775754266620157029760796036618547810092105837604672377921726136778076482261042587195382143724289727648562704136017853509498366890447377518135820019350488687720760940855188770925017427141691614329578171795155245502836823003566045473401512439769020488822239922252814521793930372045109677392922267054560823873124298362347797278136358895228352710628394677753104291145730759681035568127201415728926581298819651031969039124011747083909428303966000780000001/1289662645360006949369861000746257407436001280319251755065265332128845870443762814781235123809605857088064680964268489185006699939534405103604808409219656501996684569460322604339269283183240847085392898019358174725224320046810336117686066734234603375563939176779259583402668582130141781231300132471492379817106912569286575980909374013278684446082271715110018487411302624334211373773301777350530776470096106959578999600954818163227120978733840520341747057024362410829665648464180353713401663822320451218699907187855404695868248817490221638319371798614353218983104429230851984212687326931385356087624713397845835357448085131275963168435888903290008464274549628872588874307396967713789655882960359226223080252269573941974438901848706227554859815393541356523886747205159791816357590009481137793145730800287039814782844746646685646562878266040482945511076734668650719343995161218170979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;; after 4 iterations we've got 8000 digits in our fraction! It might take a while to calculate each step.

;; It's possible to control this crazed exponentiation of digits.
;; I leave the problem of modifying the method to produce arbitrarily accurate answers as a problem to the reader.```