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Session 4: the polynomial class Constructing and manipulating objects

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7 July 2006S Programming in R2 Objectives The class must have objects that behave as, and can be manipulated as, mathematical polynomials in a natural way construction – structure is important arithmetic operations (+, -, *, ^n OK, but "/")? calculus – derivative and integral display – printing and plotting smooth morphing into functions when used as such other convenience features S3 class version only, at this stage.

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7 July 2006S Programming in R3 Constructor function polynomial <- local({ ### basic constructor function - polynomials are functions.clip <- function(p) { ## ancillary p <- as.numeric(p) if((j <- length(p)) == 0) return(0) while(j > 1 && p[j] == 0) j <- j - 1 p[1:j] }

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7 July 2006S Programming in R4 Constructor, cont'd function(coef = c(0,1), rat = all(coef % 1 == 0)) { val <- local({ coef <-.clip(coef).Coef <- rev(coef) rat <- rat function(x) { p <- rep(0, length(x)) for(a in.Coef) p <- a + x * p p } }) oldClass(val) <- "polynomial" val } })

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7 July 2006S Programming in R5 The "rat" flag A display feature, only "rat" flag is on, by default, if initially the coefficients are all whole numbers Operations on 'rat' polynomials (including whole numbers) exclusively pass on the flag to any polynomials generated from them A single non-rat operand turns off the rat flag (by default) All arithmetic nevertheless done in floating point – rational form only applies to display.

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7 July 2006S Programming in R6 Coercion to numeric > as.numeric function (x,...) UseMethod("as.double") Hence as.double.polynomial <- function(x,...) coef(x ) converts to class "numeric".

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7 July 2006S Programming in R7 Arithmetic operations Uses methods for a "group generic" function, 'Ops' (which does not exist as a function as all) Methods are functions of two variables, e1 and e2 Dispatch to polynomial occurs if either has class "polynomial" Several methods may be incorporated into one method function – another convenience Take code in stages

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7 July 2006S Programming in R8 Ops.polynomial <- function(e1, e2) { if(missing(e2)) return(switch(.Generic, "+" = e1, "-" = polynomial(- coef(e1),.getRat(e1)))) rat <- (if(class(e1) == "polynomial").getRat(e1) else all(e1 % 1 == 0)) && (if(class(e2) == "polynomial").getRat(e2) else all(e2 % 1 == 0)) e1 <- as.numeric(e1) e2 <- as.numeric(e2)

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7 July 2006S Programming in R9 e1.op.e2 <- switch(.Generic, "+" =, "-" = { if((j <- length(e1) - length(e2)) < 0) e1 <- c(e1, rep(0, -j)) else if(j > 0) e2 <- c(e2, rep(0, j)) NextMethod(.Generic) }, "*" =.poly.mult(e1, e2), "/" =, "%/%" =.poly.quo.rem(e1, e2)$quotient, "%" =.poly.quo.rem(e1, e2)$remainder, "^" = if(length(e2) > 1 || e2 < 0 || (e2 % 1) != 0) { stop("positive integer powers only") } else {

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7 July 2006S Programming in R10 switch(as.character(e2), "0" = 1, "1" = e1, { m <- e1 for(i in 2:e2) m <-.poly.mult(m, e1) m }) }, stop(.Generic, " is not supported for polynomials")) polynomial(e1.op.e2, rat = rat) }

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7 July 2006S Programming in R11 A brief example > x <- polynomial() > p <- 1 - (1-x)^5 > p 5*x - 10*x^2 + 10*x^3 - 5*x^4 + x^5 > dp <- derivative(p) > dp *x + 30*x^2 - 20*x^3 + 5*x^4 > ip <- integral(p) > ip 5/2*x^2 - 10/3*x^3 + 5/2*x^4 - x^5 + 1/6*x^6

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7 July 2006S Programming in R12 Plotting > plot(p) > lines(derivative(p), col = "red") > tangents(p, x = 0:3/2, col = "blue") > tangents(derivative(p), x = 0:3/2, col = "blue")

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7 July 2006S Programming in R13

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7 July 2006S Programming in R14 Polynomials and functions Objects are functions and behave as such To make an efficient version, use explicit conversion May be evaluated at numerical arguments OR May be evaluated at polynomial arguments Solves two problems: numerical evaluation and change of origin/scale Polynomial iteration is a by product, e.g. branching processes.

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7 July 2006S Programming in R15 Example > p 5*x - 10*x^2 + 10*x^3 - 5*x^4 + x^5 > p(1:4) [1] > p(-4:1) [1] > p(x-1) *x - 80*x^2 + 40*x^3 - 10*x^4 + x^5 > p(x+1) 1 + x^5

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7 July 2006S Programming in R16 The function > as.function(p) function (x) { p < x * 1 p < x * p p < x * p p <- 5 + x * p 0 + x * p }

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7 July 2006S Programming in R17 How is this coercion done? as.function.polynomial <- function(x,...) { b <- coef(x) p <- as.name("p") x <- as.name("x") b0 <- as.numeric(b) while ((an <- length(b0)) <= 1) b0 <- c(b, 0) statement <- call("{") statement[[i <- 2]] <- call("<-", p, call("+", b0[an-1], call("*", x, b[an]))) for (ai in rev(b0)[-(1:2)]) statement[[i <- i + 1]] <- call("<-", p, call("+", ai, call("*", x, p))) statement[[i]] <- statement[[i]][[3]] fun <- function(x) {} body(fun) <- statement fun }

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7 July 2006S Programming in R18 Extras Two classes of polynomial –polyInterp(x, y) – interpolation polynomial –polyZeros(z) – polynomial with specified zeros (roots) Finding roots: –solve(p, b = 0) – roots of p(x) = b(x) New generics –derivative(), integral(), "coef<-"() New methods for old generics: –as.character, plot, lines, points, as.double,...

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7 July 2006S Programming in R19 Final notes A personal exploratory example that has proved more useful (for real jobs) than expected (e.g. exploring polynomial regressions, Branching processes, &c) Shows the power and flexibility of the object oriented features of the S language With S4 classes there are some extra security features, (e.g. checking validity) but not very much Extending to polynomials in more than one variable looks not to be entirely straightforward in an elegant way – recursively defined objects are not well supported.

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