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Lecture Notes: Introduction to MATLAB Functions - Spring 2009 (Math/CS 375) - Prof. Richar, Study notes of Computer Science

Lecture notes for math/cs 375 spring 2009, covering the introduction to matlab functions. Topics include the for statement, if statement, while statement, and break statement. Several examples of functions are given, along with discussions on function handles, anonymous functions, and subfunctions. Varargin and varargout structures are also introduced.

Typology: Study notes

Pre 2010

Uploaded on 07/23/2009

koofers-user-wag
koofers-user-wag 🇺🇸

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Download Lecture Notes: Introduction to MATLAB Functions - Spring 2009 (Math/CS 375) - Prof. Richar and more Study notes Computer Science in PDF only on Docsity! Math/CS 375 Spring 2009 Lecture 1e Introduction to MATLAB: Functions Ref: Appendix B Function: maxentry(A) function y = maxentry(A) % MAXENTRY Largest absolute value of matrix entries. % For vectors, MAX(X) is the largest element in X. % For matrices, MAX(X) is a row vector containing the % maximum element from each column. y = max(max(abs(A))); >> A = [1,2;3,4]; >>maxentry(1:10) >> maxentry(A) ans = ans = 10 4 marks (cont.) >> x_sort= marks(x) x_sort = 39 61 73 79 92 [x_sort, x_mean]=marks(x) x_sort = 39 61 73 79 9 x_mean = 68.8000 >>[x_sort, x_mean, x_med,x_std] =marks(x) x_sort = 39 61 73 79 92 x_mean = 68.8000 x_med = 73 x_std = 20.0549 if nargout > 2, x_med = median(x); end if nargout > 3, x_std = std(x); end >> x = [61,79,92,73,39]; >>x = 61 79 92 73 39 Function: ExpSum (Approximation to Exp(x)) function [sum,err,n] = ExpSum(x,maxn) % ExpSum computes an approximation to e^x, accurate to % machine precision, using its Taylor series. ExpSum returns % the approximation, sum, the relative error in the approximation, % err, and the required number of terms, needed to compute it, n. sum = 1; term = 1; n = 1; while abs(term) > eps*abs(sum) & n <= maxn term = term*(x/n); sum = sum + term; n = n + 1; end err = abs((sum-exp(x))/exp(x)); ExpSum (cont.) >> [sum,err,n] ExpSum(10,100) sum = 2.2026e+004 err = 1.6516e-016 n = 47 >> >> [sum,err,n] = ExpSum(-10,100) sum = 4.5400e-005 err = 2.0283e-009 n = 59 >> Why the difference in error between the approximation at x = 10 and x = -10? Subfunctions function w9plot % w9plot plots the functon w9(x), a component of the % error for polynomial interpolation using 9 points. % Define interpolation points and plot points, (xp,yp) x = [-4:+4]; xp = linspace(-4,4,100)'; yp = w9(xp); % Plot w9(x) along with the data points. plot(xp,yp,'g-',x,x,'r+'); title('Error function w9(x)'); xlabel('x'); ylabel('w9(x)'); % Subfunction w9(x) function z = w9(x) z = (x+4).*(x+3).*(x+2).*(x+1).*(x+0).*(x-1).*(x-2)... .*(x-3).*(x-4); w0d) Cont. Error function w(x) 6000 -—- T 1 1 T T 4000 3000 2000 1000 + -1000 -2000 -3000 -4000 -5000 1 1 1 nl 1 -4 -3 -2 -1 o 1 Varargout function varargout = moments(x) %MOMENTS Moments of a vector. % [m1,m2,...,mk] = MOMENTS(X) returns the first, % second, ..., k'th moments of the vector X, where % the j'th moment is SUM(X.^j)/LENGTH(X). for j = 1:nargout, varargout(j) = {sum(x.^j)/length(x)}; end >> m1 = moments(1:4) m1 = 2.5000 >> >> [m1,m2,m3] = moments(1:4) m1 = 2.5000 m2 = 7.5000 m3 = 25 >>
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