Computer Science in Practice

1. Computer Science in Practice • This course is an introduction to problems (and solutions) that arise in applied fields of computer science such as machine learning, computer vision and graphics. • We will mainly use tools of linear algebra.

2. Computer Science in Practice • Lecturer: Dani Lischinski (danix@cs.huji.ac.il) • TA: Amit Gruber Course email : csip@cs.huji.ac.ilPersonal email : amitg@cs.huji.ac.ilReception hour: Sunday 16:00-17:00 • Web site: www.cs.huji.ac.il/~csip

3. Requirements of the course • Exercises (every 2-3 weeks) • Exercises are submitted in pairs • Programming in Matlab or Octave • Experimental validation • Theoretical questions • A final exam

4. Matlab/Octave Basics • http://www.octave.org/doc/index.html • http://www.mathworks.com/access/helpdesk/help/techdoc/matlab_prog/exampleindex.html (M-file programming) • Use help

5. M-File Programming • M-files can be scripts or functions that accept input arguments and produce one or more outputs.Components of m files: • Function definition linefunction [out1 out2] = name(in1, in2, in3) • H1 line - a single comment line that follows the function definition line. % SQUARESUM compute the sum of the square of the matrix elementsit is the first text that appears when user write>> help function_name>> lookfor keyword display all functions where the keyword appeared in H1 line • Help Text - text block following the H1 line without any blank line in between the two • Function body – the Matlab code • Comments – lines starting with % (note – add short and clear comments to your code)

6. Operators • Arithmetic operators (numeric computations) • matrix arithmetic (linear algebra A*B) • array arithmetic (element by element A.*B) +, -, ./, .^,:.. • Relational operators (compare) • Compare corresponding elements of arrays of equal dimensions (<, >,<=, >=, ==, ~=) or an array to scalar • Logical operators can operate both on logical and numeric data (and: &, or: |, not: ~)true: logical 1 or non-zero numeric quantity false: logical 0 or numerical 0 logical functions : xor, any, all

7. Flow control • if, else, elseif, end • switch, case, otherwise, end • return • try,..catch • for i=start:increment:end, end • while, end • break (used with for or while) • continue (used with for or while) Try not to use

8. >> x = 0:k-1 >> ff = 5*sin(x/(2*pi)); Code optimization – vectorizing loops • Convert for / while loops to equivalent vector or matrix operations1D indexing >> for x = 1:k ff(x) = 5*sin((x-1)/(2*pi)); end

9. Code optimization – vectorizing loops • 2D indexingmeshgrid – convert rows vectors to arrays C and R that can be used for evaluating function with two variables >> for r = 1:10 >> for c = 1:10 >> b(r,c) = r^2+ c^2 >> end >> end >> [C, R] = meshgrid(1:c, 1:r) >> b = R.^2 + C.^2;

10. Code optimization – vectorizing loops • function B = repmat(A,M,N) • [m,n] = size(A); • mind = (1:m)’; • nind = (1:n)’; • mind = mind(:,ones(1,M)); • nind = nind(:,ones(1,N)); • B = A(mind,nind);

11. Code Optimization – Preallocating arrays • Simple way to improve code execution is to pre-allocate the size of the arrays in the program.The preallocation also help reduce memory fragmentation when working with large matrixes >> f = zeros(1024);

12. Cell arrays and Structures • Cell array is multidimensional array whose elements are copies of other arrays>> c = {‘gauss’,[1 0;0 1], 3}>> c{1} ans = gauss • Structures are similar to cell arrays (allow grouping of a collection of dissimilar data) but they addressed by fields rather than by numbers>> params.nimgs = 100; >> params.jump = 2; >> params.baseStr = ‘testImg’

13. A few more important commands (look in the help) • find • repmat • reshape • clear • save • plot, subplot • disp

14. Exercise 1 • Find the first eigenvector and eigenvalue of a matrix using the power method. • Targets: • Getting familiar with Octave • Understanding the Power Mehtod • Designing and performing experiments