Introduction to MATLAB

Transcription

1 Introduction to MATLAB Chen Huang Computer Science and Engineering SUNY at Buffalo

2 What is MATLAB? MATLAB (stands for matrix laboratory ) It is a language and an environment for technical computing Designed to handle large data sets and matrix operations easily and efficiently. It s a high-level language optimized for matrix and vector operations The working environment contains a number of tools for managing programs, debugging and more

3 Why use Matlab? Advantages: Handles vector and matrices very nice Quick plotting and analysis EXTENSIVE documentation (online or offline) Lots of nice functions (organized within Matlab toolbox, such as image processing, neural networks and wavelets) Disadvantage: It can be slow, especially when bad programming practices are applied.

4 Where to access Bell 101 (Linux) Library PC CSE department UNIX Matlab s m-files are platform independent

5 Desktop Tools Command Window type commands Workspace view program variables double click on a variable to see it in the Array Editor Command History view past commands Launch Pad access tools, demos and documentation

6 Basic navigation and interaction Matlab is interactive and you do not have to declare variables Semicolon suppress echo Up-arrow and down-arrow keys Ctrl-c interrupts processing

7 MATLAB files M-files Scripts Functions Mat-files Data Matlab s m-files are platform independent

8 M-files: script File containing sequence of Matlab commands to perform an action Invoke from within MATLAB by typing the name of the file, without extensioin Create files with Matlab editor (edit) Start with a comment line % (same as // comment in C, Java) No /* block comments */

9 M-file: function Funtion m-files have local variables and always start with function function y = myfunction(a, b) y = a + b; The file is saved with the function name and the usual Matlab script file extension, ".m". (the above example saved as myfunction.m ) A MATLAB function may be called from the command line or from any other M-file.

10 Scalars, vectors and matrices Defining a scalar Defining a vector Enclose in square brackets and separate elements by spaces or commas Or using syntax: init : increment : terminator, for example v = 1:2:9 >> v = Defining a matrix Separate rows by semicolons Can also use matrices / vectors as elements

11 Some useful constants Inf infinity (>>2^10000) NaN Not a number (>>0/0) ans most recent unassigned answer pi i and j sqrt(-1) (imaginary unit)

12 Common matrices to use ones(a,b) axb of all ones zeros(a,b) axb of all zeros eye(a,b) axb identity rand(a,b) axb matrix of uniformly distributed random numbers on (0, 1) linspace(a,b,n) (also see logspace) linear spacing from a to b, with n spacings x = 1:10 linear spacing from 1 to 10, counting by 1

13 Element access MATLAB IS NOT ZERO INDEXED! x retrieves entire matrix x x(1,2) retrieves element at row 1, col 2 x(2,:) retrieves row 2, all columns x(1:2,5:10) retrieves row 1 to 2, col 5 to 10 Some useful functions: length(x) length of vector x (cols) size(x) rows, cols of x

14 Operators Relational ==, >, <, >=, <=, ~= &(and), (or), ~(not) Arithmetic +, -, *, /, ^, element by element :.*,./,.^

15 Flow control The if-elseif-else statement: if (condition) <commands> elseif (condition) <commands> else <commands> end The while - loop: while (condition) <commands> end

16 Flow control (cont.) The for loop: switch statement: for x = <vector> end <commands> switch <expression> case <condition>, <statement> otherwise <condition>, <statement> end

17 Plotting 2D graphing plot(x,y) Example: x = 0 : pi/100 : 2*pi; y = sin(x); plot(x, y) Multiple graphs y2=sin(t+pi/2); plot(t, y, t, y2)

18 More plotting Old plot got overwritten To open a new graph, type figure Multiple data sets: Type hold on to add new plot to current graph Type hold off to resume overwriting Edit your figures: title( Sine function ) xtitle( x = 0 ~ 2*pi ) ytitle( Sine of x ) Multiple plots in one figure: subplot()

19 Probability Distributions Binomial Distribution: Probability Density Function : Y = binopdf (X,N,P) returns the binomial probability density function with parameters N and P at the values in X. Random Number Generator: R = binornd (N,P,MM,NN) returns n MM-by-NN matrix of random numbers chosen from a binomial distribution with parameters N and P. Gamma Distribution: Probability Density Function : Y = gampdf (X,A,B) returns the gamma probability density function with parameters A and B, at the values in X. Random Number Generator: R = gamrnd (A,B) returns a matrix of random numbers chosen from the gamma distribution with parameters A and B.

20 Probability Distributions Beta Distribution Probability Density Function : Y = betapdf (X,A,B) returns the beta probability density function with parameters A and B at the values in X. Random Number Generator: R = betarnd (A,B) returns a matrix of random numbers chosen from the beta distribution with parameters A and B. Gaussian Distribution Probability Density Function : Y = mvnpdf (X,MU,SIGMA) returns the density of the multivariate Gaussian distribution with mean MU and covariance SIGMA. Random Number Generator: R = mvnrnd (MU,SIGMA) returns an n-by-d matrix R of random vectors chosen from the multivariate Gaussian distribution with mean vector MU, and covariance matrix SIGMA.

21 Online reference Getting started with MATLAB doc/learn_matlab/