Transcript Slide 1
MATLAB Introduction
• Main Features – Simple programming rules – Extended accuracy – Continuity among integer, real and complex values – Comprehensive mathematical library – Extensive graphics tools – Linkages with other languages – Transportability across environment – MATLAB scripts will work on PC, UNIX, Mac
• Typical uses include: – Math and computation – Algorithm development – Modelling, simulation and prototyping – Data analysis, exploration and visualization – Scientific and engineering graphics – Application development, including Graphical User Interface (GUI) building
The Advantages of MATLAB
• • • • • • • • • Ease of Use. Platform Independence Predefined Function. Device-Independent Plotting. Graphical User Interface. MATLAB Compiler. a fraction of the time it would take to write a program in a scalar non-interactive language such as C, C++ or Fortran.
MATLAB is the tool of choice for high-productivity research, development and analysis
Disadvantage
• it is an interpreted language and therefore can execute more slowly than compiled languages. • a full copy of MATLAB is five to ten times more expensive than a conventional C or Fortran compiler.
Starting MATLAB
• On UNIX : type matlab at command prompt • Click on the MATLAB icon if you are on a PC • Mac can probably do both… • Issues on startup – MATLAB needs a connection to the license server – Check internet connection – Too many users can use all available licenses
The name MATLAB stands for MATRIX LABORATORY It was developed by John Little and Cleve Moler of MathWorks, Inc.
MATLAB was originally written to provide easy access to the matrix computation software packages LINPACK and EISPACK.
• MATLAB is a high-level language whose basic data type is a matrix that does not require dimensioning. • There is no compilation and linking as is done in high-level languages, such as C or FORTRAN.
• Computer solutions in MATLAB seem to be much quicker than those of a high-level language such as C or FORTRAN. • All computations are performed in complex-valued double precision arithmetic to guarantee high accuracy.
• MATLAB has a large collection of toolboxes in a variety of domains. • Some examples of MATLAB toolboxes are control system, signal processing, neural network, image processing, and system identification. • The toolboxes consist of functions that can be used to perform computations in a specific domain.
• • When MATLAB is invoked, the command window will display the prompt >>.
• MATLAB is then ready for entering data or executing commands. • To quit MATLAB, type the command
exit
or
quit
• MATLAB has on-line help. To see the list of MATLAB’s help facility, type
help For eg help fft
• Scalars are thought of as a 1-by-1 matrix. Vectors are considered as matrices with a row or column.
• Storage of data and variables is allocated automatically once the data and variables are used.
• MATLAB statements are normally of the form:
variable = expression
Starting MATLAB
• Once MATLAB is running the GUI (Graphical User Interface) will appear – Default Window apperance
• Command Window – Main window in MATLAB – Commands entered here
Starting MATLAB
Starting MATLAB
• MATLAB displays >> prompt when ready for a command – Will have no >> prompt when processing commands – Newer versions also say “Ready” or “Busy” in lower left corner of GUI – Can use arrow keys to work through command history and modify commands • Essentially the same as UNIX command prompt
• “MATrix LABoratory” • Powerful, extensible, highly integrated computation, programming, visualization, and simulation package • Widely used in engineering, mathematics, and science • Why?
• Interactive code development proceeds incrementally; excellent development and rapid prototyping environment
• Basic data element is the auto-indexed array • This allows quick solutions to problems that can be formulated in vector or matrix form • Powerful GUI tools • Large collection of
toolboxes
: collections of topic-related MATLAB functions that extend the core functionality significantly
MATLAB Toolboxes Math and Analysis Optimization Requirements Management Interface Statistics Neural Network Symbolic/Extended Math Partial Differential Equations PLS Toolbox Mapping Spline Data Acquisition and Import Data Acquisition Instrument Control Excel Link Portable Graph Object
Signal & Image Processing Signal Processing Image Processing Communications Frequency Domain System Identification
Higher-Order Spectral Analysis System Identification Wavelet Filter Design
Control Design
Control System Fuzzy Logic Robust Control μ-Analysis and Synthesis Model Predictive Control
Intro MATLAB
Toolboxes, Software, & Links
Intro MATLAB
•
MATLAB System
Language:
arrays and matrices, control flow, I/O, data structures, user defined functions and scripts
• Working Environment:
editing, variable management, importing and exporting data, debugging, profiling
• Graphics system:
2D and 3D data visualization, animation and custom GUI development
• Mathematical Functions:
basic ( sum
,
sin ,…) to advanced ( fft
,
inv
, Bessel functions
, …)
• API:
can use MATLAB with C, Fortran, and Java, in either direction
Desktop Tools (Matlab v6)
• Command Window – type commands • Workspace – view program variables – clear to clear – double click on a variable to see it in the Array Editor • Command History – view past commands – save a whole session using diary • Launch Pad – access tools, demos and documentation
Data Types
• logical • char • CELL • structure • Java Classes • Function handle • ARRAY • NUMERIC ------------------------int,single,double
Variable Basics
>> 16 + 24 ans = 40 >> product = 16 * 23.24
product = 371.84
no declarations needed mixed data types
>> product = 16 *555.24; >> product product = 8883.8
semi-colon suppresses output of the calculation’s result
Intro MATLAB
>> clear
Variable Basics
clear
>> product = 2 * 3^3; removes all variables; removes only x and y >> show_i = i^2; complex numbers (
i
or
j
) require >> save three_things no special handling >> clear >> load three_things >> who Your variables are: comp_sum product show_i >> product product = 54 >> show_i show_i = -1 use
save / load
are used to retain/restore workspace variables
home
to clear screen and put cursor at the top of the screen Intro MATLAB
MATLAB Data
•
The basic data type used in MATLAB is the double precision array
• No
declarations
needed: MATLAB automatically allocates required memory • Resize arrays dynamically • To reuse a variable name, simply use it in the left hand side of an assignment statement • MATLAB displays results in scientific notation o Use
File/Preferences
and/or
format
function to change default short (5 digits), long (16 digits) format short g; format compact (my preference) Intro MATLAB
• A matrix • may be entered as follows: A = [1 2 3 2 3 4 3 4 5]; may be entered as follows: • A = [1 2 3; 2 3 4; 3 4 5];
• A row vector B with four elements • B = [ 6 9 12 15 18 ] • can be entered in MATLAB as B = [6 9 12 15 18]; or • B = [6 , 9,12,15,18]
• The row vector B can be turned into a column vector by
transposition
, which is obtained by typing • C = B’ • The above results in • C = 6 9 12 15 18
• Other ways of entering the column vector C are • C = [6 9 12 15 18] • or • C = [6; 9; 12; 15; 18]
• • MATLAB is case sensitive in naming variables, commands and functions.
• Thus b and B are not the same variable. If you do not want MATLAB to be case sensitive, you can use the command
casesen off
• To obtain the size of a specific variable, type
size ( )
. For example, to find the size of matrix A, you can execute the following command: • size(A)
• a vector x = 1 2 5 1
Matrices
x = [1 2 5 1] • a matrix x = [1 2 3; 5 1 4; 3 2 -1] x = 1 2 3 5 1 4 3 2 -1 • transpose y = x.’ y = 1 2 5 1
• x(i,j) subscription • whole row • whole column Matrices
y=x(2,3) y = 4 y=x(3,:) y = 3 2 -1 y=x(:,2) y = 2 1 2
Operators (arithmetic)
+ addition Subtraction • Multiplication / division ^ power ‘ complex conjugate transpose .* ./ .^ .‘ element-by-element mult element-by-element div element-by-element power transpose
| ~ == equal ~= not equal
Operators (relational, logical)
pi 3.14159265… < less than j imaginary unit, <= less than or equal greater than i same as j >= greater than or equal & AND OR NOT 1
Generating Vectors from functions
• zeros(M,N) MxN matrix of zeros • ones(M,N) MxN matrix of ones • rand(M,N) numbers MxN matrix of uniformly distributed random on (0,1) x = zeros(1,3) x = 0 0 0 x = ones(1,3) x = 1 1 1 x = rand(1,3) x = 0.9501 0.2311 0.6068
• plot • stem • grid • xlabel • ylabel • title • subplot • figure • pause
Graph Functions (summary)
linear plot discrete plot add grid lines add X-axis label add Y-axis label add graph title divide figure window create new figure window wait for user response
>>a=1 a = 1 >> b=4 b = 4 >> c=a+b c = 5 >> d=cos(a) d = 0.5403
To create variables
>>t=[1 2 3 4 5] t = 1 2 3 4 5 >>t=[1 2 3 4 5]; >> t=1:5 t = 1 2 3 4 5 %equally spaced arrays >> t=1:0.5:4 t = 1.0000 1.5000 2.0000 2.5000 3.0000 3.5000 4.0000
>>Whos %To know the variables typed so far Name Size Bytes Class Attributes a 1x1 8 double b 1x1 8 double c 1x1 8 double d 1x1 8 double t 1x6 48 double
One and two dimentional arrays
>>data=rand(2,2) data = 0.8147 0.1270
0.9058 0.9134
>>size(data) ans = 2 2 >>x=3+4i x = 3.0000 + 4.0000i
>>a=[1 2 3;4 5 6;7 8 9] a =
1 2 3 4 5 6 7 8 9
>>a(2,3) ans =6 >>b=a' b =
1 4 7 2 5 8 3 6 9 >>c=a*b c = 14 32 50 32 77 122 50 122 194
>>c=a.*b c =
1 8 21 8 25 48
21 48 81
>> data=rand(5,5) data = 0.5313 0.4235 0.4401 0.9436 0.2891
0.3251 0.0908 0.5271 0.6377 0.6718
0.1056 0.2665 0.4574 0.9577 0.6951
0.6110 0.1537 0.8754 0.2407 0.0680
0.7788 0.2810 0.5181 0.6761 0.2548
>> data(1:3,2:end) ans = 0.4235 0.4401 0.9436 0.2891
0.0908 0.5271 0.6377 0.6718
0.2665 0.4574 0.9577 0.6951
>> data(1:2,:)=0 data = 0 0 0 0 0 0 0 0 0 0 0.1056 0.2665 0.4574 0.9577 0.6951
0.6110 0.1537 0.8754 0.2407 0.0680
0.7788 0.2810 0.5181 0.6761 0.2548
>> a=1.5
a =1.5000
>> whos Name Size Bytes Class Attributes a 1x1 8 double >>format long >>1/7 ans =0.142857142857143
>>format short >> 1/7 ans =0.1429
>>x=3.2
x =3.2000
>>exp(x) ans =24.5325
>> d(1)=true d =1 >> d(2)=false d = 1 0 >>a=1.5
a = 1.5000
>> a<5 ans = 1 Boolean Expression
Matrix >>a=[1 2 3 4] a =1 2 3 4 >>a=[1 2;3 4] a = 1 2 3 4 >>a=1:10 a =1 2 3 4 5 6 7 8 9 10
>> a=1:10 a = 1 2 3 4 5 6 7 8 9 10 >> a=1:2:10 a = 1 3 5 7 9 >>10:-2:1 ans =10 8 6 4 2
>>I = eye(3), x = [8; -4; 1], I*x I = 1 0 0 0 1 0 0 0 1 x = 8 -4 1 ans = 8 4 1
>> a=rand(4,4) a = 0.8147 0.6324 0.9575 0.9572
0.9058 0.0975 0.9649 0.4854
0.1270 0.2785 0.1576 0.8003
0.9134 0.5469 0.9706 0.1419
>>a(1,2) ans =0.6324
>> a(1,[1,2]) ans = 0.8147 0.6324
>> a(1,:) ans = 0.8147 0.6324 0.9575 0.9572
>> a(1,2:end) ans =0.6324 0.9575 0.9572
a = 0.8147 0.6324 0.9575 0.9572
0.9058 0.0975 0.9649 0.4854
0.1270 0.2785 0.1576 0.8003
0.9134 0.5469 0.9706 0.1419
>> a(1,2:end-1)=[10,10] a = 0.8147 10.0000 10.0000 0.9572
0.9058 0.0975 0.9649 0.4854
0.1270 0.2785 0.1576 0.8003
0.9134 0.5469 0.9706 0.1419
>> a(1:2,:)=[] a = 0.1270 0.2785 0.1576 0.8003
0.9134 0.5469 0.9706 0.1419
a = 0.8147 0.6324 0.9575 0.9572
0.9058 0.0975 0.9649 0.4854
0.1270 0.2785 0.1576 0.8003
0.9134 0.5469 0.9706 0.1419
>> a(5) ans = 0.1576
>> a(:) ans = 0.1270
0.9134
0.2785
0.5469
0.1576
0.9706
0.8003
0.1419
a = 0.1270 0.2785 0.1576 0.8003
0.9134 0.5469 0.9706 0.1419
>> a<0.5
ans = 1 1 1 0 0 0 0 1 >> a(a<0.5)= -1 a = -1.0000 -1.0000 -1.0000 0.8003
0.9134 0.5469 0.9706 -1.0000
>> ind=find(a<0.5) ind = 1 3 5 8 a = 0.1270 0.2785 0.1576 0.8003
0.9134 0.5469 0.9706 0.1419
• • • • • • [r,c]=find(a<0.5) r = 1 1 1 2 • • • • • c = 1 2 3 4 a= 0.1270 0.2785 0.1576 0.8003
0.9134 0.5469 0.9706 0.1419
>> numel(a) ans = 8 >> a=rand(2,2) a = 0.4218 0.7922
0.9157 0.9595
>> b=[a,a] b = 0.4218 0.7922 0.4218 0.7922
0.9157 0.9595 0.9157 0.9595
0.1270 0.2785 0.1576 0.8003
0.9134 0.5469 0.9706 0.1419
>> b=[a;a] b = 0.4218 0.7922
0.9157 0.9595
0.4218 0.7922
0.9157 0.9595
>> a=1.5
a =1.5000
>> if a<5 disp('it is within the range') end it is within the range b = 0.4218 0.7922 0.4218 0.7922
0.9157 0.9595 0.9157 0.9595
Character constant
>> name='john' name = john >>[name 'smith'] ans =Johnsmith >>name(1:2) ans = jo >> k=1 % index with which to construct a sring k = 1 >> str=['ring' num2str(k)] str = ring1
>> car.year=2010 ans = 2010 >> car.color=‘red’ ans =red >> car.name=‘maruthi’ ans =maruthi
Structure
>>car=struct('year','2010','color','red','name','maruthi') car = year: '2010‘ color: 'red‘ name: 'maruthi‘ >>cars=[car;car] cars = 2x1 struct array with fields: year color name
>> cars(2).name='feat' cars = 2x1 struct array with fields: year color name >>cars.name
ans = maruthi ans = feat
>> cars(1).name,cars(2).name
ans = maruthi ans = Feat
Cell Array
mycell={1 2 3;'test' [1;2] false} mycell = [1] [ 2] [3] 'test' [2x1 double] [0]
>> mycell={1, 2,'orange',true} mycell = [1] [2] 'orange' [1] >>y=mycell(1,1) y = [1] >> y=mycell(1,2) y = [2]
>> y=mycell(1,3) y = 'orange‘ >>y=mycell(1,4) y = [1] >>class(y) ans = Cell
>>y=mycell{1,3} y =orange >>class(y) ans = char
>> mycell{3:4} ans = Orange ans =1 >> newcell={mycell{3:4}} newcell = 'orange' [1]
• if • switch • for • while
Flow Control
statement statement loops loops if A > B 'greater' elseif A < B 'less' else 'equal' end • continue • break statement statement for x = 1:10 r(x) = x; end
>> x = pi*(-1:3), round(x) %Round to nearest integer x = -3.1416 0 3.1416 6.2832 9.4248
ans = -3 0 >> fix(x) ans = -3 >> floor(x) 0 ans = -4 0 3 6 9 3 3 6 6 9 9 %Round toward zero % rounds against negative infinity
>> ceil(x) ans = -3 0 >> sign(x), 4 % Round towards positive infinity 7 10 1 if the corresponding element of X is greater than zero 0 if the corresponding element of X equals zero -1 if the corresponding element of X is less than zero ans = -1 0 1 1 1
Plotting the figure
>>t=0:0.05:0.5
>>y=sin(2*pi*t) y = 0 0.5878 0.9511 0.9511 0.5878 0.0000
>>plot(t,y) 1 0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0 0 0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Random Numbers
x=rand(100,1); stem(x); hist(x,100)
Line Styles & Colours The default is to plot solid lines. A solid white line is produced by >> plot(x,y,'w-') The third argument is a string whose rst character species the colour(optional) and the second the line style. The options for colours and styles are: Colours Line Styles y yellow . point m magenta o circle c cyan x x-mark r red + plus g green - solid b blue * star w white : dotted k black -. dashdot -- dashed
• • • • • • • x=1:5 x = 1 2 3 4 5 >> y=log(x) y = 0 0.6931 1.0986 1.3863 1.6094
>> plot(x,y) • plot(x,y,'*') 1.8
1.6
1.4
1.2
1 0.8
0.6
0.4
0.2
0 1 1.5
2 2.5
3 3.5
4 4.5
5 0.6
0.4
0.2
0 1 1.8
1.6
1.4
1.2
1 0.8
1.5
2 2.5
3 3.5
4 4.5
5
>>t=[12 23] t =12 23 >> y=[1,5] y =1 5 >> plot(t,y) >> plot(t,y,'r-') 5 4.5
4 3.5
3 2.5
2 1.5
1 12 14 16 18 20 22 24
Loops
1 >>x = -1:.05:1; >> for n = 1:2:8 0 -1 -1 1 subplot(4,2,n), plot(x,sin(n*pi*x)) subplot(4,2,n+1), plot(x,cos(n*pi*x)) 0 end -1 -1 1 0 draw sin(n*pi*x)and cos sin(n*pi*x)for n = 1; 3; 5; 7 alongside each other.
-1 -1 1 0 We may use any legal variable name as the \loop counter" -1 -1 (n in the above examples) and it can be made to run through all of the values in a given vector (1:8 and 1:2:8 in the examples).
We may also use for loops of the type -0.5
-0.5
-0.5
-0.5
0 0.5
0 0.5
0 0.5
0 0.5
1 1 1 1 -1 -1 1 0 -1 -1 1 0 -1 -1 1 0 -1 -1 1 0 -0.5
-0.5
-0.5
-0.5
0 0.5
0 0 0 0.5
0.5
0.5
1 1 1 1
Matlab Graphics
x = 0:pi/100:2*pi; y = sin(x); plot(x,y) xlabel('x=0:2\pi') ylabel('Sine of x') title('Plot of the Sine Function')
Multiple Graphs
t = 0:pi/100:2*pi; y1=sin(t); y2=sin(t+pi/2); plot(t,y1,t,y2) grid on
t = 0:pi/100:2*pi; y1=sin(t); y2=sin(t+pi/2); subplot(2,2,1) plot(t,y1) subplot(2,2,2) plot(t,y2)
Multiple Plots
• x=1:10; • y=1:10; • Z=x'*y; • surf(x,y,z); 100 80 60 40 20 0 10 5 0 0 2 4 6 8 10
>> [X,Y] = meshgrid(2:.2:4, 1:.2:3); >> Z = (X-3).^2-(Y-2).^2; >> mesh(X,Y,Z) >> title('Saddle'), xlabel('x'),ylabel('y') 1 0.5
0 -0.5
-1 3 2.5
2 y 1.5
1 2 Saddle 2.5
x 3 3.5
4
>>t=0:0.1:0.5
t =0 0.1000 0.2000 0.3000 0.4000 0.5000
>>y=sin(2*pi*t) y =0 0.5878 0.9511 0.9511 0.5878 0.0000
>>w=y'*y; >>surf(w) 1 0.8
0.6
0.4
0.2
0 6 4 2 0 1 2 3 4 5 6
Coin Tosses
• Simulate the outcomes of 100 fair coin tosses x=rand(100,1); p=sum(x<0.5)/100 p = 0.5400
• Simulate the outcomes of 1000 fair coin tosses x=rand(1000,1); p=sum(x<0.5)/1000 p =
Coin Tosses
• Simulate the outcomes of 1000 biased coin tosses with p[Head]=0.4
x=rand(1000,1); p=sum(x<0.4)/1000 p = 0.4160