Lecture 2: Advanced Programming Topics

Transcription

1 Lecture 2: Advanced Programming Topics Characters and Strings A character in the MATLAB software is actually an integer value converted to its Unicode character equivalent. A character string is a vector with components that are the numeric codes for the characters. The actual characters displayed depend on the character set encoding for a given font. The elements of a character or string belong to the char class. Arrays of class char can hold multiple strings, as long as each string in the array has the same length. (This is because MATLAB arrays must be rectangular.) To store an array of strings of unequal length, use a cell array. Creating a Single Character Store a single character in the MATLAB workspace by enclosing the character in single quotation marks and assigning it to a variable: >> hchar = 'h'; This creates a 1-by-1 matrix of class char. Each character occupies 2 bytes of workspace memory: The numeric value of hchar is 104: >> uint8(hchar) ans = 104 Creating a Character String Create a string by enclosing a sequence of letters in single quotation marks. MATLAB represents the five-character string shown below as a 1-by-5 vector of class char. It occupies 2 bytes of memory for each character in the string: >> str = 'Hello'; The uint8 function converts characters to their numeric values:

2 >> str_numeric = uint8(str) str_numeric = The char function converts the integer vector back to characters: >> str_alpha = char([ ]) str_alpha = Hello Creating an Array of Strings Create an array of strings in the same way that you would create a numeric array. Use the array constructor ([]), delimit each row in the array with a semicolon, and enclose each string in single quotation marks. Like numeric arrays, character arrays must be rectangular. That is, each row of the array must be the same length: >> name = ['Thomas R. Lee';... 'Sr. Developer';... 'SFTware Corp.']; String Functions: Comparisons strcmp determines if two strings are identical. strncmp determines if the first n characters of two strings are identical. strcmpi and strncmpi are the same as strcmp and strncmp, except that they ignore case. String Functions: Searching and Replacing The strrep function performs the standard search-and-replace operation. findstr returns the starting position of a substring within a longer string. The strmatch function looks through the rows of a character array or cell array of strings to find strings that begin with a given series of characters. It returns the indices of the rows that begin with these characters. Exercise

3 Write a script that does the following: 1. Prompt for the user to enter a string and save it in a variable called str. What do you have to do to make it read in a character or string? 2. Set the variable sentence to be : 'Because MATLAB is very exciting.' 3. Replace all occurences of str in the sentence with 'However,' and include another reason 'and I love food.' 4. Test it with the following: str = 'Because' Example: If the why command returns Because Damien says so., your script should return However, Damien says so and I love food. Save your script as PROBLEM1.m File I/O Matlab supports C-style file input and output and provides most of the same functionality and same syntax commands as C. The following C commands are all available for file I/O under Matlab. fprintf sprintf fopen fclose fscanf Example >>A=[ ]; >>fid=fopen( myoutput, w ); >>fprintf(fid,%g miles/hour= %g kilometers/hour\n, [A;8*A/5]); >>fclose(fid); >>fid=fopen( myoutput, r ); >>X=fscanf(fid, %g miles/hour=%g kilometers/hour ) >>Y=fscanf(fid,%g miles/hour = %g kilometers/hour,[2 inf]) >>fclose(fid); Read the help files for these functions to see all the various formatting options. Matlab also provides a more powerful function called textscan. Textscan is used to scan textfiles and has a different syntax and output than fscanf. Example >>fid=fopen( myoutput, r ); >>C=textscan(fid, %f miles/hour = %f kilometers/hour )

4 >>C{1} >>C{2} Structures and Cells We have seen vectors and matrices as well as multidimensional arrays in the previous lecture. While incredibly useful they are limited in a few key ways. None of these data types can handle mixed data and all the data must be of the same size. To overcome these problems Matlab has structures and cells. Cells are used in many built in Matlab functions, especially those that can accept an unknown number of inputs or return an unknown number of outputs. Structures and cells serve similar purposes but the key difference lies in how they are indexed. Cells are also somewhat more flexible than structures. See help datatypes for a listing of all the functions for manipulating cells and structures. A structure is a data structure that identifies each data entry with a chosen text name. They are also called records in other programming languages. You can create n-dimensional arrays of structs the same as you would create an n-dimensional array of numbers. An intuitive example of a struct is calendar entry in a schedule book. Structures are primarily of use when we can expect our data to fall into some predetermined format. There are two different ways to declare a struct. Structures Examples type 1(Direct initialization of 1-D struct arrays.) >>Datebook(1).event= Dinner with Friends ; >>Datebook(1).day=15; >>Datebook(1).month= Jan ; >>Datebook(1).year=2010; >>Datebook(1).time=1800; >>Datebook(2).event= Hockey Game ; >>Datebook(2).day=25; >>Datebook(2).month= Feb ; >>Datebook(2).year=2010; >>Datebook(2).time=1900 >>Datebook >>Datebook(2).event >>Datebook(1).day >>n=4 >>testmat(1).name= Hilbert ; >>testmat(1).mat=hilb(n);

5 >>testmat(1).eig=eig(hilb(n)); >>testmat(2).name= Pascal ; >>testmat(2).mat=pascal(n); >>testmat(2).eig=eig(pascal(n)); >>testmat >>testmat(2).name >>testmat(1).mat >>testmat(2).eig >>testmat(1).mat(1:2,1:2) Structures Examples 2(Creating structs using the struct command) >>clear testmat Datebook; >>Datebookt=struct( event,{ Dinner with Friends, Hockey Game }, day,{15,25}, month,{ Jan, Feb }, year,{2010,2010}, time,{1800,1900}); >>testmat=struct( name,{ Hilbert, Pascal }, mat,{hilb(n),pascal(n), eig,{eig(hilb(n)),eig(pascal(n))}); >>Datebook, testmat Structures Examples 3(Creating 5 by 5 arrays of empty structs using repmat) >>clear testmat Datebook; >>Datebook=repmat(struct( event,{ }, day,{0}, month,{ }, year,{0}, time,{0},5,5) >>testmat=repmat(struct( name,{ }, mat,{zeros(n)}, eig,{zeros(n,1)}),5,5) Cells are in the same spirit as structs, however the entries of a cell are not named. Cells are very useful when we wish to combine data types but do not have an expected data format. We already saw one example of how Matlab uses cells to handle an unknown number of outputs when we used the command textscan. Note that cells are enclosed by {} at declaration unlike a struct.

6 Cell Example 1 (Create a 2 by 2 cell of different types) >>C={1:3,pi; magic(2), A string } >>C{1,1} >>C{2,:} >>C{2,1}(2,2) Cell Example 2(Showing the equivalence of Cells and Structs) >>clear testmat >>testmat{1,1}= Hilbert ; >>testmat{2,1}=hilb(n); >>testmat{3,1}=eig(hilb(n)); >>testmat{1,2}= Pascal ; >>testmat{2,2}=pascal(n); >>testmat{3,2}=eig(pascal(n)); >>testmat >>celldisp(testmat) >>testmat(1,1)={ Hilbert }; >>testmat{2,1}(4,4) Cell Example 3 >>clear testmat >>testmat=cell(3,2); Matlab also has cell2struct and struct2cell for converting between structs and cells. Efficiency Writing code that is correct is only one half of programming. Code also needs to be efficient to be truly useful. While Matlab code is never going to be as fast as compiled languages such as C or C++, there are still steps one can take to improve the speed of Matlab code. tic and toc To begin profiling code for efficiency, Matlab provides the tic and toc commands. Tic and toc are used as a pair and should bracket the code block that you wish to examine. When used appropriately these commands can help determine where code spends a majority of its processing time without the need for more complex profiling tools. Vectorizing Code and Preallocating Memory Matlab has been optimized to run code on vectors and matrices. Some of the biggest speedups in Matlab, historically, have be achieved by looking for clever ways to eliminate for loops or excessive index

7 access and instead use vectors or matrices to store data and perform parallel computation. However, Matlab has greatly improved the efficiency of for loops in more modern releases, and for smaller or less complex problems, for loops can often achieve the same speed as the vectorized equivalent. Additionally, preallocating memory can greatly speed up for loops. Note that Matlab will dynamically assign memory as needed. For example, if a vector is growing within a for loop Matlab will assign memory as needed to store the vector, but this can become very slow for very large for loops. A more detailed guide to this process can be found at A Simple Contrived Example >>A=rand(100000,1);B=rand(100000,1); >>clear C; >>tic, for i=1:100000,c(i)=a(i)+b(i);end,toc >>clear C; >>C=zeros(100000,1); >>tic, for i=1:100000,c(i)=a(i)+b(i);end,toc >>clear C; >>tic,c=a+b;toc >>clear C; >>C=zeros(100000,1); >>tic,c=a+b;toc Another Contrived Example >>A = magic(100); >> B = pascal(100); >>tic, for j = 1:100 for k = 1:100; X(j,k) = sqrt(a(j,k)) * (B(j,k) - 1); end end,toc >>clear X >>tic,x = sqrt(a).*(b-1),toc;

8 Using Permutations Another common trick exploited in more advanced Matlab code is the use of permutations to reorder data. A vector that represents the desired index permutation is used to index the vector to be reordered and the output saved. One can also use these tricks to reorder the rows or columns of a matrix as will be seen in the examples. Example 1(Vectors) >> x = [ ] >>x=x([ ]) >>[s,ix]=sort(x) >>ix_inv(ix)=1:length(ix); >>s(ix_inv) Example 2(Matrices) >>A=magic(4);p=[ ]; >>I=eye(4);P=I(p,:) >>P*A >>A(p,:) >>A*P >>A(:,p) To generate a random permutation Matlab offers the command randperm >>randperm(8) Additionally Matlab offers the commands permute and ipermute that will rearrange the dimensions of a matrix according to a permutation vector. Example 3(Permuting Matrix Dimensions) >>A=rand(12,13,14); >>B=permute(A,[3 2 1]); >>size(b) >>C=ipermute(B,[3 2 1]); >>size(c) Using the Profiler Matlab offers a built in code profiler tool that will capture information about all commands and calls run by Matlab. It can be activated from either the command line or the GUI. When the profiler is activated, it will capture the runtime information and all function calls made by the code executed. Example(Using the Profiler from the Command line)

9 >>help profile >> profile on history >>magic(4); >>profile off; >>profile viewer Commenting and the Help Command It is a good idea to comment your code using brief and descriptive comments so that goal of your code is as obvious as possible. It s not necessary to comment every bit of your code, but any area that isn t obvious from the code itself should receive some amount of commenting. It is also good style to document the inputs, outputs and expected usage or behavior of your code at the top of your files. In Matlab, the help command will return any comments written into the m-file before the first line of executable code. Executable code does not include the function declaration itself. Input Checking Many runtime errors result not from the code, but from a failure to properly check the data being input to the code (This applies to all languages). It is a good idea to include input checking into any code that is intended for use by outside parties (and is of course beneficial even for personal use code). Failure to properly check inputs can result in code crashing, bizarre code behavior and even security vulnerabilities (depending on the situation). In Matlab, some principle tools for checking inputs to functions are the is* functions such as isreal or ischar(see helpwin is* for a full listing), the size and length functions, and the class command. Errors and Warnings Matlab provides the functions error and warning for returning and displaying customized error and warning messages. These will be used in the first exercise and you should read the help files. Debugging Debugging code can be an exhausting proposition. There are two types of errors that will prevent your code from running to completion: syntax errors and runtime errors. Syntax errors are generally relatively obvious problems and the m-lint (lint tools are available for a variety of languages and I can t recommend them enough) tool in the Matlab editor makes finding these errors relatively straightforward. Run-time errors are errors that occur during the execution of your code and tend to be more algorithmic in nature. For

10 instance you may miscalculate an index and thus access the wrong data or perhaps call the wrong vector when assigning output. If your code is well structured and documented it can make isolating errors a more straightforward exercise. When debugging code I tend to follow this checklist. 1. Check my input to make sure I m not entering garbage data to the code 2. Check core algorithm code sections for any obvious typos or indexing errors 3. Display the output from intermediate calculation steps. If there are several steps that would flood my display or if there is code in a loop I ll utilize pause commands to step through the code for some reduced size input. Alternatively outputs can be sent to a log file using file I/O and then read through at leisure. 4. If none of the above steps reveal any problems I ll reexamine my algorithm and code for any subtle problems or bizarre cases that could cause problems. This checklist is a bit primitive in some ways, but it tends to work relatively well in most cases and for a wide range of programming languages. More complex code with several calls to subfunctions that could be in error can become more complicated to deal with in this fashion, but thorough testing of subfunctions(and/or examining subfunction input and output) before use can help with this problem.(a good coding practice is to validate small units of code at a time. This is called Unit Testing is the software engineering realm) Additionally, there are several more powerful tools that exist for the purpose of runtime code analysis. Matlab has a built in runtime debugger that can be used to step through code and examine runtime data at set points of interest. This tool replaces step 3 in my checklist and many major programming languages have code debugging tools similar to the Matlab tool(i.e. gdb for C/C++ code). The debugger can be called from either the command line or accessed through the Matlab editor. Key commands for the command line are dbstop dbquit dbcont dbstep dbclear dbtype dbstack

11 dbup dbdown dbstatus Exercise 5 will involve working through an example debugging process using the Matlab debugger from the editor. Exercises 1) Write a function that accepts as input 2 real numbers and returns a vector containing their sum difference, product, quotient(num1/num2) and num1/sqrt(num2). (e.g., something of the form function V=myfuncname(input1,input2)) Comment the code so that help will display the inputs and outputs of the function and explain the functions behavior and usage Check the input to make sure the numbers are realvalued and scalar and return an error if they are not Display a warning if the second input is negative or zero Profile the code and save a profile report of the run. 2) Write a function that prompts the user for their name, date of birth and eye color. Store these elements in both a struct and a cell. Input can accept strings as well an numerical input and the help file shows how to do this. 3) Modify your function from 2 to also write the data to a file called name.txt. The function strcat will be useful here. 4) Read out the data stored in name.txt and display it on the screen. Read the helpfile for fscanf to find the appropriate formatting to read the string data. 5) In the Matlab helpwin search for Debugging Process and Features and work through the example problem.