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fscanf function - file function:
The fscanf reads matrix variable mat columnwise from the file specified by fid. The 'format' involves conversion characters much similar to those used in the fprintf function. The dimensions identify the preferred dimensions of mat; if the number of values in the file is not known, inf is used for the second dimension. For illustration, the below would read in the similar file identified earlier, that has on each line a number, then a space, then a character.
>> fid = fopen('subjexp.dat');
>> mat = fscanf(fid,'%f %c',[2 inf])
mat =
5.3000 2.2000 3.3000 4.4000 1.1000
97.0000 98.0000 97.0000 97.0000 98.0000
>> fclose(fid);
Program of Persistent variables: The program below elaborates this. The script calls a function func1 that initializes a variable count to 0, then increment it, and print the
Median The median is defined only for a data set which has been sorted first, that means that the values are in order. The median of a sorted set of data values (n) is defined
Generic code for Reading from Files: The generic code to complete this is as shown below: fid = fopen('filename'); if fid == -1 disp('File open not suc
Example of Median For the vector [1 4 5 9 12 33], the median is the average of the 5 & 9 in the middle: >> median([1 4 5 9 12 33]) ans = 7
Function Stubs: The other common debugging method, which is used when there is a script main program which calls many functions, is to use the function stubs. A function stub
Editor/Debugger: The MATLAB has numerous useful functions for debugging, and debugging can also be completed through its editor, which is known as the Editor/Debugger. Typi
fscanf function - file function: The fscanf reads matrix variable mat columnwise from the file specified by fid. The 'format' involves conversion characters much similar to th
Persistent Variables: Generally, whenever a function stops executing, then the local variables from that function are cleared. That means that each and every time a function i
Introduction and Purpose In the lectures and tutorials you have been introduced to a number of probability distributions. You have also learned that modelling any system (such
#qYou will need to examine two of the nine sections of data: one section of qualitative data (choose either Gender or Position) one section of quantitative data (choose either Intr
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