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## MATLAB function and its DATA types

MATLAB function and its DATA types::MABLAB functionMATLAB functions are alike to FORTRAN subroutines or C functions.

MATLAB functions consist of two parameter lists, one for output and one for input. It supports one of the cardinal rules of MATLAB programming: do not alter the input parameters of a function. Similar to all cardinal rules, that one is broken at all times.

Function:

Declare function name, outputs, and inputs

Syntax:

Function [y1,...,yN] = myfun(x1,...,xM)

Description:

function [y1,...,yN] = myfun(x1,...,xM) declares a function termed myfun which admits inputs x1,...,xM and returns outputs y1,...,yN. The declaration statement should be the primary executable line of the function.

And then save the function code in a text file with a .m extension. The name of the file must match the name of the primary function in the file. Suitable function names begin with an alphabetic character, and can consists of underscores, letters, or numbers.

We can declare many local functions in the same file, or nest functions. When any function in a file consists of a nested function, all functions in the file should employ the end keyword to point out the end of the function. Or else, the end keyword is optional.

:MATLAB DATA typesThere are fifteen essential data types in MATLAB. Each and every of these data types is in the type of an array or matrix. This array or matrix is a least amount of 0-by-0 in size and can grow up to an n-dimensional array of any other size. All of the essential data types are appear in lowercase text in the figure below. Additional data types are object-oriented, user-defined user classes & Java classes. We can employ the latter with the MATLAB interface to Java (or see Calling Java since MATLAB in the MATLAB External Interfaces documentation). We can make two-dimensional logical and double matrices employing one of two storage formats: sparse /full. For matrices with mainly zero-valued elements, a sparse matrix needs a fraction of the storage space needed for an equal full matrix. Sparse matrices invoke techniques specifically tailored to resolve sparse problems.

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