Matrix in matlab8/8/2023 In "for notation" we have: for(int x=0 x<3 x++) Suppose we had array(matrix) int T=new int Iterating through n-dimmensional array can be seen as increasing the n-digit number.Īt each dimmension we have as many digits as the lenght of the dimmension. OutArgs = cellfun(fcn, A, 'UniformOutput', false) This is done by calling either arrayfun or cellfun with an additional parameter/value pair: outArgs = arrayfun(fcn, A, 'UniformOutput', false) if my_func returns outputs of different sizes and types when it operates on different elements of A, then outArgs will have to be made into a cell array. If there are any outputs from my_func, these are placed in outArgs, which will be the same size/dimension as A. The function my_func has to accept A as an input. If A is a cell array of arbitrary dimension, you can use cellfun to apply my_func to each cell: outArgs = cellfun(fcn, A) You first create a function handle to this function: fcn = A is a matrix (of type double, single, etc.) of arbitrary dimension, you can use arrayfun to apply my_func to each element: outArgs = arrayfun(fcn, A) Let's first assume you have a function that you want to apply to each element of A (called my_func). There are also a couple of functions you can use: arrayfun and cellfun. (Though I don't use a 64 bit MATLAB release, I believe that problem has been resolved for those lucky individuals who do.)Īs pointed out in a few other answers, you can iterate over all elements in a matrix A (of any dimension) using a linear index from 1 to numel(A) in a single for loop. It is really only an issue if you use sparse matrices often, when occasionally this will cause a problem. So if your array has more then a total of 2^32 elements in it, the linear index will fail. MATLAB uses a 32 bit integer to store these indexes. The only problem with the linear index is when they get too large. So you can use it on structures, cell arrays, etc. The linear index applies in general to any array in matlab. Conversion between the linear index and two (or higher) dimensional subscripts is accomplished with the sub2ind and ind2sub functions. There are many circumstances where the linear index is more useful. For example, if we wanted to square the elements of A (yes, I know there are better ways to do this), one might do this: B = zeros(size(A)) The result is, we can access each element in turn of a general n-d array using a single loop. In fact, the function find returns its results as a linear index. A(:)Īs you can see, the 8th element is the number 7. We can see the order the elements are stored in memory by unrolling the array into a vector. MATLAB allows you to use either a row and column index, or a single linear index. An array in MATLAB is really just a vector of elements, strung out in memory. linear/logical subscripts) that makes it very easy to reorganize data sets in just one line of code before applying subsequent matrix or array operations.The idea of a linear index for arrays in matlab is an important one. Matlab also has a very synthetic syntax to perform array operations on sub-blocks (i.e. These data are not limited to be numbers, they are just n-dimensional data sets of whatever (string, numbers, cells, etc.). The later ones relate to linear algebra, while the other ones just relate to a practical way to operate on large sets of data. I would not say array operations encompass matrix operations, they are different. denoting you are now performing array operations. To perform element-wise operations on n-dimensional data sets, you have to write. linear algebra operations) like det, pinv, svd etc.Īs you can still see nowadays in Matlab, operators like *, / are strongly tied to matrix operations and thus strongly tied to linear algebra operations, which I think was the original goal of matlab in its early elaboration, hence its name (surely quite speculative but guess not so far from reality). Matlab has also a lot of routines related to matrix operations (i.e. For instance if matrix A represents the linear transformation f and matrix B the linear transformation g, then the composition f o g writes as A*B where * denotes matrix multiplication. It is also very practical to perform linear algebra operation in a very systematic way that can be implemented on a computer. A matrix is a practical way to represent a linear transformation from a space of dimension n to a space of dimension m in the form of a nxm array of scalar values.
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