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Matlab matrix to vector3/9/2024 ![]() MATLAB ® has several indexing styles that are not only powerful and flexible, but also readable and expressive. You can access strings in a string array with matrix indexing, just as you would access elements of a numeric. For instance, if A is numeric vector 1 20 300. converts the input array to a string array. One easy improvement is to broadcast the first line in your loop to avoid allocating a matrix for (sparseR + reshape(q' * sparseS, 199, 199)) and then another one for 0.5 * 0.05 * (sparseR + reshape(q' * sparseS, 199, 199)): tmp = 0.5. Indexing into a matrix is a means of selecting a subset of elements from the matrix. You can represent text in MATLAB using string arrays. ![]() Modifying your code to pre-allocate those matrices may help a lot. Find solutions to convert a matrix in one column/row vector composed of all the rows of the original matrix. In particular, you are constructing new matrices to hold a lot of intermediate quantities. You are seeing a lot of allocations because your code really does allocate a lot of memory. 343 1 1 gold badge 5 5 silver badges 16 16 bronze badges. Running your code in a function, I see 3.699408 seconds (41.60 k allocations: 3.787 GiB, 5.39% gc time) which is already quite close to what you reported MATLAB as giving. 220k 19 19 gold badges 263 263 silver badges 362 362 bronze badges. Instead, put the code you’re timing in a function. When benchmarking code, you will not get accurate results when timing in global scope.semicolons at the end of each line are not necessary Learn how to use the colon operator (:) to create vectors, subscript arrays, and specify for-loop iterations in MATLAB.For example, if one of A or B is a scalar, then the scalar is combined with each element of the other array. If the sizes of A and B are compatible, then the two arrays implicitly expand to match each other. ts2 timeseries (rand (2,5)) timeseries Common Properties: Name: unnamed Time: 2x1 double TimeInfo: tsdata.timemetadata Data: 2x5 double DataInfo: tsdata.datametadata. Therefore there are two sample times, starting at zero seconds. The sizes of A and B must be the same or be compatible. Create a timeseries with five data samples, where each sample is a column vector of length 2. Please quote your code so that it’s easy to read: PSA: how to quote code with backticks C A.B raises each element of A to the corresponding powers in B.How to do this in Matlab matlab matrix vector Share. the process is repeated same way for the rest of elements in the matrix. Julia shows 6.867342 seconds (3.00 k allocations: 4.283 GB, 15.98% gc time) I want to get a vector from the matrix as described in example below: Matrix 2 4 5 8 2 13 0 3 1 7 7 7. I don’t understand why, but hope it can help future readers and hope someone can explain this.įor the same codes, Matlab takes Elapsed time is 4.509614 seconds. I find that my original vector q has type 799 X 1 Array, the speed is much much faster. The stored vector contains the sequence of elements 12, 45, 33, 36, 29, 25, 91, 48, 11, and can be displayed using a single colon. While the following array is displayed as a 3-by-3 matrix, MATLAB stores it as a single column made up of the columns of A appended one after the other. # Here is how I'm timing the testFun(sparseM,sparseR,sparseS,q,100,799) A good way to visualize this concept is with a matrix. Tmp = 0.5 * 0.05 * (sparseR + reshape(q' * sparseS, numGrids, numGrids)) ![]() # I want to optimize this testFun functionįunction testFun(sparseM,sparseR,sparseS,q,numIters,numGrids) SparseS = sparse(rows3,cols3,vals3,numGrids,numGrids*numGrids) SparseR = sparse(rows2,cols2,vals2,numGrids,numGrids) SparseM = sparse(rows1,cols1,vals1,numGrids,numGrids) Vals3 = zeros(numGrids*numGrids*numGrids) Rows3 = zeros(Int64,numGrids*numGrids*numGrids) Ĭols3 = zeros(Int64,numGrids*numGrids*numGrids) I only care about the for loop part inside the testFun function.) 3.5: Cell Array Functions 3.6: Structure Functions 3: Vector, Matrix and Array Commands is shared under a CC BY 1. Dear All, I have a simple 33 matrix(A) and large number of 31 vectors(v). (The creation of the matrices is ugly, but it works. Matrix-vector multiplication vectorization.
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