![]() ![]() > x = v(:, end) reads entire last column. Use the end keyword to refer to the last element. We can read or modify a whole row or a whole column using colon operator. ![]() See also The Wonders Of The Embedded World: How Embedded Systems Make Our Lives Easier Index of a vector Index of matrix We can use an index to extract or modify a particular element.įor Matrix, an element belongs to row r and column c then (r,c) becomes its index. The position of an element in a matrix or array is called its index. Size() function gives the size of a vector or a matrix. MATLAB rand function MATLAB ones function MATLAB zeros function MATLAB eye function MATLAB diag function MATLAB get diag function diag() function can be used to create a diagonal matrix or to get diagonal elements of a matrix. eye() function is used to create identity matrix. Ones() and zeros() can be used to create an array of all ones and an array of all zeros. We can quickly create a square or non-square matrix using random numbers. > even_col = even_row' Vector and Matrix Creation Functions How can we create column vectors? We can create column vectors manually, by entering the elements and separating them by a semicolon.Īnother method is to create a row vector using one of the shorthand methods discussed before and then use the transpose operator to create a column vector. > even_row = linspace(0, 1, 5) creates a row vector with 5 elements evenly spaces from 0 to 1. We must specify the number of elements we want in a vector. > even_row = 1 : 0.5 : 5 creates a row vector with elements 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0.Īnother method to create evenly spaced vectors is the linspace() function. We can specify different spacing using the colon operator. The colon operator uses a default spacing of 1. 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. Modifying your code to pre-allocate those matrices may help a lot. 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. 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. Instead, put the code you’re timing in a function.
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