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      Trending: A new answer sorting option. Linked 0. Related Hot Network Questions. Question feed. If this requirement is met, then dimensions where one of the arrays has size 1 are expanded to be the same size as the corresponding dimension in the other array. Another area where implicit expansion is useful for vectorization is if you are working with multidimensional data.

      Suppose you want to evaluate a function, F , of two variables, x and y. To evaluate this function at every combination of points in the x and y vectors, you need to define a grid of values. For this task you should avoid using loops to iterate through the point combinations. In this example, x is a by-1 vector and y is a 1-by vector, so the operation produces a by matrix by expanding the second dimension of x and the first dimension of y.

      In cases where you want to explicitly create the grids, you can use the meshgrid and ndgrid functions. A logical extension of the bulk processing of arrays is to vectorize comparisons and decision making. For example, suppose while collecting data from 10, cones, you record several negative values for the diameter.

      MATLAB can also compare two vectors with compatible sizes, allowing you to impose further restrictions. Logical operators isinf and isnan exist to help perform logical tests for these special values. When vectorizing code, you often need to construct a matrix with a particular size or structure. Techniques exist for creating uniform matrices.

      The function repmat possesses flexibility in building matrices from smaller matrices or vectors. In many applications, calculations done on an element of a vector depend on other elements in the same vector. For example, a vector, x , might represent a set.

      How to iterate through a set without a for or while loop is not obvious. The process becomes much clearer and the syntax less cumbersome when you use vectorized code. A number of different ways exist for finding the redundant elements of a vector. One way involves the function diff. After sorting the vector elements, equal adjacent elements produce a zero entry when you use the diff function on that vector. Because diff x produces a vector that has one fewer element than x , you must add an element that is not equal to any other element in the set.

      NaN always satisfies this condition. Use the tic and toc functions if you want to measure the performance of each code snippet. Rather than merely returning the set, or subset, of x , you can count the occurrences of an element in a vector. After the vector sorts, you can use the find function to determine the indices of zero values in diff x and to show where the elements change value. You can count the number of NaN and Inf values using the isnan and isinf functions.

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      The size requirement for the operands is that for each dimension, the arrays must either have the same size or one of them is 1. If this requirement is met, then dimensions where one of the arrays has size 1 are expanded to be the same size as the corresponding dimension in the other array. Another area where implicit expansion is useful for vectorization is if you are working with multidimensional data. Suppose you want to evaluate a function, F , of two variables, x and y.

      To evaluate this function at every combination of points in the x and y vectors, you need to define a grid of values. For this task you should avoid using loops to iterate through the point combinations. In this example, x is a by-1 vector and y is a 1-by vector, so the operation produces a by matrix by expanding the second dimension of x and the first dimension of y.

      In cases where you want to explicitly create the grids, you can use the meshgrid and ndgrid functions. A logical extension of the bulk processing of arrays is to vectorize comparisons and decision making. For example, suppose while collecting data from 10, cones, you record several negative values for the diameter.

      MATLAB can also compare two vectors with compatible sizes, allowing you to impose further restrictions. Logical operators isinf and isnan exist to help perform logical tests for these special values. When vectorizing code, you often need to construct a matrix with a particular size or structure. Techniques exist for creating uniform matrices. The function repmat possesses flexibility in building matrices from smaller matrices or vectors.

      In many applications, calculations done on an element of a vector depend on other elements in the same vector. For example, a vector, x , might represent a set. How to iterate through a set without a for or while loop is not obvious. The process becomes much clearer and the syntax less cumbersome when you use vectorized code. A number of different ways exist for finding the redundant elements of a vector. One way involves the function diff. After sorting the vector elements, equal adjacent elements produce a zero entry when you use the diff function on that vector.

      Because diff x produces a vector that has one fewer element than x , you must add an element that is not equal to any other element in the set. NaN always satisfies this condition. Use the tic and toc functions if you want to measure the performance of each code snippet. Rather than merely returning the set, or subset, of x , you can count the occurrences of an element in a vector. After the vector sorts, you can use the find function to determine the indices of zero values in diff x and to show where the elements change value.

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