## C program to find maximum element in a matrix.

Algorithm: Let the given binary matrix be M(R)(C). The idea of the algorithm is to construct an auxiliary size matrix S()() in which each entry S(i)(j) represents size of the square sub-matrix with all 1s including M(i)(j) where M(i)(j) is the rightmost and bottommost entry in sub-matrix.

Details. If one of nrow or ncol is not given, an attempt is made to infer it from the length of data and the other parameter. If neither is given, a one-column matrix is returned. If there are too few elements in data to fill the matrix, then the elements in data are recycled. If data has length zero, NA of an appropriate type is used for atomic vectors (0 for raw vectors) and NULL for lists.

Increase memory size in rstudio. R holds objects it is using in virtual memory. This help file documents the current design limitations on large objects: these differ between 32-bit and 64-bit builds of R. Currently R runs on 32- and 64-bit operating systems, and most 64-bit OSes (including Linux, Solaris, Windows and macOS) can run either 32- or 64-bit builds of R.

A matrix is a collection of data elements arranged in a two-dimensional rectangular layout. The following is an example of a matrix with 2 rows and 3 columns. We reproduce a memory representation of the matrix in R with the matrix function. The data elements must be of the same basic type.

Given a matrix of dimensions mxn having all entries as 1 or 0, find out the size of maximum size square sub-matrix with all 1s. Java solution is provided in code snippet section. Java visualization is provided in algorithm visualization section.

If the code you posted, however, is one step in your own HOG code, it looks like that step is an edge detection step. You are trying to find some surface of maximum gradient in 3D and collect the gradient values along that 3D surface, storing them to gxtemp and gytemp.

In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of a probability distribution by maximizing a likelihood function, so that under the assumed statistical model the observed data is most probable. The point in the parameter space that maximizes the likelihood function is called the maximum likelihood estimate. The logic of maximum likelihood is both.