Sentence examples for generate a random matrix from inspiring English sources

For the null model, we need to generate a random matrix where the observed sums of rows and columns are preserved.

Our algorithm generates a random matrix, with dimensions that are smaller than M and N, where M is the length of the sampling vector and N is the length of signal that we want to reconstruct.

We present an O(max(n, nm)) algorithm for generating a random matrix with the above conditions.

Work such as HPCgen and Epigrass [ 1, 18] take the approach of modeling actual populations; FastGen and CL-model [ 19, 20] choose instead to generate a random adjacency matrix.

Randomly selected M rows from Ψ will generate a random partial Fourier matrix Θ; the range signal reconstruction from the SAR echoes can be accomplished in range frequency domain instead of the traditional match filtering.

These functions generate a random-correlation matrix with a number of rows and columns equal to the number of traits in the input data set.

In Sufficient conditions, we find some sufficient conditions and present an algorithm to generate a random sample of the abovementioned matrices.

To generate a random set of miRNA target predictions, we first converted the real target predictions into a binary miRNA-target matrix.

The proposed approach generates a random measurement matrix, where the dimensions of the random measurement matrix are reduced to a quarter (or 1/16, 1/64, and even 1/256) of the number of dimensions, which are used for conventional CS.

Generating a random correlation matrix that is both realistic and contains strong correlations is a challenging problem as it is difficult to assign random correlation coefficients and make it a positive definite matrix.

This means that if we generate a suitable random matrix (that is, if it satisfies RIP and NSP), we can also obtain a precise reconstruction, even if the dimensions of the matrix are reduced.