Sentence examples for matrix each element from inspiring English sources

The clustering algorithm employed in the present study requires a distance matrix, each element of which represents the distance or dissimilarity between two compounds being considered for clustering.

An AASM is a 20×20 real (usually symmetric) matrix each element of which reflects the tendency of substitution between amino acid residues.

In this matrix, each element c ij is the Pearson coefficient between the vectors i and j.

For this symmetric matrix, each element in the upper half of the matrix was multiplied by a random number, and the corresponding element in the lower half was assigned the same value.

In a 20 × 20 protein substitution matrix, each element s ij is a score derived from the probability that, in homologous sequences, amino acids i and j descend from a common ancestor.

Given X and assuming C to be an invertible matrix, each element PC ij is defined as − P i j P i i P j j, where P ≡ C -1 is the inverse of the correlation matrix (ie. the so-called precision matrix).

Assume that there is an LDPC code that is defined by an M×N parity check matrix H or an M p ×N p base matrix B. Each element of base matrix b (i,j),(1≤i≤M p,1≤j≤N p ) represents the number of connected edges between type-i check nodes and type-j variable nodes.

This fuzzy soft set gives a relation matrix (weighted matrix) R, called symptom-disease matrix, where each element denotes the weight of the symptoms for a certain disease.

The posterior similarity matrix is an (ngenes × ngenes) matrix where each element gives the posterior probability that a given pair of genes are found in the same cluster (and hence also in the same context).

Band-Toeplitz noise covariance matrix, with each element given by a modeling function; Band-Toeplitz noise covariance matrix with the elements arbitrary chosen; Band noise covariance matrix used in [16].

To demonstrate the performance of the proposed algorithm, the following three situations are considered: (i) Band-Toeplitz noise covariance matrix, with each element given by a modeling function;   (ii) Band-Toeplitz noise covariance matrix with the elements arbitrary chosen;   (iii) Band noise covariance matrix used in [16].  .