Sentence examples for matrix row by from inspiring English sources

The OS browses the matrix row by row.

Here, the normalization refers to the division of each matrix row by its associated diagonal entry.

Set d' to 0. Examine the aberration matrix row by row, in left to right order incrementing d' as follows in each cell (i,j):then If x i,j = 1 then: Else, where log denotes binary logarithm.

Therefore, we need to adopt the concept of symmetrical three hexadecimal to extracting the matrix row by row for get sequence of symmetric ternary, and the symmetric ternary sequence is a Code number that is unique for each isomorphic class.

The algorithm then examines the sorted matrix row by row in a left to right order, keeping track of how many aberrations have been observed, and calculates a probability of observing an aberration in the next cell of the matrix and encoding the bit optimally according to the calculated probability.

From these the probability matrix for the reduced metabolites-only network is calculated by (1.2) P 1 = R o w N o r m (R o w N o r m (C R ) R o w N o r m (R C ) + 0.25   I ) Here the function RowNorm normalises each matrix row by a simple row sum, and converts the adjacency matrices to probability matrices.

Indeed, in this framework the convolution with g is nothing but the continuous matrix multiplication between v and a continuous Toeplitz matrix generated row by row by g.

To describe the matrices, we use 16-element binary series which are built of matrix elements row by row: begin{aligned} left[ begin{array}{llll} 1 & quad 0 & quad 0 & quad 0 0 & quad 0 & quad 1 & quad 0 0 & quad 0 & quad 0 & quad 1 0 & quad 1 & quad 0 & quad 0 end{array} right] rightarrow 1000|0010|0001|0100 rightarrow 1000001000010100.

Therefore, it is natural to define ( u ∗ g ) ′ = ( u ⋅ G ) ′ = G ′ ⋅ u ′, where G ′ ∈ R R × R is the transpose of G, i.e., the continuous Toeplitz matrix generated row by row by g : t ↦ g ( − t ) and u ′ ∈ R R × n.

Matrix rows (and columns) are ordered by total spike count in the population responses.

Alternatively, in the unsupervised analysis mode, three feature rankings are obtained from the pathway expression variance matrix (rows = pathways, columns = samples) by computing the absolute variances across the columns/samples, the magnitude of the loadings in a sparse principal component analysis (Zou and Hastie, 2008) and a recently proposed entropy score (Varshavsky et al., 2006).