svd: Singular Value Decomposition of a Matrix.

In Fuzzy-Logix/AdapteR: Converts R syntax to SQL to provide fast analytics using in-database C++ library DB Lytix.

Description

svd computes the singular value decomposition for FLMatrix objects.

Usage

svd(object, ...)

Arguments

object	is of class FLMatrix
...	has nu number of left singular vectors to be computed.This must between 0 and nrow(object). nv number of right singular vectors to be computed.This must between 0 and ncol(object).

Value

svd returns a list of three components:

d	a FLVector containing the singular values of x, of size min(n, p).
u	a FLMatrix whose columns contain the left singular vectors of x, present if nu > 0. Dimension c(n, nu).
v	a FLMatrix whose columns contain the right singular vectors of x, present if nv > 0. Dimension c(p, nv).

Constraints

Input can only be with maximum dimension limitations of (550 x 550).

See Also

svd for corresponding R function reference

Examples

flmatrix <- FLMatrix(getTestTableName("tblMatrixMulti"), 5,"MATRIX_ID",
					"ROW_ID","COL_ID","CELL_VAL",dims= c(5,5))
resultList <- svd(flmatrix)
resultList$d
resultList$u
resultList$v

Fuzzy-Logix/AdapteR documentation built on May 6, 2019, 5:07 p.m.