LeastSquares

class lsst.afw.math.LeastSquares

Bases: pybind11_object

Attributes Summary

DIRECT_SVD

NORMAL_CHOLESKY

NORMAL_EIGENSYSTEM

Methods Summary

fromDesignMatrix(design, data, factorization)

fromNormalEquations(fisher, rhs, factorization)

getCovariance(self)

getDiagnostic(self, arg0)

getDimension(self)

getFactorization(self)

getFisherMatrix(self)

getRank(self)

getSolution(self)

getThreshold(self)

setDesignMatrix(self, arg0, arg1)

setNormalEquations(self, arg0, arg1)

setThreshold(self, arg0)

Attributes Documentation

DIRECT_SVD = <Factorization.DIRECT_SVD: 2>
NORMAL_CHOLESKY = <Factorization.NORMAL_CHOLESKY: 1>
NORMAL_EIGENSYSTEM = <Factorization.NORMAL_EIGENSYSTEM: 0>

Methods Documentation

static fromDesignMatrix(design: numpy.ndarray, data: numpy.ndarray, factorization: lsst.afw.math.LeastSquares.Factorization = <Factorization.???: 0>) lsst.afw.math.LeastSquares
static fromNormalEquations(fisher: numpy.ndarray, rhs: numpy.ndarray, factorization: lsst.afw.math.LeastSquares.Factorization = <Factorization.???: 0>) lsst.afw.math.LeastSquares
getCovariance(self: lsst.afw.math.LeastSquares) numpy.ndarray
getDiagnostic(self: lsst.afw.math.LeastSquares, arg0: lsst.afw.math.LeastSquares.Factorization) numpy.ndarray
getDimension(self: lsst.afw.math.LeastSquares) int
getFactorization(self: lsst.afw.math.LeastSquares) lsst.afw.math.LeastSquares.Factorization
getFisherMatrix(self: lsst.afw.math.LeastSquares) numpy.ndarray
getRank(self: lsst.afw.math.LeastSquares) int
getSolution(self: lsst.afw.math.LeastSquares) numpy.ndarray
getThreshold(self: lsst.afw.math.LeastSquares) float
setDesignMatrix(self: lsst.afw.math.LeastSquares, arg0: numpy.ndarray, arg1: numpy.ndarray) None
setNormalEquations(self: lsst.afw.math.LeastSquares, arg0: numpy.ndarray, arg1: numpy.ndarray) None
setThreshold(self: lsst.afw.math.LeastSquares, arg0: float) None