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: SupportsFloat) → None