LeastSquares¶
- class lsst.afw.math.LeastSquares¶
Bases:
pybind11_object
Attributes Summary
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 ¶