Template Class LeastSqFitter1d¶
Defined in File LeastSqFitter1d.h
Class Documentation¶
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template<class
FittingFunc
>
classLeastSqFitter1d
Fit an lsst::afw::math::Function1 object to a set of data points in one dimension.
The class is templated over the kind of object to fit.
Input is a list of x ordinates for a set of points, the y coordinate, and the uncertainties, s. order is order of the polynomial to fit (e.g if the templated function is lsst::afw::math::PolynomialFunction1, then order=3 => fit a function of the form \(ax^2+bx+c\)
- See
- Template Parameters
FittingFunc
: The 1d function to fit in both dimensions. Must inherit from lsst::afw::math::Function1
- Parameters
x
: Ordinate of points to fity
: Co-ordinate of pionts to fits
: 1 \(\sigma\) uncertainties in zorder
: Polynomial order to fit
Public Functions
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LeastSqFitter1d
(const std::vector<double> &x, const std::vector<double> &y, const std::vector<double> &s, int order)¶ Fit a 1d polynomial to a set of data points z(x, y)
- Template Parameters
FittingFunc
: The type of function to fit. This function extends the base class of lsst::afw::math::Function1
- Parameters
x
: vector of x positions of datay
: vector of y positions of datas
: Vector of measured uncertainties in the values of zorder
: Order of 2d function to fit
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Eigen::VectorXd
getParams
()¶ Return the best fit parameters as an Eigen::Matrix.
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Eigen::VectorXd
getErrors
()¶ Return the 1 sigma uncertainties in the best fit parameters as an Eigen::Matrix.
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FittingFunc
getBestFitFunction
()¶ Return the best fit polynomial as a lsst::afw::math::Function1 object.
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double
valueAt
(double x)¶ Calculate the value of the function at a given point.
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std::vector<double>
residuals
()¶ Return a vector of residuals of the fit (i.e the difference between the input y values, and the value of the fitting function at that point.
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double
getChiSq
()¶ Return a measure of the goodness of fit.
\[ \chi_r^2 = \sum \left( \frac{y_i - f(x_i)}{s} \right)^2 \].
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double
getReducedChiSq
()¶ Return a measure of the goodness of fit.
\[ \chi_r^2 = \sum \left( \frac{y_i - f(x_i)}{s} \right)^2 \div (N-p) \]Where \( N \) is the number of data points, and \( p \) is the number of parameters in the fit.