The purpose of this function is to provide a flexible and robust fit to one-dimensional data using free-knot splines. The knots are free and able to cope with rapid change in the underlying model. Knot removal strategy is used to fit with only a small number of knots. Optional L2-regularization on the derivative of the spline function can be used to enforce the smoothness. Shape preserving approximation can be enforced by specifying the lower and upper bounds of the derivative(s) of the spline function on sub-intervals. Furthermore specific values of the spline function and its derivative can be specified on a set of discrete data points. I did not test QUADPROG engine, but I have implemented it. Any feedback is welcome.
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The purpose of this function is to provide a flexible and robust fit to one-dimensional data using free-knot splines. The knots are free and able to cope with rapid change in the underlying model. Knot removal strategy is used to fit with only a small number of knots. Optional L2-regularization on the derivative of the spline function can be use…
BrunoLuong/Free-knot-spline-approximation
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The purpose of this function is to provide a flexible and robust fit to one-dimensional data using free-knot splines. The knots are free and able to cope with rapid change in the underlying model. Knot removal strategy is used to fit with only a small number of knots. Optional L2-regularization on the derivative of the spline function can be use…
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