forked from kscience/kmath
add documentation to the main function levenbergMarquardt
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@ -104,6 +104,21 @@ public data class LMInput (
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var exampleNumber: Int
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)
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/**
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* Levenberg-Marquardt optimization.
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*
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* An optimization method that iteratively searches for the optimal function parameters
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* that best describe the dataset. The 'input' is the function being optimized, a set of real data
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* (calculated with independent variables, but with an unknown set of parameters), a set of
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* independent variables, and variables for adjusting the algorithm, described in the documentation for the LMInput class.
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* The function returns number of completed iterations, the number of evaluations of the input function during execution,
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* chi squared value on final parameters, final lambda parameter used to calculate the offset, final parameters
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* and type of convergence in the 'output'.
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*
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* @receiver the `input`.
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* @return the 'output'.
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*/
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public fun DoubleTensorAlgebra.levenbergMarquardt(inputData: LMInput): LMResultInfo {
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val resultInfo = LMResultInfo(0, 0, 0.0,
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0.0, inputData.startParameters, TypeOfConvergence.NoConvergence)
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