LevenbergMarquardtConstrained (FSharp.Stats)

LevenbergMarquardtConstrained Module

Namespace: FSharp.Stats.Fitting
Assembly: FSharp.Stats.dll
Parent Module: NonLinearRegression

This LevenbergMarquardt implementation supports the usage of box constrains.

Functions and values

Function or value	Description
`estimatedParams model solverOptions lambdaInitial lambdaFactor lowerBound upperBound xData yData` Full Usage: `estimatedParams model solverOptions lambdaInitial lambdaFactor lowerBound upperBound xData yData` Parameters: model : `Model` - solverOptions : `SolverOptions` - lambdaInitial : `float` - lambdaFactor : `float` - lowerBound : `vector` - upperBound : `vector` - xData : `float[]` - yData : `float[]` - Returns: `vector`	Returns a parameter vector as a possible solution for linear least square based nonlinear fitting of a given dataset (xData, yData) with a given model function. model : `Model` solverOptions : `SolverOptions` lambdaInitial : `float` lambdaFactor : `float` lowerBound : `vector` upperBound : `vector` xData : `float[]` yData : `float[]` Returns: `vector` Example
`estimatedParamsVerbose model solverOptions lambdaInitial lambdaFactor lowerBound upperBound xData yData` Full Usage: `estimatedParamsVerbose model solverOptions lambdaInitial lambdaFactor lowerBound upperBound xData yData` Parameters: model : `Model` - solverOptions : `SolverOptions` - lambdaInitial : `float` - lambdaFactor : `float` - lowerBound : `vector` - upperBound : `vector` - xData : `float[]` - yData : `float[]` - Returns: `ResizeArray<vector>`	Returns an collection of parameter vectors as a possible solution for least square based nonlinear fitting of a given dataset (xData, yData) with a given model function. model : `Model` solverOptions : `SolverOptions` lambdaInitial : `float` lambdaFactor : `float` lowerBound : `vector` upperBound : `vector` xData : `float[]` yData : `float[]` Returns: `ResizeArray<vector>` Example
`estimatedParamsWithRSS model solverOptions lambdaInitial lambdaFactor lowerBound upperBound xData yData` Full Usage: `estimatedParamsWithRSS model solverOptions lambdaInitial lambdaFactor lowerBound upperBound xData yData` Parameters: model : `Model` - solverOptions : `SolverOptions` - lambdaInitial : `float` - lambdaFactor : `float` - lowerBound : `vector` - upperBound : `vector` - xData : `float[]` - yData : `float[]` - Returns: `vector * float`	Returns a parameter vector tupled with its residual sum of squares (RSS) as a possible solution for linear least square based nonlinear fitting of a given dataset (xData, yData) with a given model function. model : `Model` solverOptions : `SolverOptions` lambdaInitial : `float` lambdaFactor : `float` lowerBound : `vector` upperBound : `vector` xData : `float[]` yData : `float[]` Returns: `vector * float` Example
`initialParam xData yData cutoffPercentage` Full Usage: `initialParam xData yData cutoffPercentage` Parameters: xData : `float[]` - yData : `float[]` cutoffPercentage : `float` Returns: `float[]`	Returns an estimate for an initial parameter for the linear least square estimator for a given dataset (xData, yData). The initial estimation is intended for a logistic function. The returned parameters are the max y value, the steepness of the curve and the x value in the middle of the slope. xData : `float[]` yData : `float[]` cutoffPercentage : `float` Returns: `float[]` Example
`initialParamsOverRange xData yData steepnessRange` Full Usage: `initialParamsOverRange xData yData steepnessRange` Parameters: xData : `float[]` - yData : `float[]` - steepnessRange : `float[]` - Returns: `float[][]`	Returns an estimate for an initial parameter for the linear least square estimator for a given dataset (xData, yData). The steepness is given as an array and not estimated. An initial estimate is returned for every given steepness. The initial estimation is intended for a logistic function. xData : `float[]` yData : `float[]` steepnessRange : `float[]` Returns: `float[][]` Example