LevenbergMarquardtConstrained (FSharp.Stats) | FSharp.Stats

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<float>` - upperBound : `Vector<float>` - xData : `float[]` - yData : `float[]` - Returns: `Vector<float>`	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<float>` upperBound : `Vector<float>` xData : `float[]` yData : `float[]` Returns: `Vector<float>` 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<float>` - upperBound : `Vector<float>` - xData : `float[]` - yData : `float[]` - Returns: `ResizeArray<Vector<float>>`	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<float>` upperBound : `Vector<float>` xData : `float[]` yData : `float[]` Returns: `ResizeArray<Vector<float>>` 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<float>` - upperBound : `Vector<float>` - xData : `float[]` - yData : `float[]` - Returns: `Vector<float> * 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<float>` upperBound : `Vector<float>` xData : `float[]` yData : `float[]` Returns: `Vector<float> * 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[] array`	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[] array` Example