TuningMode (bristlecone)

TuningMode Module

Namespace: Bristlecone.Optimisation
Assembly: Bristlecone.dll
Parent Module: MonteCarlo

Functions and values

Function or value	Description
`computeCovariance matrix` Full Usage: `computeCovariance matrix` Parameters: matrix : `Matrix<float>` Returns: `Matrix<float>`	Calculates the covariance of a given matrix matrix : `Matrix<float>` Returns: `Matrix<float>`
`covariance tuneInterval weighting remaining history scale` Full Usage: `covariance tuneInterval weighting remaining history scale` Parameters: tuneInterval : `int` weighting : `float` remaining : `int` history : `(float * float[]) seq` scale : `Matrix<float>` Returns: `Matrix<float>`	Tune previously observed covariance based on most recent period tuneInterval : `int` weighting : `float` remaining : `int` history : `(float * float[]) seq` scale : `Matrix<float>` Returns: `Matrix<float>`
`covarianceAllTime weighting history scale` Full Usage: `covarianceAllTime weighting history scale` Parameters: weighting : `float` history : `(float * float[]) seq` scale : `Matrix<float>` Returns: `Matrix<float>`	Tune previously observed covariance based on all time weighting : `float` history : `(float * float[]) seq` scale : `Matrix<float>` Returns: `Matrix<float>`
`covarianceFromBounds n domain random` Full Usage: `covarianceFromBounds n domain random` Parameters: n : `int` domain : `Domain` random : `Random` Returns: `Matrix<float>`	n : `int` domain : `Domain` random : `Random` Returns: `Matrix<float>`
`covarianceFromSigmas n random sigmas` Full Usage: `covarianceFromSigmas n random sigmas` Parameters: n : `int` random : `Random` sigmas : `float[]` Returns: `Matrix<float>`	n : `int` random : `Random` sigmas : `float[]` Returns: `Matrix<float>`
`covarianceMatrix parameterCount scale` Full Usage: `covarianceMatrix parameterCount scale` Parameters: parameterCount : `int` scale : `float` Returns: `Matrix<float>`	Generate a covariance matrix parameterCount : `int` scale : `float` Returns: `Matrix<float>`
`covarianceOnly tuneInterval weighting remaining history (arg5, arg6)` Full Usage: `covarianceOnly tuneInterval weighting remaining history (arg5, arg6)` Parameters: tuneInterval : `int` weighting : `float` remaining : `int` history : `(float * float[]) seq` arg4 : `Matrix<float>` arg5 : `'a` Returns: `Matrix<float> * 'a`	tuneInterval : `int` weighting : `float` remaining : `int` history : `(float * float[]) seq` arg4 : `Matrix<float>` arg5 : `'a` Returns: `Matrix<float> * 'a`
`defaultCovariance n` Full Usage: `defaultCovariance n` Parameters: n : `int` Returns: `Matrix<float>`	The starting covariance matrix for parameters in a multivariate distribution n : `int` Returns: `Matrix<float>`
`dual tuneInterval weighting remaining history (arg5, arg6)` Full Usage: `dual tuneInterval weighting remaining history (arg5, arg6)` Parameters: tuneInterval : `int` weighting : `float` remaining : `int` history : `(float * float[]) seq` arg4 : `Matrix<float>` arg5 : `float` Returns: `Matrix<float> * float`	tuneInterval : `int` weighting : `float` remaining : `int` history : `(float * float[]) seq` arg4 : `Matrix<float>` arg5 : `float` Returns: `Matrix<float> * float`
`dualTotalHistory tuneInterval weighting remaining history (arg5, arg6)` Full Usage: `dualTotalHistory tuneInterval weighting remaining history (arg5, arg6)` Parameters: tuneInterval : `int` weighting : `float` remaining : `int` history : `(float * float[]) seq` arg4 : `Matrix<float>` arg5 : `float` Returns: `Matrix<float> * float`	tuneInterval : `int` weighting : `float` remaining : `int` history : `(float * float[]) seq` arg4 : `Matrix<float>` arg5 : `float` Returns: `Matrix<float> * float`
`none arg1 arg2 factors` Full Usage: `none arg1 arg2 factors` Parameters: arg0 : `'a` arg1 : `'b` factors : `'c` Returns: `'c`	arg0 : `'a` arg1 : `'b` factors : `'c` Returns: `'c`
`samplesToMatrix samples` Full Usage: `samplesToMatrix samples` Parameters: samples : `float[][]` Returns: `Matrix<float>`	Parameters to matrix samples : `float[][]` Returns: `Matrix<float>`
`scaleFactor tuneInterval remainingIterations history scale` Full Usage: `scaleFactor tuneInterval remainingIterations history scale` Parameters: tuneInterval : `int` remainingIterations : `int` history : `'a seq` scale : `float` Returns: `float`	Modifies a scale factor depending on tuneInterval : `int` remainingIterations : `int` history : `'a seq` scale : `float` Returns: `float`
`scaleOnly tuneInterval remaining history (arg4, arg5)` Full Usage: `scaleOnly tuneInterval remaining history (arg4, arg5)` Parameters: tuneInterval : `int` remaining : `int` history : `'a seq` arg3 : `'b` arg4 : `float` Returns: `'b * float`	tuneInterval : `int` remaining : `int` history : `'a seq` arg3 : `'b` arg4 : `float` Returns: `'b * float`
`tuneCovariance weight recentCovariance oldCovariance` Full Usage: `tuneCovariance weight recentCovariance oldCovariance` Parameters: weight : `float` recentCovariance : `Matrix<float>` oldCovariance : `Matrix<float>` Returns: `Matrix<float>`	Tunes a covariance matrix based on recent samples. weight : `float` recentCovariance : `Matrix<float>` oldCovariance : `Matrix<float>` Returns: `Matrix<float>`
`tuneScale scale accRate` Full Usage: `tuneScale scale accRate` Parameters: scale : `float` accRate : `float` Returns: `float`	Find an appropriate scale factor for a standard deviation and its acceptance rate. scale : `float` accRate : `float` Returns: `float`