EndConditions (bristlecone) | bristlecone

bristlecone

EndConditions Module

Namespace: Bristlecone.Optimisation
Assembly: Bristlecone.dll

Nested modules

Modules	Description
Profiles

Functions and values

Function or value	Description
`acceptanceRateGate min max interval log results iteration` Full Usage: `acceptanceRateGate min max interval log results iteration` Parameters: min : `float` max : `float` interval : `int<MeasureProduct<iteration, MeasureOne>>` log : `LogEvent -> unit` results : `Solution list` iteration : `int<MeasureProduct<iteration, MeasureOne>>` Returns: `OptimStopReason`	Stops when acceptance rate is not within the defined range. Used to avoid stopping when not mixing. min : `float` max : `float` interval : `int<MeasureProduct<iteration, MeasureOne>>` log : `LogEvent -> unit` results : `Solution list` iteration : `int<MeasureProduct<iteration, MeasureOne>>` Returns: `OptimStopReason`
`atIteration iteration arg2 currentIteration` Full Usage: `atIteration iteration arg2 currentIteration` Parameters: iteration : `int<MeasureProduct<iteration, MeasureOne>>` arg1 : `Solution list` currentIteration : `int<MeasureProduct<iteration, MeasureOne>>` Returns: `OptimStopReason`	End the optimisation procedure when a minimum number of iterations is exceeded. iteration : `int<MeasureProduct<iteration, MeasureOne>>` arg1 : `Solution list` currentIteration : `int<MeasureProduct<iteration, MeasureOne>>` Returns: `OptimStopReason`
`convergence thin chainCount` Full Usage: `convergence thin chainCount` Parameters: thin : `int` chainCount : `int` Returns: `EndCondition`	Convergence of results using the Gelman-Rubin Rhat statistic. TODO Keep track of model-engine-subject ID for unique composition only. `thin` - Only test for convergence at multiples of the following intervals (when all chains are ready). `chainCount` - The number of chains to test for convergence. This makes the agent wait until results for all chains are in. thin : `int` chainCount : `int` Returns: `EndCondition`
`defaultTolerance` Full Usage: `defaultTolerance` Returns: `float<MeasureProduct<-logL, MeasureOne>>`	Returns: `float<MeasureProduct<-logL, MeasureOne>>`
`improvementCount count interval results iteration` Full Usage: `improvementCount count interval results iteration` Parameters: count : `int` interval : `int<MeasureProduct<iteration, MeasureOne>>` results : `Solution list` iteration : `int<MeasureProduct<iteration, MeasureOne>>` Returns: `OptimStopReason`	count : `int` interval : `int<MeasureProduct<iteration, MeasureOne>>` results : `Solution list` iteration : `int<MeasureProduct<iteration, MeasureOne>>` Returns: `OptimStopReason`
`movementFloorGate movementFloor interval results iteration` Full Usage: `movementFloorGate movementFloor interval results iteration` Parameters: movementFloor : `float<MeasureProduct<-logL, MeasureOne>>` interval : `int<MeasureProduct<iteration, MeasureOne>>` results : `Solution list` iteration : `int<MeasureProduct<iteration, MeasureOne>>` Returns: `OptimStopReason`	movementFloor : `float<MeasureProduct<-logL, MeasureOne>>` interval : `int<MeasureProduct<iteration, MeasureOne>>` results : `Solution list` iteration : `int<MeasureProduct<iteration, MeasureOne>>` Returns: `OptimStopReason`
`noImprovement results iteration` Full Usage: `noImprovement results iteration` Parameters: results : `Solution list` iteration : `int<MeasureProduct<iteration, MeasureOne>>` Returns: `OptimStopReason`	results : `Solution list` iteration : `int<MeasureProduct<iteration, MeasureOne>>` Returns: `OptimStopReason`
`stationarySquaredJumpDistance log` Full Usage: `stationarySquaredJumpDistance log` Parameters: log : `LogEvent -> unit` Returns: `EndCondition`	True if there is no significant slope in mean squared jumping distances (MSJD), binned per 200 iterations and a regression of five bins. log : `LogEvent -> unit` Returns: `EndCondition`
`stationarySquaredJumpDistance' fixedBin pointsRequired slopeTol log results i` Full Usage: `stationarySquaredJumpDistance' fixedBin pointsRequired slopeTol log results i` Parameters: fixedBin : `int` pointsRequired : `int` slopeTol : `float` log : `LogEvent -> unit` results : `Solution list` i : `int<MeasureProduct<iteration, MeasureOne>>` Returns: `OptimStopReason`	An `EndCondition` that calculates that segregates the most recent n results into five bins, and runs a regression to detect a temporal variation in the mean squared jumping distance (MSJD). The significance of the slope coefficient of a linear regression is assessed to determine if the MSJD is increasing through time for every parameter sequentially: if all p-values are >0.1, then the `EndCondition` is true. fixedBin : `int` pointsRequired : `int` slopeTol : `float` log : `LogEvent -> unit` results : `Solution list` i : `int<MeasureProduct<iteration, MeasureOne>>` Returns: `OptimStopReason`
`stoppedImproving chains minimums` Full Usage: `stoppedImproving chains minimums` Parameters: chains : `int` minimums : `Solution list` Returns: `bool`	Given a list of solutions, which are ordered most recent first, returns `true` if there are at least `chains` recent results, and the change within the recent results is no more than `tolerance`. chains : `int` minimums : `Solution list` Returns: `bool`