F# Data
Deedle
R Provider
F# ChartingGaussianProcess
Nested types and modules
| Type | Description |
| GaussianProcess<'T> | |
| Kernel<'T> | |
| MeanFunction | |
| Observation<'T> | Type for data representation |
Functions and values
| Function or value | Description |
addObservationNoise noiseVariance matrix
Signature: noiseVariance:float option -> matrix:Matrix<float> -> Matrix<float>
|
Add observational noise to a covariance matrix |
covarianceMatrix kernel input1 input2
Signature: kernel:Kernel<'T> -> input1:'T [] -> input2:'T [] -> Matrix<float>
Type parameters: 'T |
Computes covariance matrix between two sets of inputs using a specified covariance function (kernel) |
mvNormalLoglik (...)
Signature: meanVector:Vector<float> -> covarianceMatrix:Matrix<float> -> x:Vector<float> -> float
|
Compute log likelihood of a standard multivariate normal distribution |
plot data gp
Signature: data:seq<Observation<float>> -> gp:GaussianProcess<float> -> GenericChart
|
Displays a Gaussian process regression curve given a set of data points Shows a region of +/- 1 standard deviations from the posterior mean. |
plotRange (timeMin, timeMax) data gp
Signature: (timeMin:float<MeasureOne> * timeMax:float<MeasureOne>) -> data:seq<Observation<float>> -> gp:GaussianProcess<float> -> GenericChart
|
Displays a Gaussian process regression curve given a set of data points Extrapolates the figure to [timeMin, timeMax] interval. Shows a region of +/- 1 standard deviations from the posterior mean. |
