io.github.mandar2812.dynaml.models
SecondOrderProcessModel
Related Doc:
package models
Underlying covariance function of the Gaussian Processes.
Convert from the underlying data structure to Seq[(I, Y)] where I is the index set of the GP and Y is the value/label type.
Convert from the underlying data structure to Seq[(I, Y)] where I is the index set of the GP and Y is the value/label type.
The training data
The training data
Mean Function: Takes a member of the index set (input) and returns the corresponding mean of the distribution corresponding to input.
Predict the value of the target variable given a point.
Predict the value of the target variable given a point.
Calculates posterior predictive distribution for a particular set of test data points.
Calculates posterior predictive distribution for a particular set of test data points.
A Sequence or Sequence like data structure storing the values of the input patters.
Convert from the underlying data structure to Seq[I] where I is the index set of the GP
Convert from the underlying data structure to Seq[I] where I is the index set of the GP
Processes which can be specified by upto second order statistics i.e. mean and covariance
The underlying data structure storing the training & test data.
The type of the index set (i.e. Double for time series, DenseVector for GP regression)
The type of the output label
The type returned by the kernel function.
The data structure holding the kernel/covariance matrix
Implementing class of the posterior distribution