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
Predict the value of the target variable given a point.
Predict the value of the target variable given a point.
Draw three predictions from the posterior predictive distribution 1) Mean or MAP estimate Y 2) Y- : The lower error bar estimate (mean - sigma*stdDeviation) 3) Y+ : The upper error bar.
Draw three predictions from the posterior predictive distribution 1) Mean or MAP estimate Y 2) Y- : The lower error bar estimate (mean - sigma*stdDeviation) 3) Y+ : The upper error bar. (mean + sigma*stdDeviation)
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
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.
Returns a prediction with error bars for a test set of indexes and labels.
Returns a prediction with error bars for a test set of indexes and labels. (Index, Actual Value, Prediction, Lower Bar, Higher Bar)
A process which is a multinomial mixture of continuous component processes.
The type of the index set (i.e. Double for time series, DenseVector for GP regression)
The type of the output label
Implementing class of the posterior distribution for the base processes should inherit from ContinuousMixtureRV