Computes Predictions for the Exemplar-based Models (GCM, EBM)

Usage

ebm_cpp(
  criterion,
  features,
  w,
  r,
  q,
  lambda,
  b,
  wf,
  lastLearnTrial,
  firstOutTrial,
  init,
  has_criterion,
  similarity,
  ismultiplicative
)

Arguments

criterion: numeric vector with experienced criterion
features: numeric matrix with feature criterion
w: numeric vector of weights (model parameter)
r: order of Minkowski distance metic (model parameter)
q: relation between similarity and distance (model parameter)
lambda: sensitivity (model parameter)
b: bias parameter vector for classification (model parameter), must be NA for judgments
wf: weight vector with a weight for each feature combination
lastLearnTrial: integer last trial of learning phase
firstOutTrial: integer first trial of output, starting the predictions later
init: value for the initial trials
has_criterion: vector where a criterion is present
similarity: A string, the similarity function
ismultiplicative: A number (0 or 1), 1 means the combination of exemplars is multiplicative, i.e. multiplicative exemplar model

Examples

# none