Discretize candidates with specified indices.
Discretize candidates with specified indices.
candidate with values in [0, 1]
Map that specifies the indices of discrete parameters and their numbers of discrete values
candidate with the specified discrete values
Draw candidates from the distributions along each dimension in the space
Draw candidates from the distributions along each dimension in the space
the number of candidates to draw
Searches and returns n points in the space.
Searches and returns n points in the space.
The number of points to find
The found points
Searches and returns n points in the space, given prior observations from past data sets.
Searches and returns n points in the space, given prior observations from past data sets.
The number of points to find
Observations made prior to searching, from past data sets (mean-centered)
The found points
Searches and returns n points in the space, given prior observations from this data set and past data sets.
Searches and returns n points in the space, given prior observations from this data set and past data sets.
The number of points to find
Observations made prior to searching, from this data set (not mean-centered)
Observations made prior to searching, from past data sets (mean-centered)
The found points
Returns the last model trained during search
Returns the last model trained during search
the last model
Produces the next candidate, given the last.
Produces the next candidate, given the last. In this case, we fit a Gaussian Process to the previous observations, and use it to predict the value of uniformly-drawn candidate points. The candidate with the best predicted evaluation is chosen.
the last candidate
the last observed value
the next candidate
Handler callback for each observation.
Handler callback for each observation. In this case, we record the observed point and values.
the observed point in the space
the observed value
Handler callback for each observation in the prior data.
Handler callback for each observation in the prior data. In this case, we record the observed point and values.
the observed point in the space
the observed value
Initial random sample of parameters to investigate.
Having evaluation for the certain parameters sample next point to evaluate.
Selects the best candidate according to the predicted values, where "best" is determined by the given transformation.
Selects the best candidate according to the predicted values, where "best" is determined by the given transformation. In the case of EI, we always search for the max; In the case of CB, we always search for the min.
matrix of candidates
predicted values for each candidate
prediction transformation function
the candidate with the best value
Advanced sampler modeling the efficiency function as a family of Gaussian processes (with integrated kernel parameters) and sampling from it trying to maximize Expected Improvement.