If all three of the conditions in points below are satisfied, this function simply collects the samples from the component models and updates the sample indices to ensure that they are different for different component models:
equal weights for all models,
the same number of samples from each component model
no limit on the number of samples the ensemble is allowed to produce
We need to ensure that the output type ids are distinct for samples from different component models, and have the right data type. A proposal is to paste the input/component model_id together with the output_type_id provided by that model to ensure that they are distinct across models. If the output_type_ids should be numeric, we can do the as.integer(factor(…)) trick to convert these to distinct integers.
If any of the above conditions are not satisfied, this function will throw an error.
If all three of the conditions in points below are satisfied, this function simply collects the samples from the component models and updates the sample indices to ensure that they are different for different component models:
We need to ensure that the output type ids are distinct for samples from different component models, and have the right data type. A proposal is to paste the input/component model_id together with the output_type_id provided by that model to ensure that they are distinct across models. If the output_type_ids should be numeric, we can do the
as.integer(factor(…))trick to convert these to distinct integers.If any of the above conditions are not satisfied, this function will throw an error.