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AdaptiveInterpolator by Jeff Gaston

AdaptiveInterpolater was created to predict the value of a function based on example data. It's essentially an R tree that predicts each value to be the average of the points in its box.

Interesting attributes of AdaptiveInterpolator:

  1. It adjusts how far to split a node based on how good the predictions are.
  • When predictions are good, then noise is small, and there are more splits and better predictions.

  • When predictions are bad, then noise is large, and there are fewer splits so we can better estimate the noise.

  1. It evaluates dimension values lazily
  • This can help when computing the value of a specific dimension is expensive.

  • AdaptiveInterpolator can handle thousands of dimensions and tens of thousands of datapoints on a phone in under a second

  1. Each split is a vote of several dimensions
  • This can help when individual dimensions are independently noisy.

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Performs numerical interpolation for multidimensional data

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