RLPark 1.0.0
Reinforcement Learning Framework in Java
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00001 package rlpark.plugin.rltoys.algorithms.representations.discretizer.partitions; 00002 00003 import rlpark.plugin.rltoys.algorithms.representations.discretizer.Discretizer; 00004 import rlpark.plugin.rltoys.math.ranges.Range; 00005 00006 public class WrappedPartitionFactory extends AbstractPartitionFactory { 00007 private static final long serialVersionUID = -5578336702743121475L; 00008 00009 public WrappedPartitionFactory(Range... ranges) { 00010 super(ranges); 00011 } 00012 00013 @Override 00014 public Discretizer createDiscretizer(int inputIndex, int resolution, int tilingIndex, int nbTilings) { 00015 Range range = ranges[inputIndex]; 00016 double offset = range.length() / resolution / nbTilings; 00017 double shift = computeShift(offset, tilingIndex, inputIndex); 00018 double min = range.min() + shift; 00019 double max = range.max() + shift; 00020 return new WrappedPartition(min, max, resolution); 00021 } 00022 }