Joel Therrien
a5fe856857
Changed the covariates to be more clever with how they produce the different splits. In the future (not yet implemented) a clever GroupDifferentiator could update the current score calculation based just on how many rows moved from one hand to the other. There were a few other changes as well; TreeTrainer#growTree now accepts a Random as a parameter which is used throughout the entire growing process. This means it's now theoretically possible to grow trees using a seed, so that results can be fully reproducible.
47 lines
1.2 KiB
Java
47 lines
1.2 KiB
Java
package ca.joeltherrien.randomforest;
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import ca.joeltherrien.randomforest.covariates.Covariate;
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import java.util.HashMap;
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import java.util.List;
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import java.util.Map;
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public class Row<Y> extends CovariateRow {
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private final Y response;
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public Row(final Covariate.Value[] valueArray, final int id, final Y response){
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super(valueArray, id);
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this.response = response;
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}
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public Y getResponse() {
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return this.response;
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}
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@Override
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public String toString() {
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return "Row " + this.getId();
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}
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public static <Y> Row<Y> createSimple(Map<String, String> simpleMap, List<Covariate> covariateList, int id, final Y response){
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final Covariate.Value[] valueArray = new Covariate.Value[covariateList.size()];
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final Map<String, Covariate> covariateMap = new HashMap<>();
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covariateList.forEach(covariate -> covariateMap.put(covariate.getName(), covariate));
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simpleMap.forEach((name, valueStr) -> {
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final Covariate covariate = covariateMap.get(name);
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if(covariate != null){ // happens often in tests where we experiment with adding / removing covariates
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valueArray[covariate.getIndex()] = covariate.createValue(valueStr);
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}
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});
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return new Row<Y>(valueArray, id, response);
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}
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}
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