Switch code to storing Covariate.Value using arrays instead of Maps

src/main/java/ca/joeltherrien/randomforest/CovariateRow.java

@@ -12,14 +12,13 @@ import java.util.Map;
 @RequiredArgsConstructor
 public class CovariateRow implements Serializable {
 
-    private final Map<String, Covariate.Value> valueMap;
+    private final Covariate.Value[] valueArray;
 
     @Getter
     private final int id;
 
-    public Covariate.Value<?> getCovariateValue(String name){
+    public Covariate.Value<?> getCovariateValue(Covariate covariate){
-        return valueMap.get(name);
+        return valueArray[covariate.getIndex()];
-
     }
 
     @Override
@@ -28,18 +27,21 @@ public class CovariateRow implements Serializable {
     }
 
     public static CovariateRow createSimple(Map<String, String> simpleMap, List<Covariate> covariateList, int id){
-        final Map<String, Covariate.Value> valueMap = new HashMap<>();
+        final Covariate.Value[] valueArray = new Covariate.Value[covariateList.size()];
         final Map<String, Covariate> covariateMap = new HashMap<>();
 
         covariateList.forEach(covariate -> covariateMap.put(covariate.getName(), covariate));
 
         simpleMap.forEach((name, valueStr) -> {
-            if(covariateMap.containsKey(name)){
+            final Covariate covariate = covariateMap.get(name);
-                valueMap.put(name, covariateMap.get(name).createValue(valueStr));
+
+            if(covariate != null){ // happens often in tests where we experiment with adding / removing covariates
+                valueArray[covariate.getIndex()] = covariate.createValue(valueStr);
             }
+
             });
 
-        return new CovariateRow(valueMap, id);
+        return new CovariateRow(valueArray, id);
     }
 
 }

src/main/java/ca/joeltherrien/randomforest/DataLoader.java

@@ -37,15 +37,15 @@ public class DataLoader {
 
         int id = 1;
         for(final CSVRecord record : parser){
-            final Map<String, Covariate.Value> covariateValueMap = new HashMap<>();
+            final Covariate.Value[] valueArray = new Covariate.Value[covariates.size()];
 
             for(final Covariate<?> covariate : covariates){
-                covariateValueMap.put(covariate.getName(), covariate.createValue(record.get(covariate.getName())));
+                valueArray[covariate.getIndex()] = covariate.createValue(record.get(covariate.getName()));
             }
 
             final Y y = responseLoader.parse(record);
 
-            dataset.add(new Row<>(covariateValueMap, id++, y));
+            dataset.add(new Row<>(valueArray, id++, y));
 
         }
 

src/main/java/ca/joeltherrien/randomforest/Main.java

@@ -1,9 +1,6 @@
 package ca.joeltherrien.randomforest;
 
-import ca.joeltherrien.randomforest.covariates.BooleanCovariateSettings;
+import ca.joeltherrien.randomforest.covariates.*;
-import ca.joeltherrien.randomforest.covariates.Covariate;
-import ca.joeltherrien.randomforest.covariates.FactorCovariateSettings;
-import ca.joeltherrien.randomforest.covariates.NumericCovariateSettings;
 import ca.joeltherrien.randomforest.responses.competingrisk.*;
 import ca.joeltherrien.randomforest.responses.competingrisk.combiner.CompetingRiskFunctionCombiner;
 import ca.joeltherrien.randomforest.responses.competingrisk.combiner.alternative.CompetingRiskListCombiner;
@@ -17,6 +14,7 @@ import com.fasterxml.jackson.databind.node.ObjectNode;
 import com.fasterxml.jackson.databind.node.TextNode;
 
 import java.io.*;
+import java.util.ArrayList;
 import java.util.List;
 import java.util.stream.Collectors;
 
@@ -35,8 +33,7 @@ public class Main {
         final File settingsFile = new File(args[0]);
         final Settings settings = Settings.load(settingsFile);
 
-        final List<Covariate> covariates = settings.getCovariates().stream()
+        final List<Covariate> covariates = settings.getCovariates();
-                .map(cs -> cs.build()).collect(Collectors.toList());
 
         if(args[1].equalsIgnoreCase("train")){
             final List<Row> dataset = DataLoader.loadData(covariates, settings.getResponseLoader(), settings.getTrainingDataLocation());
@@ -168,7 +165,7 @@ public class Main {
         yVarSettings.set("name", new TextNode("y"));
 
         return Settings.builder()
-                .covariates(Utils.easyList(
+                .covariateSettings(Utils.easyList(
                         new NumericCovariateSettings("x1"),
                         new BooleanCovariateSettings("x2"),
                         new FactorCovariateSettings("x3", Utils.easyList("cat", "mouse", "dog"))

src/main/java/ca/joeltherrien/randomforest/Row.java

@@ -3,14 +3,16 @@ package ca.joeltherrien.randomforest;
 
 import ca.joeltherrien.randomforest.covariates.Covariate;
 
+import java.util.HashMap;
+import java.util.List;
 import java.util.Map;
 
 public class Row<Y> extends CovariateRow {
 
 	private final Y response;
 
-	public Row(Map<String, Covariate.Value> valueMap, int id, Y response){
+	public Row(final Covariate.Value[] valueArray, final int id, final Y response){
-	    super(valueMap, id);
+	    super(valueArray, id);
 	    this.response = response;
     }
 
@@ -24,6 +26,20 @@ public class Row<Y> extends CovariateRow {
 		return "Row " + this.getId();
 	}
 
+	public static <Y> Row<Y> createSimple(Map<String, String> simpleMap, List<Covariate> covariateList, int id, final Y response){
+		final Covariate.Value[] valueArray = new Covariate.Value[covariateList.size()];
+		final Map<String, Covariate> covariateMap = new HashMap<>();
 
+		covariateList.forEach(covariate -> covariateMap.put(covariate.getName(), covariate));
+
+		simpleMap.forEach((name, valueStr) -> {
+			final Covariate covariate = covariateMap.get(name);
+			if(covariate != null){ // happens often in tests where we experiment with adding / removing covariates
+				valueArray[covariate.getIndex()] = covariate.createValue(valueStr);
+			}
+		});
+
+		return new Row<Y>(valueArray, id, response);
+	}
 	
 }

src/main/java/ca/joeltherrien/randomforest/Settings.java

@@ -1,5 +1,6 @@
 package ca.joeltherrien.randomforest;
 
+import ca.joeltherrien.randomforest.covariates.Covariate;
 import ca.joeltherrien.randomforest.covariates.CovariateSettings;
 import ca.joeltherrien.randomforest.responses.competingrisk.*;
 import ca.joeltherrien.randomforest.responses.competingrisk.combiner.CompetingRiskFunctionCombiner;
@@ -195,7 +196,7 @@ public class Settings {
     private ObjectNode groupDifferentiatorSettings = new ObjectNode(JsonNodeFactory.instance);
     private ObjectNode treeCombinerSettings = new ObjectNode(JsonNodeFactory.instance);
 
-    private List<CovariateSettings> covariates = new ArrayList<>();
+    private List<CovariateSettings> covariateSettings = new ArrayList<>();
     private ObjectNode yVarSettings = new ObjectNode(JsonNodeFactory.instance);
 
     // number of covariates to randomly try
@@ -227,7 +228,7 @@ public class Settings {
         //mapper.enableDefaultTyping();
 
         // Jackson can struggle with some types of Lists, such as that returned by the useful List.of(...)
-        this.covariates = new ArrayList<>(this.covariates);
+        this.covariateSettings = new ArrayList<>(this.covariateSettings);
 
         mapper.writeValue(file, this);
     }
@@ -260,4 +261,14 @@ public class Settings {
         return getResponseCombinerConstructor(type).apply(treeCombinerSettings);
     }
 
+    @JsonIgnore
+    public List<Covariate> getCovariates(){
+        final List<CovariateSettings> covariateSettingsList = this.getCovariateSettings();
+        final List<Covariate> covariates = new ArrayList<>(covariateSettingsList.size());
+        for(int i = 0; i < covariateSettingsList.size(); i++){
+            covariates.add(covariateSettingsList.get(i).build(i));
+        }
+        return covariates;
+    }
+
 }

src/main/java/ca/joeltherrien/randomforest/covariates/BooleanCovariate.java

@@ -13,6 +13,9 @@ public final class BooleanCovariate implements Covariate<Boolean>{
     @Getter
     private final String name;
 
+    @Getter
+    private final int index;
+
     private final BooleanSplitRule splitRule = new BooleanSplitRule(); // there's only one possible rule for BooleanCovariates.
 
     @Override

src/main/java/ca/joeltherrien/randomforest/covariates/BooleanCovariateSettings.java

@@ -12,7 +12,7 @@ public final class BooleanCovariateSettings extends CovariateSettings<Boolean> {
     }
 
     @Override
-    public BooleanCovariate build() {
+    public BooleanCovariate build(int index) {
-        return new BooleanCovariate(name);
+        return new BooleanCovariate(name, index);
     }
 }

src/main/java/ca/joeltherrien/randomforest/covariates/Covariate.java

@@ -12,6 +12,8 @@ public interface Covariate<V> extends Serializable {
 
     String getName();
 
+    int getIndex();
+
     Collection<? extends SplitRule<V>> generateSplitRules(final List<Value<V>> data, final int number);
 
     Value<V> createValue(V value);
@@ -54,7 +56,7 @@ public interface Covariate<V> extends Serializable {
 
 
             for(final Row<Y> row : rows) {
-                final Value<V> value = (Value<V>) row.getCovariateValue(getParent().getName());
+                final Value<V> value = (Value<V>) row.getCovariateValue(getParent());
 
                 if(value.isNA()){
                     missingValueRows.add(row);
@@ -76,7 +78,7 @@ public interface Covariate<V> extends Serializable {
         }
 
         default boolean isLeftHand(CovariateRow row, final double probabilityNaLeftHand){
-            final Value<V> value = (Value<V>) row.getCovariateValue(getParent().getName());
+            final Value<V> value = (Value<V>) row.getCovariateValue(getParent());
 
             if(value.isNA()){
                 return ThreadLocalRandom.current().nextDouble() <= probabilityNaLeftHand;

src/main/java/ca/joeltherrien/randomforest/covariates/CovariateSettings.java

@@ -28,5 +28,5 @@ public abstract class CovariateSettings<V> {
         this.name = name;
     }
 
-    public abstract Covariate<V> build();
+    public abstract Covariate<V> build(int index);
 }

src/main/java/ca/joeltherrien/randomforest/covariates/FactorCovariate.java

@@ -1,20 +1,27 @@
 package ca.joeltherrien.randomforest.covariates;
 
 import lombok.EqualsAndHashCode;
+import lombok.Getter;
 
 import java.util.*;
 import java.util.concurrent.ThreadLocalRandom;
 
 public final class FactorCovariate implements Covariate<String>{
 
+    @Getter
     private final String name;
+
+    @Getter
+    private final int index;
+
     private final Map<String, FactorValue> factorLevels;
     private final FactorValue naValue;
     private final int numberOfPossiblePairings;
 
 
-    public FactorCovariate(final String name, List<String> levels){
+    public FactorCovariate(final String name, final int index, List<String> levels){
         this.name = name;
+        this.index = index;
         this.factorLevels = new HashMap<>();
 
         for(final String level : levels){
@@ -33,10 +40,6 @@ public final class FactorCovariate implements Covariate<String>{
     }
 
 
-    @Override
-    public String getName() {
-        return name;
-    }
 
     @Override
     public Set<FactorSplitRule> generateSplitRules(List<Value<String>> data, int number) {

src/main/java/ca/joeltherrien/randomforest/covariates/FactorCovariateSettings.java

@@ -18,7 +18,7 @@ public final class FactorCovariateSettings extends CovariateSettings<String> {
     }
 
     @Override
-    public FactorCovariate build() {
+    public FactorCovariate build(int index) {
-        return new FactorCovariate(name, levels);
+        return new FactorCovariate(name, index, levels);
     }
 }

src/main/java/ca/joeltherrien/randomforest/covariates/NumericCovariate.java

@@ -16,6 +16,9 @@ public final class NumericCovariate implements Covariate<Double>{
     @Getter
     private final String name;
 
+    @Getter
+    private final int index;
+
     @Override
     public Collection<NumericSplitRule> generateSplitRules(List<Value<Double>> data, int number) {
 

src/main/java/ca/joeltherrien/randomforest/covariates/NumericCovariateSettings.java

@@ -12,7 +12,7 @@ public final class NumericCovariateSettings extends CovariateSettings<Double> {
     }
 
     @Override
-    public NumericCovariate build() {
+    public NumericCovariate build(int index) {
-        return new NumericCovariate(name);
+        return new NumericCovariate(name, index);
     }
 }

src/main/java/ca/joeltherrien/randomforest/tree/TreeTrainer.java

@@ -113,7 +113,7 @@ public class TreeTrainer<Y, O> {
                     .generateSplitRules(
                             data
                                     .stream()
-                                    .map(row -> row.getCovariateValue(covariate.getName()))
+                                    .map(row -> row.getCovariateValue(covariate))
                                     .collect(Collectors.toList())
                             , numberToTry);
 

src/test/java/ca/joeltherrien/randomforest/TestSavingLoading.java

@@ -53,7 +53,7 @@ public class TestSavingLoading {
         yVarSettings.set("delta", new TextNode("status"));
 
         return Settings.builder()
-                .covariates(Utils.easyList(
+                .covariateSettings(Utils.easyList(
                         new NumericCovariateSettings("ageatfda"),
                         new BooleanCovariateSettings("idu"),
                         new BooleanCovariateSettings("black"),
@@ -87,14 +87,10 @@ public class TestSavingLoading {
                 , covariates, 1);
     }
 
-    public List<Covariate> getCovariates(Settings settings){
-        return settings.getCovariates().stream().map(covariateSettings -> covariateSettings.build()).collect(Collectors.toList());
-    }
-
     @Test
     public void testSavingLoading() throws IOException, ClassNotFoundException {
         final Settings settings = getSettings();
-        final List<Covariate> covariates = getCovariates(settings);
+        final List<Covariate> covariates = settings.getCovariates();
         final List<Row<CompetingRiskResponse>> dataset = DataLoader.loadData(covariates, settings.getResponseLoader(), settings.getTrainingDataLocation());
 
         final File directory = new File(settings.getSaveTreeLocation());

src/test/java/ca/joeltherrien/randomforest/competingrisk/TestCompetingRisk.java

@@ -55,7 +55,7 @@ public class TestCompetingRisk {
         yVarSettings.set("delta", new TextNode("status"));
 
         return Settings.builder()
-                .covariates(Utils.easyList(
+                .covariateSettings(Utils.easyList(
                         new NumericCovariateSettings("ageatfda"),
                         new BooleanCovariateSettings("idu"),
                         new BooleanCovariateSettings("black"),
@@ -79,9 +79,6 @@ public class TestCompetingRisk {
                 .build();
     }
 
-    public List<Covariate> getCovariates(Settings settings){
-        return settings.getCovariates().stream().map(covariateSettings -> covariateSettings.build()).collect(Collectors.toList());
-    }
 
     public CovariateRow getPredictionRow(List<Covariate> covariates){
         return CovariateRow.createSimple(Utils.easyMap(
@@ -96,12 +93,12 @@ public class TestCompetingRisk {
     public void testSingleTree() throws IOException {
         final Settings settings = getSettings();
         settings.setTrainingDataLocation("src/test/resources/wihs.bootstrapped.csv");
-        settings.setCovariates(Utils.easyList(
+        settings.setCovariateSettings(Utils.easyList(
                 new BooleanCovariateSettings("idu"),
                 new BooleanCovariateSettings("black")
                 )); // by only using BooleanCovariates (only one split rule) we can guarantee identical results with randomForestSRC on one tree.
 
-        final List<Covariate> covariates = getCovariates(settings);
+        final List<Covariate> covariates = settings.getCovariates();
 
         final List<Row<CompetingRiskResponse>> dataset = DataLoader.loadData(covariates, settings.getResponseLoader(), settings.getTrainingDataLocation());
 
@@ -154,7 +151,7 @@ public class TestCompetingRisk {
         settings.setNumberOfSplits(0);
         settings.setTrainingDataLocation("src/test/resources/wihs.bootstrapped2.csv");
 
-        final List<Covariate> covariates = getCovariates(settings);
+        final List<Covariate> covariates = settings.getCovariates();
 
         final List<Row<CompetingRiskResponse>> dataset = DataLoader.loadData(covariates, settings.getResponseLoader(), settings.getTrainingDataLocation());
 
@@ -199,12 +196,12 @@ public class TestCompetingRisk {
     @Test
     public void testLogRankSingleGroupDifferentiatorTwoBooleans() throws IOException {
         final Settings settings = getSettings();
-        settings.setCovariates(Utils.easyList(
+        settings.setCovariateSettings(Utils.easyList(
                 new BooleanCovariateSettings("idu"),
                 new BooleanCovariateSettings("black")
         ));
 
-        final List<Covariate> covariates = getCovariates(settings);
+        final List<Covariate> covariates = settings.getCovariates();
 
         final List<Row<CompetingRiskResponse>> dataset = DataLoader.loadData(covariates, settings.getResponseLoader(), settings.getTrainingDataLocation());
 
@@ -254,7 +251,7 @@ public class TestCompetingRisk {
     public void verifyDataset() throws IOException {
         final Settings settings = getSettings();
 
-        final List<Covariate> covariates = getCovariates(settings);
+        final List<Covariate> covariates = settings.getCovariates();
 
         final List<Row<CompetingRiskResponse>> dataset = DataLoader.loadData(covariates, settings.getResponseLoader(), settings.getTrainingDataLocation());
 
@@ -291,7 +288,7 @@ public class TestCompetingRisk {
         final Settings settings = getSettings();
         settings.setNtree(300); // results are too variable at 100
 
-        final List<Covariate> covariates = getCovariates(settings);
+        final List<Covariate> covariates = settings.getCovariates();
         final List<Row<CompetingRiskResponse>> dataset = DataLoader.loadData(covariates, settings.getResponseLoader(), settings.getTrainingDataLocation());
         final ForestTrainer<CompetingRiskResponse, CompetingRiskFunctions, CompetingRiskFunctions> forestTrainer = new ForestTrainer<>(settings, dataset, covariates);
         final Forest<CompetingRiskFunctions, CompetingRiskFunctions> forest = forestTrainer.trainSerial();

src/test/java/ca/joeltherrien/randomforest/competingrisk/TestCompetingRiskErrorRateCalculator.java

@@ -1,6 +1,7 @@
 package ca.joeltherrien.randomforest.competingrisk;
 
 import ca.joeltherrien.randomforest.Row;
+import ca.joeltherrien.randomforest.covariates.Covariate;
 import ca.joeltherrien.randomforest.responses.competingrisk.*;
 import ca.joeltherrien.randomforest.tree.Forest;
 import ca.joeltherrien.randomforest.utils.MathFunction;
@@ -54,10 +55,10 @@ public class TestCompetingRiskErrorRateCalculator {
         final CompetingRiskResponse response4 = new CompetingRiskResponse(1, 3.0);
 
         final List<Row<CompetingRiskResponse>> dataset = Utils.easyList(
-                new Row<>(Collections.emptyMap(), 1, response1),
+                new Row<>(new Covariate.Value[]{}, 1, response1),
-                new Row<>(Collections.emptyMap(), 2, response2),
+                new Row<>(new Covariate.Value[]{}, 2, response2),
-                new Row<>(Collections.emptyMap(), 3, response3),
+                new Row<>(new Covariate.Value[]{}, 3, response3),
-                new Row<>(Collections.emptyMap(), 4, response4)
+                new Row<>(new Covariate.Value[]{}, 4, response4)
         );
 
         final double[] mortalityOneArray = new double[]{1, 4, 3, 9};

src/test/java/ca/joeltherrien/randomforest/covariates/FactorCovariateTest.java

@@ -58,7 +58,7 @@ public class FactorCovariateTest {
     private FactorCovariate createTestCovariate(){
         final List<String> levels = Utils.easyList("DOG", "CAT", "MOUSE");
 
-        return new FactorCovariate("pet", levels);
+        return new FactorCovariate("pet", 0, levels);
     }
 
 

src/test/java/ca/joeltherrien/randomforest/csv/TestLoadingCSV.java

@@ -38,7 +38,7 @@ public class TestLoadingCSV {
 
         final Settings settings = Settings.builder()
                 .trainingDataLocation(filename)
-                .covariates(
+                .covariateSettings(
                         Utils.easyList(new NumericCovariateSettings("x1"),
                                 new FactorCovariateSettings("x2", Utils.easyList("dog", "cat", "mouse")),
                                 new BooleanCovariateSettings("x3"))
@@ -46,8 +46,7 @@ public class TestLoadingCSV {
                 .yVarSettings(yVarSettings)
                 .build();
 
-        final List<Covariate> covariates = settings.getCovariates().stream()
+        final List<Covariate> covariates = settings.getCovariates();
-                .map(cs -> cs.build()).collect(Collectors.toList());
 
 
         final DataLoader.ResponseLoader loader = settings.getResponseLoader();
@@ -56,46 +55,50 @@ public class TestLoadingCSV {
     }
 
     @Test
-    public void verifyLoadingNormal() throws IOException {
+    public void verifyLoadingNormal(final List<Covariate> covariates) throws IOException {
         final List<Row<Double>> data = loadData("src/test/resources/testCSV.csv");
 
-        assertData(data);
+        assertData(data, covariates);
     }
 
     @Test
-    public void verifyLoadingGz() throws IOException {
+    public void verifyLoadingGz(final List<Covariate> covariates) throws IOException {
         final List<Row<Double>> data = loadData("src/test/resources/testCSV.csv.gz");
 
-        assertData(data);
+        assertData(data, covariates);
     }
 
 
-    private void assertData(final List<Row<Double>> data){
+    private void assertData(final List<Row<Double>> data, final List<Covariate> covariates){
+        final Covariate x1 = covariates.get(0);
+        final Covariate x2 = covariates.get(0);
+        final Covariate x3 = covariates.get(0);
+
         assertEquals(4, data.size());
 
         Row<Double> row = data.get(0);
         assertEquals(5.0, (double)row.getResponse());
-        assertEquals(3.0, row.getCovariateValue("x1").getValue());
+        assertEquals(3.0, row.getCovariateValue(x1).getValue());
-        assertEquals("mouse", row.getCovariateValue("x2").getValue());
+        assertEquals("mouse", row.getCovariateValue(x2).getValue());
-        assertEquals(true, row.getCovariateValue("x3").getValue());
+        assertEquals(true, row.getCovariateValue(x3).getValue());
 
         row = data.get(1);
         assertEquals(2.0, (double)row.getResponse());
-        assertEquals(1.0, row.getCovariateValue("x1").getValue());
+        assertEquals(1.0, row.getCovariateValue(x1).getValue());
-        assertEquals("dog", row.getCovariateValue("x2").getValue());
+        assertEquals("dog", row.getCovariateValue(x2).getValue());
-        assertEquals(false, row.getCovariateValue("x3").getValue());
+        assertEquals(false, row.getCovariateValue(x3).getValue());
 
         row = data.get(2);
         assertEquals(9.0, (double)row.getResponse());
-        assertEquals(1.5, row.getCovariateValue("x1").getValue());
+        assertEquals(1.5, row.getCovariateValue(x1).getValue());
-        assertEquals("cat", row.getCovariateValue("x2").getValue());
+        assertEquals("cat", row.getCovariateValue(x2).getValue());
-        assertEquals(true, row.getCovariateValue("x3").getValue());
+        assertEquals(true, row.getCovariateValue(x3).getValue());
 
         row = data.get(3);
         assertEquals(-3.0, (double)row.getResponse());
-        assertTrue(row.getCovariateValue("x1").isNA());
+        assertTrue(row.getCovariateValue(x1).isNA());
-        assertTrue(row.getCovariateValue("x2").isNA());
+        assertTrue(row.getCovariateValue(x2).isNA());
-        assertTrue(row.getCovariateValue("x3").isNA());
+        assertTrue(row.getCovariateValue(x3).isNA());
     }
 
 }

src/test/java/ca/joeltherrien/randomforest/settings/TestPersistence.java

@@ -31,7 +31,7 @@ public class TestPersistence {
         yVarSettings.set("name", new TextNode("y"));
 
         final Settings settingsOriginal =  Settings.builder()
-                .covariates(Utils.easyList(
+                .covariateSettings(Utils.easyList(
                         new NumericCovariateSettings("x1"),
                         new BooleanCovariateSettings("x2"),
                         new FactorCovariateSettings("x3", Utils.easyList("cat", "mouse", "dog"))

src/test/java/ca/joeltherrien/randomforest/workshop/TrainForest.java

@@ -27,22 +27,22 @@ public class TrainForest {
 
         final List<Covariate> covariateList = new ArrayList<>(p);
         for(int j =0; j < p; j++){
-            final NumericCovariate covariate = new NumericCovariate("x"+j);
+            final NumericCovariate covariate = new NumericCovariate("x"+j, j);
             covariateList.add(covariate);
         }
 
         for(int i=0; i<n; i++){
             double y = 0.0;
-            final Map<String, Covariate.Value> map = new HashMap<>();
+            final Covariate.Value[] valueArray = new Covariate.Value[covariateList.size()];
 
             for(final Covariate covariate : covariateList) {
                 final double x = random.nextDouble();
                 y += x;
 
-                map.put(covariate.getName(), covariate.createValue(x));
+                valueArray[covariate.getIndex()] = covariate.createValue(y);
             }
 
-            data.add(i, new Row<>(map, i, y));
+            data.add(i, new Row<>(valueArray, i, y));
 
             if(y < minY){
                 minY = y;

src/test/java/ca/joeltherrien/randomforest/workshop/TrainSingleTree.java

@@ -18,15 +18,13 @@ import java.util.stream.DoubleStream;
 public class TrainSingleTree {
 
 	public static void main(String[] args) {
-		System.out.println("Hello world!");
-
         final Random random = new Random(123);
 
         final int n = 1000;
         final List<Row<Double>> trainingSet = new ArrayList<>(n);
 
-        final Covariate<Double> x1Covariate = new NumericCovariate("x1");
+        final Covariate<Double> x1Covariate = new NumericCovariate("x1", 0);
-        final Covariate<Double> x2Covariate = new NumericCovariate("x2");
+        final Covariate<Double> x2Covariate = new NumericCovariate("x2", 1);
 
         final List<Covariate.Value<Double>> x1List = DoubleStream
                 .generate(() -> random.nextDouble()*10.0)
@@ -100,17 +98,21 @@ public class TrainSingleTree {
 	public static Row<Double> generateRow(Covariate.Value<Double> x1, Covariate.Value<Double> x2, int id){
 	    double y = generateResponse(x1.getValue(), x2.getValue());
 
-	    final Map<String, Covariate.Value> map = Utils.easyMap("x1", x1, "x2", x2);
+	    final Covariate.Value[] valueArray = new Covariate.Value[2];
+	    valueArray[0] = x1;
+	    valueArray[1] = x2;
 
-        return new Row<>(map, id, y);
+        return new Row<>(valueArray, id, y);
 
     }
 
 
     public static CovariateRow generateCovariateRow(Covariate.Value x1, Covariate.Value x2, int id){
-        final Map<String, Covariate.Value> map = Utils.easyMap("x1", x1, "x2", x2);
+        final Covariate.Value[] valueArray = new Covariate.Value[2];
+        valueArray[0] = x1;
+        valueArray[1] = x2;
 
-        return new CovariateRow(map, id);
+        return new CovariateRow(valueArray, id);
 
     }
 

src/test/java/ca/joeltherrien/randomforest/workshop/TrainSingleTreeFactor.java

@@ -28,9 +28,9 @@ public class TrainSingleTreeFactor {
         final int n = 10000;
         final List<Row<Double>> trainingSet = new ArrayList<>(n);
 
-        final Covariate<Double> x1Covariate = new NumericCovariate("x1");
+        final Covariate<Double> x1Covariate = new NumericCovariate("x1", 0);
-        final Covariate<Double> x2Covariate = new NumericCovariate("x2");
+        final Covariate<Double> x2Covariate = new NumericCovariate("x2", 1);
-        final FactorCovariate x3Covariate = new FactorCovariate("x3", Utils.easyList("cat", "dog", "mouse"));
+        final FactorCovariate x3Covariate = new FactorCovariate("x3", 2, Utils.easyList("cat", "dog", "mouse"));
 
         final List<Covariate.Value<Double>> x1List = DoubleStream
                 .generate(() -> random.nextDouble()*10.0)
@@ -128,17 +128,25 @@ public class TrainSingleTreeFactor {
 	public static Row<Double> generateRow(Covariate.Value<Double> x1, Covariate.Value<Double> x2, Covariate.Value<String> x3, int id){
 	    double y = generateResponse(x1.getValue(), x2.getValue(), x3.getValue());
 
-	    final Map<String, Covariate.Value> map = Utils.easyMap("x1", x1, "x2", x2);
+        final Covariate.Value[] valueArray = new Covariate.Value[3];
+        valueArray[0] = x1;
+        valueArray[1] = x2;
+        valueArray[2] = x3;
 
-        return new Row<>(map, id, y);
+	    //final Map<String, Covariate.Value> map = Utils.easyMap("x1", x1, "x2", x2); // Missing x3?
+
+        return new Row<>(valueArray, id, y);
 
     }
 
 
     public static CovariateRow generateCovariateRow(Covariate.Value x1, Covariate.Value x2, Covariate.Value x3, int id){
-        final Map<String, Covariate.Value> map = Utils.easyMap("x1", x1, "x2", x2, "x3", x3);
+        final Covariate.Value[] valueArray = new Covariate.Value[3];
+        valueArray[0] = x1;
+        valueArray[1] = x2;
+        valueArray[2] = x3;
 
-        return new CovariateRow(map, id);
+        return new CovariateRow(valueArray, id);
 
     }