prefix estimator type variables with "T" and use single-letters for c…

…ategory in classes that don't have other type variables
douira · Feb 22, 2025 · 554685f · 554685f
1 parent 68bec84
commit 554685f
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Showing 3 changed files with 39 additions and 39 deletions.
diff --git a/...net/caffeinemc/mods/sodium/client/render/chunk/compile/estimation/Average1DEstimator.java b/...net/caffeinemc/mods/sodium/client/render/chunk/compile/estimation/Average1DEstimator.java
@@ -4,7 +4,7 @@
 
 import java.util.Locale;
 
-public abstract class Average1DEstimator<Category> extends Estimator<Category, Average1DEstimator.Value<Category>, Average1DEstimator.ValueBatch<Category>, Void, Long, Average1DEstimator.Average<Category>> {
+public abstract class Average1DEstimator<C> extends Estimator<C, Average1DEstimator.Value<C>, Average1DEstimator.ValueBatch<C>, Void, Long, Average1DEstimator.Average<C>> {
     private final float newDataRatio;
     private final long initialEstimate;
 
@@ -39,11 +39,11 @@ public float getAverage() {
     }
 
     @Override
-    protected ValueBatch<Category> createNewDataBatch() {
+    protected ValueBatch<C> createNewDataBatch() {
         return new ValueBatch<>();
     }
 
-    protected static class Average<ModelCategory> implements Estimator.Model<Void, Long, ValueBatch<ModelCategory>, Average<ModelCategory>> {
+    protected static class Average<C> implements Estimator.Model<Void, Long, ValueBatch<C>, Average<C>> {
         private final float newDataRatio;
         private boolean hasRealData = false;
         private float average;
@@ -54,7 +54,7 @@ public Average(float newDataRatio, float initialValue) {
         }
 
         @Override
-        public Average<ModelCategory> update(ValueBatch<ModelCategory> batch) {
+        public Average<C> update(ValueBatch<C> batch) {
             if (batch.count > 0) {
                 if (this.hasRealData) {
                     this.average = MathUtil.exponentialMovingAverage(this.average, batch.getAverage(), this.newDataRatio);
@@ -79,11 +79,11 @@ public String toString() {
     }
 
     @Override
-    protected Average<Category> createNewModel() {
+    protected Average<C> createNewModel() {
         return new Average<>(this.newDataRatio, this.initialEstimate);
     }
 
-    public Long predict(Category category) {
+    public Long predict(C category) {
         return super.predict(category, null);
     }
 }
diff --git a/...ain/java/net/caffeinemc/mods/sodium/client/render/chunk/compile/estimation/Estimator.java b/...ain/java/net/caffeinemc/mods/sodium/client/render/chunk/compile/estimation/Estimator.java
@@ -5,46 +5,46 @@
 /**
  * This generic model learning class that can be used to estimate values based on a set of data points. It performs batch-wise model updates. The actual data aggregation and model updates are delegated to the implementing classes. The estimator stores multiple models in a map, one for each category.
  *
- * @param <Category> The type of the category key
- * @param <Point> A data point contains a category and one piece of data
- * @param <Batch> A data batch contains multiple data points
- * @param <Input> The input to the model
- * @param <Output> The output of the model
- * @param <Model> The model that is used to predict values
+ * @param <TCategory> The type of the category key
+ * @param <TPoint> A data point contains a category and one piece of data
+ * @param <TBatch> A data batch contains multiple data points
+ * @param <TInput> The input to the model
+ * @param <TOutput> The output of the model
+ * @param <TModel> The model that is used to predict values
  */
 public abstract class Estimator<
-        Category,
-        Point extends Estimator.DataPoint<Category>,
-        Batch extends Estimator.DataBatch<Point>,
-        Input,
-        Output,
-        Model extends Estimator.Model<Input, Output, Batch, Model>> {
-    protected final Map<Category, Model> models = createMap();
-    protected final Map<Category, Batch> batches = createMap();
-
-    protected interface DataBatch<BatchPoint> {
-        void addDataPoint(BatchPoint input);
+        TCategory,
+        TPoint extends Estimator.DataPoint<TCategory>,
+        TBatch extends Estimator.DataBatch<TPoint>,
+        TInput,
+        TOutput,
+        TModel extends Estimator.Model<TInput, TOutput, TBatch, TModel>> {
+    protected final Map<TCategory, TModel> models = createMap();
+    protected final Map<TCategory, TBatch> batches = createMap();
+
+    protected interface DataBatch<TBatchPoint> {
+        void addDataPoint(TBatchPoint input);
 
         void reset();
     }
 
-    protected interface DataPoint<PointCategory> {
-        PointCategory category();
+    protected interface DataPoint<TPointCategory> {
+        TPointCategory category();
     }
 
-    protected interface Model<ModelInput, ModelOutput, ModelBatch, ModelSelf extends Model<ModelInput, ModelOutput, ModelBatch, ModelSelf>> {
-        ModelSelf update(ModelBatch batch);
+    protected interface Model<TModelInput, TModelOutput, TModelBatch, TModelSelf extends Model<TModelInput, TModelOutput, TModelBatch, TModelSelf>> {
+        TModelSelf update(TModelBatch batch);
 
-        ModelOutput predict(ModelInput input);
+        TModelOutput predict(TModelInput input);
     }
 
-    protected abstract Batch createNewDataBatch();
+    protected abstract TBatch createNewDataBatch();
 
-    protected abstract Model createNewModel();
+    protected abstract TModel createNewModel();
 
-    protected abstract <T> Map<Category, T> createMap();
+    protected abstract <T> Map<TCategory, T> createMap();
 
-    public void addData(Point data) {
+    public void addData(TPoint data) {
         var category = data.category();
         var batch = this.batches.get(category);
         if (batch == null) {
@@ -54,7 +54,7 @@ public void addData(Point data) {
         batch.addDataPoint(data);
     }
 
-    private Model ensureModel(Category category) {
+    private TModel ensureModel(TCategory category) {
         var model = this.models.get(category);
         if (model == null) {
             model = this.createNewModel();
@@ -77,11 +77,11 @@ public void updateModels() {
         });
     }
 
-    public Output predict(Category category, Input input) {
-        return (Output) this.ensureModel(category).predict(input);
+    public TOutput predict(TCategory category, TInput input) {
+        return this.ensureModel(category).predict(input);
     }
 
-    public String toString(Category category) {
+    public String toString(TCategory category) {
         var model = this.models.get(category);
         if (model == null) {
             return "-";

diff --git a/.../net/caffeinemc/mods/sodium/client/render/chunk/compile/estimation/Linear2DEstimator.java b/.../net/caffeinemc/mods/sodium/client/render/chunk/compile/estimation/Linear2DEstimator.java
@@ -4,7 +4,7 @@
 
 import java.util.Locale;
 
-public abstract class Linear2DEstimator<Category> extends Estimator<Category, Linear2DEstimator.DataPair<Category>, Linear2DEstimator.LinearRegressionBatch<Category>, Long, Long, Linear2DEstimator.LinearFunction<Category>> {
+public abstract class Linear2DEstimator<C> extends Estimator<C, Linear2DEstimator.DataPair<C>, Linear2DEstimator.LinearRegressionBatch<C>, Long, Long, Linear2DEstimator.LinearFunction<C>> {
     private final float newDataRatio;
     private final int initialSampleTarget;
     private final long initialOutput;
@@ -34,7 +34,7 @@ public void reset() {
     }
 
     @Override
-    protected LinearRegressionBatch<Category> createNewDataBatch() {
+    protected LinearRegressionBatch<C> createNewDataBatch() {
         return new LinearRegressionBatch<>();
     }
 
@@ -137,7 +137,7 @@ public String toString() {
     }
 
     @Override
-    protected LinearFunction<Category> createNewModel() {
+    protected LinearFunction<C> createNewModel() {
         return new LinearFunction<>(this.newDataRatio, this.initialSampleTarget, this.initialOutput);
     }
 }