replace single-letter type variable names with more descriptive ones

common/src/main/java/net/caffeinemc/mods/sodium/client/render/chunk/compile/estimation/Average1DEstimator.java

@@ -4,7 +4,7 @@
 
 import java.util.Locale;
 
-public abstract class Average1DEstimator<C> extends Estimator<C, Average1DEstimator.Value<C>, Average1DEstimator.ValueBatch<C>, Void, Long, Average1DEstimator.Average<C>> {
+public abstract class Average1DEstimator<Category> extends Estimator<Category, Average1DEstimator.Value<Category>, Average1DEstimator.ValueBatch<Category>, Void, Long, Average1DEstimator.Average<Category>> {
     private final float newDataRatio;
     private final long initialEstimate;
 
@@ -39,7 +39,7 @@ public float getAverage() {
     }
 
     @Override
-    protected ValueBatch<C> createNewDataBatch() {
+    protected ValueBatch<Category> createNewDataBatch() {
         return new ValueBatch<>();
     }
 
@@ -79,11 +79,11 @@ public String toString() {
     }
 
     @Override
-    protected Average<C> createNewModel() {
+    protected Average<Category> createNewModel() {
         return new Average<>(this.newDataRatio, this.initialEstimate);
     }
 
-    public Long predict(C category) {
+    public Long predict(Category category) {
         return super.predict(category, null);
     }
 }

common/src/main/java/net/caffeinemc/mods/sodium/client/render/chunk/compile/estimation/Estimator.java

@@ -5,22 +5,22 @@
 /**
  * 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 <C> The type of the category key
- * @param <D> A data point contains a category and one piece of data
- * @param <B> A data batch contains multiple data points
- * @param <I> The input to the model
- * @param <O> The output of the model
- * @param <M> The model that is used to predict values
+ * @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
  */
 public abstract class Estimator<
-        C,
-        D extends Estimator.DataPoint<C>,
-        B extends Estimator.DataBatch<D>,
-        I,
-        O,
-        M extends Estimator.Model<I, O, B, M>> {
-    protected final Map<C, M> models = createMap();
-    protected final Map<C, B> batches = createMap();
+        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<D> {
         void addDataPoint(D input);
@@ -38,13 +38,13 @@ protected interface Model<I, O, B, M extends Model<I, O, B, M>> {
         O predict(I input);
     }
 
-    protected abstract B createNewDataBatch();
+    protected abstract Batch createNewDataBatch();
 
-    protected abstract M createNewModel();
+    protected abstract Model createNewModel();
 
-    protected abstract <T> Map<C, T> createMap();
+    protected abstract <T> Map<Category, T> createMap();
 
-    public void addData(D data) {
+    public void addData(Point data) {
         var category = data.category();
         var batch = this.batches.get(category);
         if (batch == null) {
@@ -54,7 +54,7 @@ public void addData(D data) {
         batch.addDataPoint(data);
     }
 
-    private M ensureModel(C category) {
+    private Model ensureModel(Category category) {
         var model = this.models.get(category);
         if (model == null) {
             model = this.createNewModel();
@@ -77,11 +77,11 @@ public void updateModels() {
         });
     }
 
-    public O predict(C category, I input) {
+    public Output predict(Category category, Input input) {
         return this.ensureModel(category).predict(input);
     }
 
-    public String toString(C category) {
+    public String toString(Category category) {
         var model = this.models.get(category);
         if (model == null) {
             return "-";

common/src/main/java/net/caffeinemc/mods/sodium/client/render/chunk/compile/estimation/Linear2DEstimator.java

@@ -4,7 +4,7 @@
 
 import java.util.Locale;
 
-public abstract class Linear2DEstimator<C> extends Estimator<C, Linear2DEstimator.DataPair<C>, Linear2DEstimator.LinearRegressionBatch<C>, Long, Long, Linear2DEstimator.LinearFunction<C>> {
+public abstract class Linear2DEstimator<Category> extends Estimator<Category, Linear2DEstimator.DataPair<Category>, Linear2DEstimator.LinearRegressionBatch<Category>, Long, Long, Linear2DEstimator.LinearFunction<Category>> {
     private final float newDataRatio;
     private final int initialSampleTarget;
     private final long initialOutput;
@@ -34,7 +34,7 @@ public void reset() {
     }
 
     @Override
-    protected LinearRegressionBatch<C> createNewDataBatch() {
+    protected LinearRegressionBatch<Category> createNewDataBatch() {
         return new LinearRegressionBatch<>();
     }
 
@@ -137,7 +137,7 @@ public String toString() {
     }
 
     @Override
-    protected LinearFunction<C> createNewModel() {
+    protected LinearFunction<Category> createNewModel() {
         return new LinearFunction<>(this.newDataRatio, this.initialSampleTarget, this.initialOutput);
     }
 }