fittingData parameter renamed to trainData

gyrdym · Oct 25, 2019 · 7dc6c4e · 7dc6c4e
1 parent 6050899
commit 7dc6c4e
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Showing 3 changed files with 9 additions and 8 deletions.
diff --git a/lib/src/classifier/knn_classifier/_helpers/create_knn_classifier.dart b/lib/src/classifier/knn_classifier/_helpers/create_knn_classifier.dart
@@ -11,16 +11,16 @@ import 'package:ml_linalg/distance.dart';
 import 'package:ml_linalg/dtype.dart';
 
 KnnClassifier createKnnClassifier(
-    DataFrame fittingData,
+    DataFrame trainData,
     String targetName,
     int k,
     KernelType kernelType,
     Distance distance,
     DType dtype,
 ) {
-  validateTrainData(fittingData, [targetName]);
+  validateTrainData(trainData, [targetName]);
 
-  final splits = featuresTargetSplit(fittingData,
+  final splits = featuresTargetSplit(trainData,
     targetNames: [targetName],
   ).toList();
 

diff --git a/lib/src/classifier/knn_classifier/knn_classifier.dart b/lib/src/classifier/knn_classifier/knn_classifier.dart
@@ -21,14 +21,14 @@ import 'package:ml_linalg/dtype.dart';
 abstract class KnnClassifier implements Assessable, Classifier {
   /// Parameters:
   ///
-  /// [fittingData] Labelled observations, among which will be searched [k]
+  /// [trainData] Labelled observations, among which will be searched [k]
   /// nearest to the given unlabelled observations neighbours. Must contain
   /// [targetName] column.
   ///
   /// [targetName] A string, that serves as a name of the column, that contains
   /// labels (or outcomes).
   ///
-  /// [k] a number of nearest neighbours to be found among [fittingData]
+  /// [k] a number of nearest neighbours to be found among [trainData]
   ///
   /// [kernel] a type of a kernel function, that will be used to predict an
   /// outcome for a new observation
@@ -40,13 +40,13 @@ abstract class KnnClassifier implements Assessable, Classifier {
   /// affect performance or accuracy of the computations. Default value is
   /// [DType.float32]
   factory KnnClassifier(
-      DataFrame fittingData,
+      DataFrame trainData,
       String targetName,
       int k,
       {
         KernelType kernel = KernelType.gaussian,
         Distance distance = Distance.euclidean,
         DType dtype = DType.float32,
       }
-  ) => createKnnClassifier(fittingData, targetName, k, kernel, distance, dtype);
+  ) => createKnnClassifier(trainData, targetName, k, kernel, distance, dtype);
 }
diff --git a/lib/src/classifier/knn_classifier/knn_classifier_factory_impl.dart b/lib/src/classifier/knn_classifier/knn_classifier_factory_impl.dart
@@ -9,7 +9,8 @@ class KnnClassifierFactoryImpl implements KnnClassifierFactory {
   const KnnClassifierFactoryImpl();
 
   @override
-  KnnClassifier create(String targetName,
+  KnnClassifier create(
+      String targetName,
       List<num> classLabels,
       Kernel kernel,
       KnnSolver solver,