🔥 Remove class replaced

src/LDA/LDA_Classifications/LDA_Classifications_Datasets.py

@@ -111,41 +111,4 @@ def forward_and_predict(self) -> float:
         return accuracy
 
 
-class LDAClassificationsSKLearns(LDA):
-    """
-    LDAClassificationsSKLearns class for demonstrating LDA on a dataset from sklearn.
-    Inherits from LDA.
-
-    Attributes:
-        dataset (Tuple[NDArray[Float64], NDArray[Any]]): The dataset to be used.
-    """
-
-    def __init__(self, k: int, dataset: Tuple[NDArray[Float64], NDArray[Any]]):
-        """
-        Initializes the LDAClassificationsSKLearns class with the specified number of components and dataset.
-
-        Args:
-            k (int): Number of components to keep.
-            dataset (Tuple[NDArray[Float64], NDArray[Any]]): The dataset to be used.
-        """
-        super().__init__(k)
-        self.dataset: Tuple[NDArray[Float64], NDArray[Any]] = dataset
 
-    def forward_and_predict(self) -> float:
-        """
-        Splits the data, fits the LDA model, and makes predictions.
-
-        Returns:
-            float: The accuracy of the model.
-        """
-        X_train: NDArray[Float64]
-        X_test: NDArray[Float64]
-        y_train: NDArray[Any]
-        y_test: NDArray[Any]
-
-        X_train, X_test, y_train, y_test = train_test_split(
-            self.dataset[0], self.dataset[1], test_size=0.3, random_state=42)
-        self.fit(X_train, y_train)
-        y_pred: NDArray[Any] = self.predict(X_test)
-        accuracy: float = accuracy_score(y_test, y_pred)
-        return accuracy