updated code in attempt to fix pca problem

aeon/clustering/feature_based/_r_cluster.py

@@ -418,30 +418,35 @@ def _fit(self, X, y=None):
         pca = PCA().fit(X_std)
         optimal_dimensions = np.argmax(pca.explained_variance_ratio_ < 0.01)
 
-        self.__optimal_dimensions = max(
+        self.optimal_dimensions = max(
             1, min(optimal_dimensions, X_std.shape[0], X_std.shape[1])
         )
 
         pca = PCA(n_components=optimal_dimensions, random_state=self.random_state)
         transformed_data_pca = pca.fit_transform(X_std)
-        self.__estimator = KMeans(
+        self.estimator = KMeans(
             n_clusters=self.n_clusters,
             random_state=self.random_state,
             n_init=self.n_init,
         )
-        self.__estimator.fit(transformed_data_pca)
-        self.labels_ = self.__estimator.labels_
+        self.estimator.fit(transformed_data_pca)
+        self.labels_ = self.estimator.labels_
 
     def _predict(self, X, y=None) -> np.ndarray:
         parameters = self._get_parameterised_data(X)
 
         transformed_data = self._get_transformed_data(X=X, parameters=parameters)
 
         X_std = self.scaler.fit_transform(transformed_data)
-        pca = PCA(n_components=self.__optimal_dimensions, random_state=self.random_state)
+        if (self.optimal_dimensions > max(1, min(X_std.shape[0], X_std.shape[1]))):
+            raise ValueError(
+                f"optimal dimensions={self.optimal_dimensions} must be between 0 and "
+                f"min(n_samples, n_features)={min(X_std.shape[0], X_std.shape[1])}"
+            )
+        pca = PCA(n_components=self.optimal_dimensions, random_state=self.random_state)
         transformed_data_pca = pca.fit_transform(X_std)
 
-        return self.__estimator.predict(transformed_data_pca)
+        return self.estimator.predict(transformed_data_pca)
 
     def _fit_predict(self, X, y=None) -> np.ndarray:
         parameters = self._get_parameterised_data(X)
@@ -457,13 +462,13 @@ def _fit_predict(self, X, y=None) -> np.ndarray:
         )
         pca = PCA(n_components=optimal_dimensions, random_state=self.random_state)
         transformed_data_pca = pca.fit_transform(X_std)
-        self.__estimator = KMeans(
+        self.estimator = KMeans(
             n_clusters=self.n_clusters,
             random_state=self.random_state,
             n_init=self.n_init,
         )
-        Y = self.__estimator.fit_predict(transformed_data_pca)
-        self.labels_ = self.__estimator.labels_
+        Y = self.estimator.fit_predict(transformed_data_pca)
+        self.labels_ = self.estimator.labels_
         return Y
 
     @classmethod