fix typo haussdorf -> hausdorff

Author: agoscinski

docs/src/references/selection.rst

@@ -80,7 +80,7 @@ Farthest Point Sampling is a common selection technique intended to exploit the
 diversity of the input space.
 
 In FPS, the selection of the first point is made at random or by a separate metric. Each
-subsequent selection is made to maximize the Haussdorf distance, i.e. the minimum
+subsequent selection is made to maximize the Hausdorf distance, i.e. the minimum
 distance between a point and all previous selections. It is common to use the Euclidean
 distance, however other distance metrics may be employed.
 

src/skmatter/_selection.py

@@ -965,11 +965,11 @@ def __init__(
 
     def score(self, X, y=None):
         """
-        Returns the Haussdorf distances of all samples to previous selections
+        Returns the Hausdorff distances of all samples to previous selections
 
         NOTE: This function does not compute the importance score each time it
-        is called, in order to avoid unnecessary computations. The haussdorf
-        distance is updated in :py:func:`self._update_haussdorf`
+        is called, in order to avoid unnecessary computations. The hausdorff
+        distance is updated in :py:func:`self._update_hausdorff`
 
         Parameters
         ----------
@@ -978,9 +978,9 @@ def score(self, X, y=None):
 
         Returns
         -------
-        haussdorf : Haussdorf distances
+        hausdorff : Hausdorff distances
         """
-        return self.haussdorf_
+        return self.hausdorff_
 
     def get_distance(self):
         """
@@ -1002,40 +1002,40 @@ def get_distance(self):
         Returns
         -------
 
-        haussdorf : ndarray of shape (`n_to_select_from_`)
+        hausdorff : ndarray of shape (`n_to_select_from_`)
                      the minimum distance from each point to the set of selected
                      points. once a point is selected, the distance is not updated;
                      the final list will reflect the distances when selected.
 
         """
-        return self.haussdorf_
+        return self.hausdorff_
 
     def get_select_distance(self):
         """
 
         Returns
         -------
 
-        haussdorf_at_select : ndarray of shape (`n_to_select`)
+        hausdorff_at_select : ndarray of shape (`n_to_select`)
                      at the time of selection, the minimum distance from each
                      selected point to the set of previously selected points.
 
         """
         mask = self.get_support(indices=True, ordered=True)
-        return self.haussdorf_at_select_[mask]
+        return self.hausdorff_at_select_[mask]
 
     def _init_greedy_search(self, X, y, n_to_select):
         """
         Initializes the search. Prepares an array to store the selections,
         makes the initial selection (unless provided), and
-        computes the starting haussdorf distances.
+        computes the starting hausdorff distances.
         """
 
         super()._init_greedy_search(X, y, n_to_select)
 
         self.norms_ = (X**2).sum(axis=abs(self._axis - 1))
-        self.haussdorf_ = np.full(X.shape[self._axis], np.inf)
-        self.haussdorf_at_select_ = np.full(X.shape[self._axis], np.inf)
+        self.hausdorff_ = np.full(X.shape[self._axis], np.inf)
+        self.hausdorff_at_select_ = np.full(X.shape[self._axis], np.inf)
 
         if self.initialize == "random":
             random_state = check_random_state(self.random_state)
@@ -1055,8 +1055,8 @@ def _init_greedy_search(self, X, y, n_to_select):
         else:
             raise ValueError("Invalid value of the initialize parameter")
 
-    def _update_haussdorf(self, X, y, last_selected):
-        self.haussdorf_at_select_[last_selected] = self.haussdorf_[last_selected]
+    def _update_hausdorff(self, X, y, last_selected):
+        self.hausdorff_at_select_[last_selected] = self.hausdorff_[last_selected]
 
         # distances of all points to the new point
         if self._axis == 1:
@@ -1068,15 +1068,15 @@ def _update_haussdorf(self, X, y, last_selected):
                 self.norms_ + self.norms_[last_selected] - 2 * X[last_selected] @ X.T
             )
 
-        # update in-place the Haussdorf distance list
-        np.minimum(self.haussdorf_, new_dist, self.haussdorf_)
+        # update in-place the Hausdorff distance list
+        np.minimum(self.hausdorff_, new_dist, self.hausdorff_)
 
     def _update_post_selection(self, X, y, last_selected):
         """
         Saves the most recent selections, increments the counter,
-        and, recomputes haussdorf distances.
+        and, recomputes hausdorff distances.
         """
-        self._update_haussdorf(X, y, last_selected)
+        self._update_hausdorff(X, y, last_selected)
         super()._update_post_selection(X, y, last_selected)
 
 
@@ -1135,11 +1135,11 @@ def __init__(
 
     def score(self, X, y=None):
         """
-        Returns the Haussdorf distances of all samples to previous selections
+        Returns the Hausdorff distances of all samples to previous selections
 
         NOTE: This function does not compute the importance score each time it
-        is called, in order to avoid unnecessary computations. The haussdorf
-        distance is updated in :py:func:`self._update_haussdorf`
+        is called, in order to avoid unnecessary computations. The hausdorff
+        distance is updated in :py:func:`self._update_hausdorff`
 
         Parameters
         ----------
@@ -1148,43 +1148,43 @@ def score(self, X, y=None):
 
         Returns
         -------
-        haussdorf : Haussdorf distances
+        hausdorff : Hausdorff distances
         """
-        return self.haussdorf_
+        return self.hausdorff_
 
     def get_distance(self):
         """
 
         Returns
         -------
 
-        haussdorf : ndarray of shape (`n_to_select_from_`)
+        hausdorff : ndarray of shape (`n_to_select_from_`)
                      the minimum distance from each point to the set of selected
                      points. once a point is selected, the distance is not updated;
                      the final list will reflect the distances when selected.
 
         """
-        return self.haussdorf_
+        return self.hausdorff_
 
     def get_select_distance(self):
         """
 
         Returns
         -------
 
-        haussdorf_at_select : ndarray of shape (`n_to_select`)
+        hausdorff_at_select : ndarray of shape (`n_to_select`)
                      at the time of selection, the minimum distance from each
                      selected point to the set of previously selected points.
 
         """
         mask = self.get_support(indices=True, ordered=True)
-        return self.haussdorf_at_select_[mask]
+        return self.hausdorff_at_select_[mask]
 
     def _init_greedy_search(self, X, y, n_to_select):
         """
         Initializes the search. Prepares an array to store the selections,
         makes the initial selection (unless provided), and
-        computes the starting haussdorf distances.
+        computes the starting hausdorff distances.
         """
 
         super()._init_greedy_search(X, y, n_to_select)
@@ -1205,12 +1205,12 @@ def _init_greedy_search(self, X, y, n_to_select):
             raise ValueError("Invalid value of the initialize parameter")
 
         self.selected_idx_[0] = initialize
-        self.haussdorf_ = np.full(X.shape[self._axis], np.inf)
-        self.haussdorf_at_select_ = np.full(X.shape[self._axis], np.inf)
+        self.hausdorff_ = np.full(X.shape[self._axis], np.inf)
+        self.hausdorff_at_select_ = np.full(X.shape[self._axis], np.inf)
         self._update_post_selection(X, y, self.selected_idx_[0])
 
-    def _update_haussdorf(self, X, y, last_selected):
-        self.haussdorf_at_select_[last_selected] = self.haussdorf_[last_selected]
+    def _update_hausdorff(self, X, y, last_selected):
+        self.hausdorff_at_select_[last_selected] = self.hausdorff_[last_selected]
 
         # distances of all points to the new point
         new_dist = (
@@ -1219,15 +1219,15 @@ def _update_haussdorf(self, X, y, last_selected):
             - 2 * np.take(self.pcovr_distance_, last_selected, axis=self._axis)
         )
 
-        # update in-place the Haussdorf distance list
-        np.minimum(self.haussdorf_, new_dist, self.haussdorf_)
+        # update in-place the Hausdorff distance list
+        np.minimum(self.hausdorff_, new_dist, self.hausdorff_)
 
     def _update_post_selection(self, X, y, last_selected):
         """
         Saves the most recent selections, increments the counter,
-        and, recomputes haussdorf distances.
+        and, recomputes hausdorff distances.
         """
-        self._update_haussdorf(X, y, last_selected)
+        self._update_hausdorff(X, y, last_selected)
         super()._update_post_selection(X, y, last_selected)
 
     def _more_tags(self):

src/skmatter/sample_selection/_voronoi_fps.py

@@ -93,10 +93,10 @@ def __init__(
 
     def score(self, X=None, y=None):
         """
-        Returns the Haussdorf distances of all samples to previous selections
+        Returns the Hausdorff distances of all samples to previous selections
 
         NOTE: This function does not compute the importance score each time it
-        is called, in order to avoid unnecessary computations. The haussdorf
+        is called, in order to avoid unnecessary computations. The hausdorff
         distance is updated in :py:func:`self._update_post_selection`
 
         Parameters
@@ -106,9 +106,9 @@ def score(self, X=None, y=None):
 
         Returns
         -------
-        haussdorf : Haussdorf distances
+        hausdorff : Hausdorff distances
         """
-        return self.haussdorf_
+        return self.hausdorff_
 
     def get_distance(self):
         """
@@ -130,27 +130,27 @@ def get_distance(self):
         Returns
         -------
 
-        haussdorf : ndarray of shape (`n_to_select_from_`)
+        hausdorff : ndarray of shape (`n_to_select_from_`)
                      the minimum distance from each point to the set of selected
                      points. once a point is selected, the distance is not updated;
                      the final list will reflect the distances when selected.
 
         """
-        return self.haussdorf_
+        return self.hausdorff_
 
     def get_select_distance(self):
         """
 
         Returns
         -------
 
-        haussdorf_at_select : ndarray of shape (`n_to_select`)
+        hausdorff_at_select : ndarray of shape (`n_to_select`)
                      at the time of selection, the minimum distance from each
                      selected point to the set of previously selected points.
 
         """
         mask = self.get_support(indices=True, ordered=True)
-        return self.haussdorf_at_select_[mask]
+        return self.hausdorff_at_select_[mask]
 
     def _init_greedy_search(self, X, y, n_to_select):
         """
@@ -233,8 +233,8 @@ def _init_greedy_search(self, X, y, n_to_select):
             raise ValueError("Invalid value of the initialize parameter")
 
         self.selected_idx_[0] = initialize
-        self.haussdorf_ = np.full(X.shape[self._axis], np.inf)
-        self.haussdorf_at_select_ = np.full(X.shape[self._axis], np.inf)
+        self.hausdorff_ = np.full(X.shape[self._axis], np.inf)
+        self.hausdorff_at_select_ = np.full(X.shape[self._axis], np.inf)
         self._update_post_selection(X, y, self.selected_idx_[0])
 
     def _continue_greedy_search(self, X, y, n_to_select):
@@ -264,7 +264,7 @@ def _get_active(self, X, last_selected):
         S are the selected points from before this iteration;
         X are the candidates;
         The logic here is that we want to check if d(XL) can be smaller than
-        min(d(X,S)) (which is stored in self.haussdorf_)
+        min(d(X,S)) (which is stored in self.hausdorff_)
         now, if a point belongs to the Voronoi cell of S then
         min(d(X,S_i))=d(X,S). Triangle inequality implies that
         d(S,L) < |d(X,S) + d(L,X)| so we just need to check if
@@ -284,15 +284,15 @@ def _get_active(self, X, last_selected):
             # calculation in a single block
 
             active_points = np.where(
-                self.dSL_[self.vlocation_of_idx] < self.haussdorf_
+                self.dSL_[self.vlocation_of_idx] < self.hausdorff_
             )[0]
 
             return active_points
 
     def _update_post_selection(self, X, y, last_selected):
         """
         Saves the most recently selected feature, increments the feature counter
-        and update the haussdorf distances
+        and update the hausdorff distances
         Let:
         L is the last point selected;
         S are the selected points from before this iteration;
@@ -303,7 +303,7 @@ def _update_post_selection(self, X, y, last_selected):
         the distances between L and all the points in the dataset.
         """
 
-        self.haussdorf_at_select_[last_selected] = self.haussdorf_[last_selected]
+        self.hausdorff_at_select_[last_selected] = self.hausdorff_[last_selected]
         active_points = self._get_active(X, last_selected)
 
         if len(active_points) > 0:
@@ -314,7 +314,7 @@ def _update_post_selection(self, X, y, last_selected):
                     - 2 * X[last_selected] @ X.T
                 )
             else:
-                self.new_dist_ = self.haussdorf_.copy()
+                self.new_dist_ = self.hausdorff_.copy()
 
                 self.new_dist_[active_points] = (
                     self.norms_[active_points]
@@ -323,9 +323,9 @@ def _update_post_selection(self, X, y, last_selected):
                 )
                 self.new_dist_[last_selected] = 0
 
-            updated_points = np.where(self.new_dist_ < self.haussdorf_)[0]
+            updated_points = np.where(self.new_dist_ < self.hausdorff_)[0]
             np.minimum(
-                self.haussdorf_, self.new_dist_, self.haussdorf_, casting="unsafe"
+                self.hausdorff_, self.new_dist_, self.hausdorff_, casting="unsafe"
             )
         else:
             updated_points = np.array([])

tests/test_feature_simple_fps.py

@@ -49,7 +49,7 @@ def test_initialize(self):
 
     def test_get_distances(self):
         """
-        This test checks that the haussdorf distances are returnable after fitting
+        This test checks that the hausdorff distances are returnable after fitting
         """
         selector = FPS(n_to_select=7)
         selector.fit(self.X)

tests/test_sample_simple_fps.py

@@ -52,7 +52,7 @@ def test_initialize(self):
 
     def test_get_distances(self):
         """
-        This test checks that the haussdorf distances are returnable after fitting
+        This test checks that the hausdorff distances are returnable after fitting
         """
         selector = FPS(n_to_select=1)
         selector.fit(self.X)

tests/test_voronoi_fps.py

@@ -95,7 +95,7 @@ def test_switching_point(self):
 
     def test_get_distances(self):
         """
-        This test checks that the haussdorf distances are returnable after fitting
+        This test checks that the hausdorff distances are returnable after fitting
         """
         selector = VoronoiFPS(n_to_select=1)
         selector.fit(self.X)
@@ -141,7 +141,7 @@ def test_calculate_dSL(self):
         selector.fit(self.X)
 
         active_points = np.where(
-            selector.dSL_[selector.vlocation_of_idx] < selector.haussdorf_
+            selector.dSL_[selector.vlocation_of_idx] < selector.hausdorff_
         )[0]
 
         ap = selector._get_active(self.X, selector.selected_idx_[-1])
@@ -165,13 +165,13 @@ def test_calculate_dSL(self):
         )
 
     def test_score(self):
-        """This test check that function score return haussdorf distance"""
+        """This test check that function score return hausdorff distance"""
         selector = VoronoiFPS(n_to_select=3, initialize=0)
         selector.fit(self.X)
 
         self.assertTrue(
             np.allclose(
-                selector.haussdorf_,
+                selector.hausdorff_,
                 selector.score(self.X, selector.selected_idx_[-1]),
             )
         )