Default arguments don't do what you expect...

clusterview/algorithms.py

@@ -8,7 +8,7 @@ class CentroidGrouping:
     can change). This allows us to do better than just dumping the grouping
     into a dictionary with a long tuple pointing at an array.
     """
-    def __init__(self, centroid, points=[]):
+    def __init__(self, centroid, points=None):
         if not isinstance(centroid, Point):
             ValueError("Centroid must be a Point.")
 
@@ -16,7 +16,11 @@ class CentroidGrouping:
             ValueError("Points must be in a list.")
 
         self.__centroid = centroid
-        self.__points = points
+
+        if points is None:
+            self.__points = []
+        else:
+            self.__points = points
 
     @property
     def centroid(self):
@@ -32,15 +36,20 @@ class CentroidGrouping:
 
         @param point The point.
         """
-
         if not isinstance(point, Point):
             raise ValueError("Point must be of type Point.")
 
         self.__points.append(point)
 
+    def __repr__(self):
+        s = f"CENTROID: {self.__centroid}\n"
+        s += f"POINTS: {self.__points}"
+
+        return s
+
     def __eq__(self, other):
-        return (self.centroid == other.centroid and
+        return (self.__centroid == other.centroid and
-                self.points == other.points)
+                self.__points == other.points)
 
 
 class Algorithms:
@@ -49,72 +58,48 @@ class Algorithms:
     geometry algorithms.
     """
 
-    # Since all algorithms rely on a set of centroids it is stored here
+    @staticmethod
-    # statically.
+    def euclidean_grouping(centroids, point_set):
-    __centroids = []
-
-    @classmethod
-    def clear_centroids(cls):
-        cls.__centroids = []
-
-    @classmethod
-    def centroids(cls):
-        return cls.__centroids
-
-    @classmethod
-    def set_centroids(cls, centroids):
-        for c in centroids:
-            if not isinstance(c, Point):
-                raise ValueError("Centroids must be of type Point.")
-
-            cls.__centroids.append(c)
-
-    @classmethod
-    def euclidean_grouping(cls, point_set):
         """
         Given a point set that EXCLUDES the centroids specified
         it returns a map from centroid to array of points, where the array
         of points contains the points with the smallest euclidean distance
         from that point.
 
-        @param cls The class calling the method.
+        @param centroids The centroids to use.
-        @param point_set The set of points from the UI.
+        @param point_set The set of points from the UI excluding centroids.
         """
         if not isinstance(point_set, PointSet):
             raise ValueError("Euclidean grouping can only be calculated on " +
                              "PointSet types.")
 
-        if not cls.__centroids:
+        if not isinstance(centroids, list):
+            raise ValueError("Centroids must be of type list.")
+
+        if not centroids:
             raise ValueError("No centroids specified.")
 
         groups = []
 
-        for centroid in cls.__centroids:
+        for centroid in centroids:
             groups.append(CentroidGrouping(centroid))
 
         for point in point_set.points:
             nearest_distance = float("inf")
-            nearest_centroid = None
+            nearest_group = None
 
-            for centroid in cls.__centroids:
+            for current_group in groups:
-                current_distance = Math.euclidean_distance(centroid, point)
+                current_distance = (
+                    Math.euclidean_distance(current_group.centroid, point))
 
                 if current_distance < nearest_distance:
-                    nearest_centroid = centroid
+                    nearest_group = current_group
                     nearest_distance = current_distance
 
-            if nearest_centroid is None:
+            if nearest_group is None:
                 raise ValueError("Failed to find centroid nearest " +
                                  f"to point {point}")
 
-            # We successfully found the nearest centroid to the point
+            nearest_group.add_point(point)
-            # and we can add it to the list.
-            # TODO: Can CentroidGrouping be made hashable?
-            # This is relatively slow for large numbers of groups. If
-            # CentroidGrouping can be made hashable then this becomes O(1).
-            for group in groups:
-                if nearest_centroid == group.centroid:
-                    group.add_point(point)
-                    break
 
         return groups

tests/test_algorithms.py

@@ -5,19 +5,9 @@ from clusterview.colors import Color
 from clusterview.points import Point, PointSet
 
 
-@pytest.fixture(autouse=True, scope="function")
-def teardown():
-    """
-    Teardown function for after each test. The current pytest best practice
-    is to run a setup routine, yield, and then run your teardown routine.
-    """
-    yield
-    Algorithms.clear_centroids()
-
-
 def test_empty_centroids():
     with pytest.raises(ValueError):
-        Algorithms.euclidean_grouping(None)
+        Algorithms.euclidean_grouping([], None)
 
 
 def test_wrong_point_set():
@@ -27,13 +17,22 @@ def test_wrong_point_set():
 
     centroids = [centroid_g1, centroid_g2, centroid_g3]
 
-    Algorithms.set_centroids(centroids)
+    with pytest.raises(ValueError):
+        Algorithms.euclidean_grouping(centroids, None)
+
+
+def test_empty_point_set():
+    centroid_g1 = Point(101, 81, Color.ORANGE, 8, 800, 600)
+    centroid_g2 = Point(357, 222, Color.RED, 8, 800, 600)
+    centroid_g3 = Point(728, 47, Color.PURPLE, 8, 800, 600)
+
+    centroids = [centroid_g1, centroid_g2, centroid_g3]
 
     with pytest.raises(ValueError):
-        Algorithms.euclidean_grouping(None)
+        Algorithms.euclidean_grouping(centroids, [])
 
 
-def test_euclidean_distance():
+def test_euclidean_grouping():
     centroid_g1 = Point(101, 81, Color.ORANGE, 8, 800, 600)
     centroid_g2 = Point(357, 222, Color.RED, 8, 800, 600)
     centroid_g3 = Point(728, 47, Color.PURPLE, 8, 800, 600)
@@ -77,8 +76,7 @@ def test_euclidean_distance():
 
     expected = [centroid_grouping_1, centroid_grouping_2, centroid_grouping_3]
 
-    Algorithms.set_centroids(centroids)
+    actual = Algorithms.euclidean_grouping(centroids, point_set)
-    actual = Algorithms.euclidean_grouping(point_set)
 
     assert len(actual) == len(expected)