3 changed files with 216 additions and 1 deletions
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from .math import Math |
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from .points import Point, PointSet |
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class CentroidGrouping: |
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""" |
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A storage class used because Points are not hashable (since the x and y |
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can change). This allows us to do better than just dumping the grouping |
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into a dictionary with a long tuple pointing at an array. |
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""" |
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def __init__(self, centroid, points=[]): |
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if not isinstance(centroid, Point): |
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ValueError("Centroid must be a Point.") |
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if not isinstance(points, list): |
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ValueError("Points must be in a list.") |
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self.__centroid = centroid |
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self.__points = points |
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@property |
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def centroid(self): |
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return self.__centroid |
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@property |
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def points(self): |
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return self.__points |
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def add_point(self, point): |
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""" |
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Adds a point. |
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@param point The point. |
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""" |
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if not isinstance(point, Point): |
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raise ValueError("Point must be of type Point.") |
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self.__points.append(point) |
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def __eq__(self, other): |
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return (self.centroid == other.centroid and |
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self.points == other.points) |
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class Algorithms: |
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""" |
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A static class for handling and containing various computational |
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geometry algorithms. |
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""" |
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# Since all algorithms rely on a set of centroids it is stored here |
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# statically. |
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__centroids = [] |
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@classmethod |
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def clear_centroids(cls): |
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cls.__centroids = [] |
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@classmethod |
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def centroids(cls): |
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return cls.__centroids |
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@classmethod |
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def set_centroids(cls, centroids): |
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for c in centroids: |
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if not isinstance(c, Point): |
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raise ValueError("Centroids must be of type Point.") |
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cls.__centroids.append(c) |
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@classmethod |
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def euclidean_grouping(cls, point_set): |
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""" |
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Given a point set that EXCLUDES the centroids specified |
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it returns a map from centroid to array of points, where the array |
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of points contains the points with the smallest euclidean distance |
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from that point. |
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@param cls The class calling the method. |
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@param point_set The set of points from the UI. |
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""" |
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if not isinstance(point_set, PointSet): |
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raise ValueError("Euclidean grouping can only be calculated on " + |
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"PointSet types.") |
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if not cls.__centroids: |
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raise ValueError("No centroids specified.") |
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groups = [] |
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for centroid in cls.__centroids: |
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groups.append(CentroidGrouping(centroid)) |
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for point in point_set.points: |
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nearest_distance = float("inf") |
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nearest_centroid = None |
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for centroid in cls.__centroids: |
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current_distance = Math.euclidean_distance(centroid, point) |
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if current_distance < nearest_distance: |
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nearest_centroid = centroid |
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nearest_distance = current_distance |
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if nearest_centroid is None: |
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raise ValueError("Failed to find centroid nearest " + |
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f"to point {point}") |
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# We successfully found the nearest centroid to the point |
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# and we can add it to the list. |
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# TODO: Can CentroidGrouping be made hashable? |
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# This is relatively slow for large numbers of groups. If |
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# CentroidGrouping can be made hashable then this becomes O(1). |
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for group in groups: |
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if nearest_centroid == group.centroid: |
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group.add_point(point) |
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break |
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return groups |
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@ -0,0 +1,89 @@
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import pytest |
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from clusterview.algorithms import Algorithms, CentroidGrouping |
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from clusterview.colors import Color |
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from clusterview.points import Point, PointSet |
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@pytest.fixture(autouse=True, scope="function") |
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def teardown(): |
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""" |
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Teardown function for after each test. The current pytest best practice |
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is to run a setup routine, yield, and then run your teardown routine. |
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""" |
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yield |
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Algorithms.clear_centroids() |
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def test_empty_centroids(): |
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with pytest.raises(ValueError): |
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Algorithms.euclidean_grouping(None) |
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def test_wrong_point_set(): |
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centroid_g1 = Point(101, 81, Color.ORANGE, 8, 800, 600) |
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centroid_g2 = Point(357, 222, Color.RED, 8, 800, 600) |
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centroid_g3 = Point(728, 47, Color.PURPLE, 8, 800, 600) |
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centroids = [centroid_g1, centroid_g2, centroid_g3] |
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Algorithms.set_centroids(centroids) |
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with pytest.raises(ValueError): |
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Algorithms.euclidean_grouping(None) |
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def test_euclidean_distance(): |
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centroid_g1 = Point(101, 81, Color.ORANGE, 8, 800, 600) |
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centroid_g2 = Point(357, 222, Color.RED, 8, 800, 600) |
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centroid_g3 = Point(728, 47, Color.PURPLE, 8, 800, 600) |
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centroids = [centroid_g1, centroid_g2, centroid_g3] |
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point1_g1 = Point(67, 59, Color.GREY, 8, 800, 600) |
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point2_g1 = Point(116, 53, Color.GREY, 8, 800, 600) |
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point3_g1 = Point(144, 105, Color.GREY, 8, 800, 600) |
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point1_g2 = Point(388, 243, Color.GREY, 8, 800, 600) |
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point2_g2 = Point(358, 248, Color.GREY, 8, 800, 600) |
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point3_g2 = Point(426, 202, Color.GREY, 8, 800, 600) |
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point1_g3 = Point(750, 47, Color.GREY, 8, 800, 600) |
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point2_g3 = Point(741, 85, Color.GREY, 8, 800, 600) |
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point3_g3 = Point(700, 72, Color.GREY, 8, 800, 600) |
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# This PointSet is the PointSet that excludes the centroids. |
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point_set = PointSet(8, 800, 600) |
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point_set.add_point(67, 59, Color.GREY) |
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point_set.add_point(116, 53, Color.GREY) |
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point_set.add_point(144, 105, Color.GREY) |
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point_set.add_point(388, 243, Color.GREY) |
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point_set.add_point(358, 248, Color.GREY) |
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point_set.add_point(426, 202, Color.GREY) |
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point_set.add_point(750, 47, Color.GREY) |
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point_set.add_point(741, 85, Color.GREY) |
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point_set.add_point(700, 72, Color.GREY) |
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centroid_grouping_1 = CentroidGrouping(centroid_g1, |
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[point1_g1, point2_g1, point3_g1]) |
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centroid_grouping_2 = CentroidGrouping(centroid_g2, |
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[point1_g2, point2_g2, point3_g2]) |
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centroid_grouping_3 = CentroidGrouping(centroid_g3, |
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[point1_g3, point2_g3, point3_g3]) |
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expected = [centroid_grouping_1, centroid_grouping_2, centroid_grouping_3] |
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Algorithms.set_centroids(centroids) |
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actual = Algorithms.euclidean_grouping(point_set) |
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assert len(actual) == len(expected) |
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# Since I don't want to figure out what grouping is where I'll accept |
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# the linearity of `in`. |
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assert centroid_grouping_1 in actual |
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assert centroid_grouping_2 in actual |
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assert centroid_grouping_3 in actual |
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