Last active
January 17, 2018 19:11
-
-
Save hoenirvili/169b2361c21694c4999eef405df7a1a3 to your computer and use it in GitHub Desktop.
kmeans
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
#!/usr/bin/env python3 | |
import abc | |
import math | |
import random | |
class Point(metaclass=abc.ABCMeta): | |
def __init__(self, coordinate): | |
if not isinstance(coordinate, float) and not isinstance( | |
coordinate, int) and not isinstance(coordinate, list): | |
raise TypeError("Invalid type for point") | |
self._coordinate = coordinate | |
@abc.abstractmethod | |
def distance(self, point): | |
raise NotImplementedError( | |
"Users must define a distance method to be a point") | |
@abc.abstractproperty | |
def coordinates(self): | |
raise NotImplementedError( | |
"Users must define a coordinates method to be a point") | |
class OneDimensionalPoint(Point): | |
def distance(self, point): | |
if not isinstance(point, Point): | |
raise TypeError("Invalid type for distance") | |
point_coordinate = point.coordinates[0] | |
if self._coordinate > point_coordinate: | |
return self._coordinate - point_coordinate | |
return point_coordinate - self._coordinate | |
@property | |
def coordinates(self): | |
return [self._coordinate] | |
def __str__(self): | |
return str(self._coordinate) | |
def __repr__(self): | |
return self.__str__() | |
def __add__(self, point): | |
return OneDimensionalPoint(point.coordinates[0] + self._coordinate) | |
def __radd__(self, other): | |
if other == 0: | |
return self | |
return self.__add__(other) | |
def __len__(self): | |
return 1 | |
class TwoDimensionalPoint(Point): | |
def distance(self, point): | |
if not isinstance(point, Point): | |
raise TypeError("Invalid type for distance") | |
x2, y2 = point.coordinates[0], point.coordinates[1] | |
x1, y1 = self.coordinates[0], self.coordinates[1] | |
return math.sqrt((math.pow((x1 - x2), 2) + math.pow((y1 - y2), 2))) | |
@property | |
def coordinates(self): | |
return self._coordinate | |
def __str__(self): | |
x = str(self.coordinates[0]) | |
y = str(self.coordinates[1]) | |
return str("[" + x + "," + y + "]") | |
def __repr__(self): | |
return self.__str__() | |
def __add__(self, point): | |
x2, y2 = point.coordinates[0], point.coordinates[1] | |
x1, y1 = self.coordinates[0], self.coordinates[1] | |
ux, uy = x1 + x2, y1 + y2 | |
return TwoDimensionalPoint([ux, uy]) | |
def __radd__(self, other): | |
if other == 0: | |
return self | |
return self.__add__(other) | |
def __len__(self): | |
return 2 | |
def onePoints(arr): | |
if not isinstance(arr, list): | |
raise TypeError("Arr must be a list") | |
if len(arr) < 2: | |
raise ValueError("Too few values") | |
points = [] | |
for a in arr: | |
point = OneDimensionalPoint(a) | |
points.append(point) | |
return points | |
def twoPoints(arr): | |
if not isinstance(arr, list): | |
raise TypeError("Arr must be a list") | |
if len(arr) < 2: | |
raise ValueError("Invalid row length") | |
for a in arr: | |
if not isinstance(a, list): | |
raise TypeError("Elements of arr must also be a list") | |
if len(a) < 2: | |
raise ValueError("Invalid col length") | |
points = [] | |
for col in arr: | |
row = TwoDimensionalPoint(col) | |
points.append(row) | |
return points | |
def best(centroids, point): | |
d = {} | |
for centroid in centroids: | |
d[centroid] = point.distance(centroid) | |
return min(d, key=d.get) | |
def compose_clusters(points, centroids): | |
clusters = {} | |
for centroid in centroids: | |
clusters[centroid] = [] | |
for point in points: | |
centroid = best(centroids, point) | |
clusters[centroid] += [point.coordinates] | |
return clusters | |
def sum_coordinates(points, dimension): | |
if dimension == 1: | |
r = sum(point[0] for point in points) | |
return (r, r) | |
r1, r2 = 0, 0 | |
for point in points: | |
r1 += point[0] | |
r2 += point[1] | |
return (r1, r2) | |
def recompute_centroids(clusters, dimension): | |
centroids = [] | |
for key, points in clusters.items(): | |
if points == []: | |
centroids += [key] | |
continue | |
ux, uy = sum_coordinates(points, dimension) | |
if dimension == 1: | |
u = ux / len(points) | |
centroids.append(OneDimensionalPoint(u)) | |
elif dimension == 2: | |
ux, uy = ux / len(points), uy / len(points) | |
centroids.append(TwoDimensionalPoint([ux, uy])) | |
return centroids | |
def compare_centroids(p1, p2): | |
if len(p1) != len(p2): | |
raise ValueError("Can't compare these two centroids") | |
for i, _ in enumerate(p1): | |
if set(p1[i].coordinates) != set(p2[i].coordinates): | |
return False | |
return True | |
def jcriterion(clusters): | |
s = 0 | |
for key, values in clusters.items(): | |
if values == []: | |
continue | |
centroid = key | |
for point in values: | |
if len(point) == 2: | |
x1 = point[0] | |
y1 = point[1] | |
x2 = centroid.coordinates[0] | |
y2 = centroid.coordinates[1] | |
x = math.pow((x1 - x2), 2) + math.pow((y1 - y2), 2) | |
s += x | |
else: | |
x = OneDimensionalPoint(point[0]) | |
d = centroid.distance(x) | |
s += math.pow(d, 2) | |
return s | |
def kmeans(points, centroids, k, iterations=-1): | |
if k < 2: | |
raise ValueError("Invalid number of clusters") | |
if centroids is None: | |
raise ValueError("Need valid centroids") | |
dimension = len(points[0]) | |
convergence = False | |
i = 0 | |
t = 0 | |
while not convergence: | |
if iterations != -1: | |
i += 1 | |
if i == iterations: | |
break | |
clusters = compose_clusters(points, centroids) | |
j = jcriterion(clusters) | |
print("Step: {:d}, value: {:f}".format(t, j)) | |
newCentroids = recompute_centroids(clusters, dimension) | |
convergence = compare_centroids(centroids, newCentroids) | |
centroids = newCentroids | |
t += 1 | |
return clusters | |
def main(): | |
coordinates = [-9, -8, -7, -6, -5, 5, 6, 7, 8, 9, 5, 6, 7, 8, 9] | |
points = onePoints(coordinates) | |
centroids = onePoints([-20, -10]) | |
clusters = kmeans(points, centroids, len(centroids)) | |
keys = list(clusters.keys()) | |
print("Centroid = ", keys[0], "Cluster = ", clusters[keys[0]]) | |
print("Centroid = ", keys[1], "Cluster = ", clusters[keys[1]]) | |
print() | |
coordinates = [[1, 0], [-1, 0], [0, 1], [3, 0], [3, 1]] | |
points = twoPoints(coordinates) | |
centroids = twoPoints([[-1, 0], [3, 1]]) | |
clusters = kmeans(points, centroids, 2) | |
keys = list(clusters.keys()) | |
print("Centroid = ", keys[0], "Cluster = ", clusters[keys[0]]) | |
print("Centroid = ", keys[1], "Cluster = ", clusters[keys[1]]) | |
if __name__ == "__main__": | |
main() |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment