clean code

requirements.txt

@@ -0,0 +1,2 @@
+numpy==1.26.4
+matplotlib==3.9.0

src/cluster.py

@@ -0,0 +1,125 @@
+from math import sqrt
+from random import randrange
+from typing import Any, List, Tuple
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+class Cluster:
+    def __init__(self, centroid: Tuple[Any]):
+        self.centroid = centroid
+        self.color = np.random.rand(3,)
+        self.points = list()
+        self.dimension = len(self.centroid)
+        if self.dimension != 2 and self.dimension % 3 != 0:
+            raise ValueError('dimension must be 2 or a multiple of 3')
+
+    def distance(self, point: Tuple[Any]) -> float:
+        sum = 0
+        for dim, p in enumerate(self.centroid):
+            sum += (p - point[dim]) ** 2
+        return sqrt(sum)
+
+    def append(self, point: Tuple[Any]) -> None:
+        self.points.append(point)
+
+    def draw(self, draw_centroid: bool = True, *args) -> None:
+        if len(self.points) == 0:
+            return
+        if self.dimension == 2:
+            if draw_centroid:
+                plt.scatter(*zip(*self.points), c=self.color),
+                plt.scatter(
+                    self.centroid[0], self.centroid[1], c=self.color, edgecolors='k')
+                return
+            plt.scatter(*zip(*self.points), c=self.color)
+        elif self.dimension % 3 == 0:
+            unzipped = list(map(list, zip(*self.points)))
+            for i, ax in enumerate(args):
+                ax.scatter(*unzipped[i * 3: (i + 1) * 3])
+
+    def clear(self) -> None:
+        self.points = list()
+
+    def calc_new_centroid(self) -> bool:
+        if len(self.points) == 0:
+            return False
+        arr = np.array(self.points)
+        new_centroid = tuple(np.average(arr, axis=0))
+        if new_centroid == self.centroid:
+            return False
+        else:
+            self.clear()
+            self.centroid = new_centroid
+            return True
+
+
+def generate_clusters(number_of_clusters: int, number_of_dimensions: int, random_range=0) -> List[Cluster]:
+    return [Cluster([randrange(random_range) for _ in range(number_of_dimensions)]) for _ in range(number_of_clusters)]
+
+
+def k_means(points: Tuple[Any], clusters: List[Cluster], max_iter: int = 1e9):
+    for _ in range(int(max_iter)):
+        for point in points:
+            ind = 0
+            best = 1e7
+            for i, cluster in enumerate(clusters):
+                d = cluster.distance(point)
+                if d < best:
+                    best = d
+                    ind = i
+            clusters[ind].append(point)
+        progress = False
+        for cluster in clusters:
+            changed = cluster.calc_new_centroid()
+            if changed:
+                progress = True
+        if not progress:
+            break
+
+# TODO: Delete below code
+def load_file(filepath: str):
+    points = list()
+    with open(filepath, 'r') as f:
+        lines = f.readlines()
+        for line in lines:
+            point = list(map(int, line.split()))
+            points.append(point)
+    return points
+
+
+def main(dimension: str):
+    if dimension.lower() == '2d':
+        number_of_clusters = 15
+        dim = 2
+        random_range = int(1e6)
+        points = load_file("./s1.txt")
+    elif dimension.lower() == '9d':
+        number_of_clusters = 2
+        dim = 9
+        random_range = int(10)
+        points = load_file("./breast.txt")
+    else:
+        raise ValueError('dimension must be 2d or 9d')
+
+    clusters = generate_clusters(number_of_clusters, dim, random_range)
+
+    k_means(points, clusters)
+
+    if dimension.lower() == '9d':
+        fig = plt.figure(figsize=(20, 10))
+        ax1 = fig.add_subplot(131, projection='3d')
+        ax2 = fig.add_subplot(132, projection='3d')
+        ax3 = fig.add_subplot(133, projection='3d')
+        for cluster in clusters:
+            cluster.draw(False, ax1, ax2, ax3)
+    else:
+        for cluster in clusters:
+            cluster.draw()
+    plt.show()
+
+
+if __name__ == '__main__':
+    main('2d')
+    # main('9d')

src/cluster_class.py

@@ -1,141 +0,0 @@
-import os
-import matplotlib.pyplot as plt
-import numpy as np
-from math import sqrt
-from random import randrange, random
-
-WORKDIR = './lab1'
-
-
-class Cluster:
-    def __init__(self, centroid, color):
-        self.centroid = centroid
-        self.color = color
-        self.points = list()
-
-    def distance(self, point):
-        sum = 0
-        for dim, p in enumerate(self.centroid):
-            sum += (p - point[dim]) ** 2
-        return sqrt(sum)
-
-    def append(self, point):
-        self.points.append(point)
-
-    def draw(self, with_centroids=True, ax1=None, ax2=None, ax3=None):
-        if len(self.points) != 0:
-            if len(self.points[0]) > 2:
-                unzipped = list(map(list, zip(*self.points)))
-                ax1.scatter(unzipped[0], unzipped[1], unzipped[2])
-                ax2.scatter(unzipped[3], unzipped[4], unzipped[5])
-                ax3.scatter(unzipped[6], unzipped[7], unzipped[8])
-            else:
-                plt.scatter(*zip(*self.points), c=self.color)
-                if with_centroids:
-                    plt.scatter(
-                        self.centroid[0], self.centroid[1], c=self.color, edgecolors='k')
-
-    def clear(self):
-        self.points = []
-
-    def calc_new_centroid(self):
-        if len(self.points) == 0:
-            return False
-        arr = np.array(self.points)
-        new_centroid = np.average(arr, axis=0).tolist()
-        if new_centroid == self.centroid:
-            return False
-        else:
-            self.clear()
-            self.centroid = new_centroid
-            return True
-
-
-def generate_clusters(number_of_clusters: int, number_of_dimensions: int, random_range=0, with_centroids: bool = True):
-    clusters = list()
-    if with_centroids:
-        for _ in range(number_of_clusters):
-            clusters.append(Cluster([randrange(random_range)
-                            for i in range(number_of_dimensions)], [random(), random(), random()]))
-    else:
-        for _ in range(number_of_clusters):
-            clusters.append(Cluster([-1
-                            for i in range(number_of_dimensions)], [random(), random(), random()]))
-    return clusters
-
-
-def k_mean(points, clusters):
-    while True:
-        for point in points:
-            ind = 0
-            best = 1e7
-            for i, cluster in enumerate(clusters):
-                d = cluster.distance(point)
-                if d < best:
-                    best = d
-                    ind = i
-            clusters[ind].append(point)
-        progress = False
-        for cluster in clusters:
-            res = cluster.calc_new_centroid()
-            if res:
-                progress = True
-        if not progress:
-            break
-
-
-def load_file(filepath: str):
-    points = list()
-    with open(filepath, 'r') as f:
-        lines = f.readlines()
-        for line in lines:
-            point = list(map(int, line.split()))
-            points.append(point)
-    return points
-
-
-def draw_raw_points(points):
-    if len(points[0]) > 2:
-        pass
-    else:
-        plt.scatter(*zip(*points))
-    plt.show()
-
-
-def main(dimension: str):
-    if dimension.lower() == '2d':
-        number_of_clusters = 15
-        dim = 2
-        random_range = int(1e6)
-        points = load_file("./s1.txt")
-    elif dimension.lower() == '9d':
-        number_of_clusters = 2
-        dim = 9
-        random_range = int(10)
-        points = load_file("./breast.txt")
-    else:
-        raise ValueError('dimension must be 2d or 9d')
-
-    clusters = generate_clusters(number_of_clusters, dim, random_range)
-
-    k_mean(points, clusters)
-
-    if dimension.lower() == '9d':
-        fig1 = plt.figure(1)
-        ax1 = fig1.add_subplot(projection='3d')
-        fig2 = plt.figure(2)
-        ax2 = fig2.add_subplot(projection='3d')
-        fig3 = plt.figure(3)
-        ax3 = fig3.add_subplot(projection='3d')
-        for cluster in clusters:
-            cluster.draw(ax1, ax2, ax3)
-    else:
-        for cluster in clusters:
-            cluster.draw()
-    plt.show()
-
-
-if __name__ == '__main__':
-    os.chdir(WORKDIR)
-    main('2d')
-    main('9d')