From b06d1771db6f57c2873d47433f2b41906d8cb55f Mon Sep 17 00:00:00 2001
From: mszmyd <erilf9@gmail.com>
Date: Tue, 4 Jun 2024 20:39:28 +0200
Subject: [PATCH] add cluster

---
 src/cluster_class.py | 141 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 141 insertions(+)
 create mode 100644 src/cluster_class.py

diff --git a/src/cluster_class.py b/src/cluster_class.py
new file mode 100644
index 0000000..d2af583
--- /dev/null
+++ b/src/cluster_class.py
@@ -0,0 +1,141 @@
+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')