init kmeans

clustering/cluster.py

@@ -54,7 +54,7 @@ class Cluster:
             self.centroid = new_centroid
             return True
 
-
+# TODO: Delete code below after moving to kmeans.py
 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)]
 
@@ -78,7 +78,6 @@ def k_means(points: Tuple[Any], clusters: List[Cluster], max_iter: int = 1e9):
         if not progress:
             break
 
-# TODO: Delete below code
 def load_file(filepath: str):
     points = list()
     with open(filepath, 'r') as f:

clustering/kmeans.py

@@ -0,0 +1,39 @@
+from typing import List
+
+import numpy as np
+from cluster import Cluster
+
+
+class KMeans:
+    def __init__(self, n_clusters: int, random_state: int = 0) -> None:
+        self.n_clusters = n_clusters
+        self.random_state = random_state
+        self.clusters: List[Cluster] = list()
+        self.labels: np.ndarray = None
+
+    def fit(self, X: np.ndarray) -> None:
+        self.__generate_clusters(X)
+        # self.labels = np.zeros(X.shape[0])
+
+        # while True:
+        #     new_labels = np.array([np.argmin(np.linalg.norm(X - centroid, axis=1)) for centroid in self.centroids])
+        #     if np.array_equal(self.labels, new_labels):
+        #         break
+        #     self.labels = new_labels
+
+        #     for i in range(self.n_clusters):
+        #         self.centroids[i] = np.mean(X[self.labels == i], axis=0)
+
+    def __generate_clusters(self, X: np.ndarray) -> None:
+        generator = np.random.default_rng(self.random_state)
+        min_val, max_val = np.min(X), np.max(X)
+        self.clusters = [Cluster(
+            generator.uniform(min_val, max_val, X.shape[1])
+        ) for _ in range(self.n_clusters)]
+
+
+# TODO: Delete code below
+kmeans = KMeans(2)
+kmeans.fit(np.array([[1, 2], [3, 4], [5, 6], [7, 8]]))
+for cluster in kmeans.clusters:
+    print(cluster.centroid)