Add K-Means.

homemade/k_means/__init__.py

@@ -0,0 +1 @@
+from .k_means import KMeans

homemade/k_means/k_means.py

@@ -0,0 +1,58 @@
+import numpy as np
+
+
+class KMeans:
+    """K-Means Class"""
+
+    def __init__(self, data, num_clusters):
+        self.data = data
+        self.num_clusters = num_clusters
+
+    def train(self, max_iterations):
+        centroids = KMeans.init_centroids(self.data, self.num_clusters)
+
+        # Run K-Means.
+        for iteration_index in range(max_iterations):
+            # Find the closest centroids for training examples.
+            closest_centroids_ids = KMeans.find_closest_centroids(self.data, centroids)
+
+    @staticmethod
+    def init_centroids(data, num_clusters):
+        """Initializes num_clusters centroids that are to be used in K-Means on the dataset data"""
+
+        # Get number of training examples.
+        num_examples = data.shape[0]
+
+        # Randomly reorder indices of training examples.
+        random_ids = np.random.permutation(num_examples)
+
+        # Take the first K examples as centroids.
+        centroids = data[random_ids[:num_clusters + 1], :]
+
+        # Return generated centroids.
+        return centroids
+
+    @staticmethod
+    def find_closest_centroids(data, centroids):
+        # Get number of training examples.
+        num_examples = data.shape[0]
+
+        # Get number of centroids.
+        num_centroids = centroids.shape[0]
+        print(num_centroids)
+
+        # We need to return the following variables correctly.
+        closest_centroids_ids = np.zeros((num_examples, 1))
+
+        # Go over every example, find its closest centroid, and store
+        # the index inside closest_centroids_ids at the appropriate location.
+        # Concretely, closest_centroids_ids(i) should contain the index of the centroid
+        # closest to example i. Hence, it should be a value in the range 1...num_centroids.
+        for example_index in range(num_examples):
+            distances = np.zeros((num_centroids, 1))
+            for centroid_index in range(num_centroids):
+                distance_difference = data[example_index, :] - centroids[centroid_index, :]
+                distances[centroid_index] = np.sum(distance_difference ** 2)
+            print(distances)
+            print(np.argmin(distances))
+            break