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kmeans/app.py

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+from sklearn.datasets import make_blobs
+import matplotlib.pyplot as plt
+from kmeans import KMeans
+import pandas as pd
+
+#centroids = [(-5,-5),(5,5),(-2.5,2.5),(2.5,-2.5)]
+#cluster_std = [1,1,1,1]
+
+#X,y = make_blobs(n_samples=100,cluster_std=cluster_std,centers=centroids,n_features=2,random_state=2)
+
+#plt.scatter(X[:,0],X[:,1])
+
+df = pd.read_csv('student_clustering.csv')
+
+X = df.iloc[:,:].values
+
+km = KMeans(n_clusters=4,max_iter=500)
+y_means = km.fit_predict(X)
+
+plt.scatter(X[y_means == 0,0],X[y_means == 0,1],color='red')
+plt.scatter(X[y_means == 1,0],X[y_means == 1,1],color='blue')
+plt.scatter(X[y_means == 2,0],X[y_means == 2,1],color='green')
+plt.scatter(X[y_means == 3,0],X[y_means == 3,1],color='yellow')
+plt.show()

kmeans/kmeans.py

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+import random
+import numpy as np
+
+class KMeans:
+    def __init__(self,n_clusters=2,max_iter=100):
+        self.n_clusters = n_clusters
+        self.max_iter = max_iter
+        self.centroids = None
+
+    def fit_predict(self,X):
+
+        random_index = random.sample(range(0,X.shape[0]),self.n_clusters)
+        self.centroids = X[random_index]
+
+        for i in range(self.max_iter):
+            # assign clusters
+            cluster_group = self.assign_clusters(X)
+            old_centroids = self.centroids
+            # move centroids
+            self.centroids = self.move_centroids(X,cluster_group)
+            # check finish
+            if (old_centroids == self.centroids).all():
+                break
+
+        return cluster_group
+
+    def assign_clusters(self,X):
+        cluster_group = []
+        distances = []
+
+        for row in X:
+            for centroid in self.centroids:
+                distances.append(np.sqrt(np.dot(row-centroid,row-centroid)))
+            min_distance = min(distances)
+            index_pos = distances.index(min_distance)
+            cluster_group.append(index_pos)
+            distances.clear()
+
+        return np.array(cluster_group)
+
+    def move_centroids(self,X,cluster_group):
+        new_centroids = []
+
+        cluster_type = np.unique(cluster_group)
+
+        for type in cluster_type:
+            new_centroids.append(X[cluster_group == type].mean(axis=0))
+
+        return np.array(new_centroids)
+