feat(algs): add k means

Author: jessicayung

algorithms/k_means.py

@@ -0,0 +1,49 @@
+"""
+K-means implementation
+
+Seems okay: Have not tested this rigorously, but this separates 'Iris-setosa' 
+pretty well from 'Iris-versicolor' and 'Iris-virginica',
+but mixes the latter two. 
+
+Dataset: Iris dataset.
+"""
+
+from load_data import load_iris
+import numpy as np
+
+
+X_std, y = load_iris(std=True)
+
+
+def k_means(X, num_means=3, num_iterations=10):
+    """K means. Assumes each datapoint is a 1D array."""
+    # data dim
+    N, D = X.shape
+    
+    # initialise vars
+    assignments = np.zeros(N)
+    dists = np.zeros((N, num_means))    
+
+    # 1. Init means
+    means = np.random.random((num_means, D))
+    
+    # 2. Iterate
+    for i in range(num_iterations):
+        # 2a(i) Calculate dists
+        for k in range(num_means):
+            dists[:,k] = np.sum((X - np.tile(means[k],(N,1)))**2,axis=1)
+
+        # 2a(ii): Assign clusters
+        for n in range(N):
+            assignments[n] = np.argmin(dists[n])
+
+        # 2b. Recalculate cluster means
+        for k in range(num_means):
+            means[k] = np.mean([X[i] for i in range(N) if assignments[i] == k], axis=0)
+
+    return means, assignments
+
+
+means, assignments = k_means(X_std)
+
+print(assignments, y)