k_means_3.py

pred = []
for point in test_data:
    ## Find distance between test data point and centroids
    dis_point_c1 = ((c1[0]-point[0])**2 + (c1[1]-point[1])**2 + 
                    (c1[2]-point[2])**2 + (c1[3]-point[3])**2)**0.5
    dis_point_c2 = ((c2[0]-point[0])**2 + (c2[1]-point[1])**2 + 
                    (c2[2]-point[2])**2 + (c2[3]-point[3])**2)**0.5
    dis_point_c3 = ((c3[0]-point[0])**2 + (c3[1]-point[1])**2 + 
                    (c3[2]-point[2])**2 + (c3[3]-point[3])**2)**0.5
    ## Find the cluster to which the point is closest to and append 
    ## it to pred
    distances = [dis_point_c1,dis_point_c2,dis_point_c3]
    pos = distances.index(min(distances))
    pred.append(pos)
    ## Print the predictions 
    print(pred)