K-means 알고리즘 구현

unsupervised learning 중에 k-means 알고리즘을 읽고 구현해보았다.

1차 시도

import numpy as np
import matplotlib.pyplot as plt

# 데이터들
data = np.array([
    [-1, 0],
    [0, 0],
    [2, 2],
    [3, 2],
    [3, 3],
    [-1, -1]
], dtype=np.float32)

# initial center
center0 = data[0]
center1 = data[1]

iter = 3

for _ in range(1, iter +1) :
    res = np.stack(
        (np.sum((data - center0)**2, axis=1),
        np.sum((data - center1)**2, axis=1)),
        axis=0
        )

    argmin_list = np.argmin(res, axis=0)
    
    center0 = np.mean(data[argmin_list==[0]], axis=0)
    center1 = np.mean(data[argmin_list==[1]], axis=0)
    
cluster0 = data[argmin_list==[0]]
cluster1 = data[argmin_list==[1]]

plt.scatter(center0[0].reshape(-1,1), center0[1].reshape(-1,1), color="#00ffff")
plt.scatter(center1[0].reshape(-1,1), center1[1].reshape(-1,1), color="#0000ff")
plt.scatter(cluster0[:, 0], cluster0[:,1], color="#ff0000")
plt.scatter(cluster1[:, 0], cluster1[:,1], color="#ffff00")
plt.show()

 

2차 시도

import numpy as np
import matplotlib.pyplot as plt

# creating data
def random_3d_data(r, g, b, n=100, diff=5) :
    
    res = np.concatenate(
        (np.random.randint(r-diff, r+diff+1, (n, 1)),
        np.random.randint(g-diff, g+diff+1, (n, 1)),
        np.random.randint(b-diff, b+diff+1, (n, 1))),
        axis=1
    )
    
    return res

# calculate distance
def distance(pos1, pos2, l_norm=2) :
    return np.sum(np.abs((pos1-pos2) ** l_norm), axis=1)

train_data = np.concatenate(
    (
        random_3d_data(5, 10, 10, diff=20),
        random_3d_data(20, 3, 3, diff=10),
        random_3d_data(0, 0, 0, diff=15)
    ),
    axis = 0
)
test_data = random_3d_data(10, 10, 10, diff=15)

iters = 5
k_cluster = 10

# initial center point
center = np.array(
    train_data[np.random.randint(0, train_data.shape[0], (k_cluster,))],
    dtype = np.float32
)
for _ in range(iters) :
    # calculate argmax index
    idx = np.argmin([distance(train_data, i) for i in center], axis=0)
    # updating centeral points
    for i in range(k_cluster) : center[i] = np.mean(train_data[idx == i], axis = 0)

idx = np.argmin([distance(test_data, i) for i in center], axis=0)

X = test_data[:, 0]
Y = test_data[:, 1]
Z = test_data[:, 2]

fig, ax = plt.subplots(
    figsize=(10,10),
    subplot_kw={'projection':'3d'}
)

ax.scatter(X, Y, Z, c=idx, cmap="inferno")
ax.scatter(center[:, 0], center[:, 1], center[:, 2],
           marker="x", color="blue")

plt.show()

3D Scatter plot