Python手動實現kmeans聚類和調用sklearn實現

1. 算法步驟

1. 隨機選取k個樣本點充當k個簇的中心點；
2. 計算所有樣本點與各個簇中心之間的距離，然后把樣本點划入最近的簇中；
3. 根據簇中已有的樣本點，重新計算簇中心；
4. 重復步驟2和3，直到簇中心不再改變或改變很小。

2. 手動Python實現

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets.samples_generator import make_blobs

n_data = 400
n_cluster = 4
# generate training data
X, y = make_blobs(n_samples=n_data, centers=n_cluster, cluster_std=0.60, random_state=0)

# generate centers of clusters
centers = np.random.rand(4, 2)*5

EPOCH = 10
tol = 1e-5
for epoch in range(EPOCH):
    labels = np.zeros(n_data, dtype=np.int)

    # 計算每個點到簇中心的距離並分配label
    for i in range(n_data):
        distance = np.sum(np.square(X[i]-centers), axis=1)
        label = np.argmin(distance)
        labels[i] = label

    # 重新計算簇中心
    for i in range(n_cluster):
        indices = np.where(labels == i)[0]       # 找出第i簇的樣本點的下標
        points = X[indices]
        centers[i, :] = np.mean(points, axis=0)  # 更新第i簇的簇中心

plt.scatter(X[:, 0], X[:, 1], c=labels, s=40, cmap='viridis')
plt.show()

運行結果：(注：當簇中心初始化不好時，可能計算會有點錯誤)

 

3. 調用sklearn實現kmeans

import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.datasets.samples_generator import make_blobs

# Generate some data
X, y = make_blobs(n_samples=400, centers=4, cluster_std=0.60, random_state=0)

# kmeans clustering
kmeans = KMeans(4, random_state=0)
kmeans.fit(X)   # 訓練模型
labels = kmeans.predict(X)   # 預測分類
plt.scatter(X[:, 0], X[:, 1], c=labels, s=40, cmap='viridis')
plt.show()

運行結果：