🧩分群範例¶

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%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt

%matplotlib inline import numpy as np import matplotlib.pyplot as plt

產生n群高斯分布的資料點¶

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from sklearn.datasets.samples_generator import make_blobs
X, y = make_blobs(n_samples=300, centers=3, cluster_std=0.60, random_state=0)

from sklearn.datasets.samples_generator import make_blobs X, y = make_blobs(n_samples=300, centers=3, cluster_std=0.60, random_state=0)

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

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

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from sklearn.cluster import KMeans
kmeans = KMeans(3, random_state=0)
labels = kmeans.fit(X).predict(X)
plt.scatter(X[:, 0], X[:, 1], c=labels, s=40, cmap='viridis');

from sklearn.cluster import KMeans kmeans = KMeans(3, random_state=0) labels = kmeans.fit(X).predict(X) plt.scatter(X[:, 0], X[:, 1], c=labels, s=40, cmap='viridis');

產生隨機的資料點¶

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np.random.seed(0)

np.random.seed(0)

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x = np.random.randn(100, 2)

x = np.random.randn(100, 2)

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plt.scatter(x[:,0],x[:,1])

plt.scatter(x[:,0],x[:,1])

使用 KMeans 分群¶

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# 1. 載入想要用的模型
from sklearn.cluster import KMeans

# 1. 載入想要用的模型 from sklearn.cluster import KMeans

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#2. 建立模型
clf = KMeans(n_clusters=3)

#2. 建立模型 clf = KMeans(n_clusters=3)

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#3. 訓練模錫
clf.fit(x)

#3. 訓練模錫 clf.fit(x)

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clf.labels_

clf.labels_

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plt.scatter(x[:,0], x[:,1], c=clf.labels_)

plt.scatter(x[:,0], x[:,1], c=clf.labels_)

看看新的點加入，要分到哪一群？¶

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y = np.random.randn(10, 2)

y = np.random.randn(10, 2)

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y

y

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#4. 使用模型來預測
clf.predict(y)

#4. 使用模型來預測 clf.predict(y)