April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
Content preview from 精通機器學習
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264
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第九章：無監督學習技術
最小值
圖
9-21
各種群聚數量的
AIC
與
BIC
貝氏高斯混合模型
你不需要手動搜尋最佳群聚數量
，
只要使用
BayesianGaussianMixture
類別即可
，
它可以
將沒必要的群聚的權重設為零
（
或接近零
）。
將群聚數量
n_components
設成比最佳群聚數
量更大的值之後
（
假設你已經稍微瞭解眼前的問題
，
並且有充分的理由
），
這個演算法就
會自動移除沒必要的群聚
。
例如
，
我們將群聚數量設為
10，
來看看會如何
：
>>> from sklearn.mixture import
BayesianGaussianMixture
>>>
bgm = BayesianGaussianMixture(n_components=10, n_init=10)
>>>
bgm.fit(X)
>>>
np.round(bgm.weights_, 2)
array([0.4 , 0.21, 0.4 , 0. , 0. , 0. , 0. , 0. , 0. , 0. ])
完美
！
這個演算法自動發現只有三個群聚是必要的
，
最後的群聚幾乎與圖
9-17
的一模
一樣
。
這個模型不會將群聚參數
（
包括權重
、
均值與共變異數矩陣
）
視為固定的模型參數
，
而是
潛在隨機變數
，
就像分配群聚那樣
（
見圖
9-22）。
所以現在
z
包含群聚參數與指派的群聚
。
Beta
分布經常被用來模擬值在固定範圍之內的隨機變數
，
在這個例子中
，
範圍是從
0
到
1。
用範例來解釋
Stick-Breaking
程序
（SBP）
是最好的做法
：
假設
Φ
=[0.3, 0.6, ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.