April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
Content preview from 精通機器學習
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分群法（clustering）
|
235
>>>
kmeans.transform(X_new)
array([[2.81093633, 0.32995317, 2.9042344 , 1.49439034, 2.88633901],
[5.80730058, 2.80290755, 5.84739223, 4.4759332 , 5.84236351],
[1.21475352, 3.29399768, 0.29040966, 1.69136631, 1.71086031],
[0.72581411, 3.21806371, 0.36159148, 1.54808703, 1.21567622]])
在這個例子中
，
X_new
的第一個實例距離第一個質心
2.81，
距離第二個質心
0.33，
距離第
三個質心
2.90，
距離第四個質心
1.49，
距離第五個質心
2.89。
如果你的資料組是高維的
，
用這種方式轉換它可產生一個
k
維的資料組
：
這個轉換或許是非常高效的非線性降維技
術
。
K-Means 演算法
那麼
，
這個演算法究竟是如何運作的呢
？
如果你已經知道質心了
，
你可以將資料組的所有
實例分配給最接近它的質心所屬的群聚
，
來輕鬆地幫所有實例加上標籤
。
反過來說
，
如果
你已經取得所有實例標籤了
，
你可以計算各個群體的實例均值
，
來輕鬆地找到所有質心
。
但是現在你既沒有標籤
，
也沒有質心
，
該如何繼續工作
？
你只要隨便放置質心
（
例如隨機
選擇
k
個實例
，
並且將它們的位置當成質心
），
再幫實例加上標籤
，
更改質心
，
加標籤
，
更改質心
，
以此類推 ...
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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.
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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.