擅长:python、mysql、java
<p>参照这个交叉验证的链接<a href="https://stats.stackexchange.com/questions/70801/how-to-normalize-data-to-0-1-range">How to normalize data to 0-1 range?</a>,看起来您可以对<code>foo</code>的最后一列执行最小最大规范化。</p>
<pre><code>v = foo[:, 1] # foo[:, -1] for the last column
foo[:, 1] = (v - v.min()) / (v.max() - v.min())
</code></pre>
<p/>
<pre><code>foo
array([[ 0. , 0. ],
[ 0.13216 , 0.06609523],
[ 0.25379 , 1. ],
[ 0.30874 , 0.09727968]])
</code></pre>
<hr/>
<p>执行标准化的另一个选项(如OP所建议的)是使用<code>sklearn.preprocessing.normalize</code>,这会产生稍微不同的结果-</p>
<pre><code>from sklearn.preprocessing import normalize
foo[:, [-1]] = normalize(foo[:, -1, None], norm='max', axis=0)
</code></pre>
<p/>
<pre><code>foo
array([[ 0. , 0.2378106 ],
[ 0.13216 , 0.28818769],
[ 0.25379 , 1. ],
[ 0.30874 , 0.31195614]])
</code></pre>