上一篇文章讲解了数据预处理区间缩放法，这篇文章主要讲解数据归一化处理
既然讲到归一化和前面的标准化需要对比一下，首先二者处理的维度是不一样的，假设我们要处理的矩阵是m*n大小，m个样本，n维特征
标准化处理的方式是以列为单位，也就是处理的对象是
$$m*k k={1……..n}$$
归一化处理的方式却是以行为单位，处理的对象如下：
$$k*n k={1……..m}$$
归一化处理对样本的特征向量处理之后使其变为单位向量，单位向量可以用于点积运算或者计算样本之间相似度
L2归一化的计算公式如下所示：
$$ y=\frac{x}{\sqrt{\sum_{i=0}^{n}x^2}} $$
L1归一化的计算公式如下所示
$$ y=\frac{x}{\sum_{i=0}^{n}\|x\|} $$
sklearn代码
from sklearn.preprocessing import Normalizer tmp=Normalizer().fit_transform(irisdata.data) print tmp[0:5]
实验结果如下
[[ 0.80377277 0.55160877 0.22064351 0.0315205 ] [ 0.82813287 0.50702013 0.23660939 0.03380134] [ 0.80533308 0.54831188 0.2227517 0.03426949] [ 0.80003025 0.53915082 0.26087943 0.03478392] [ 0.790965 0.5694948 0.2214702 0.0316386 ]]
spark代码如下
<span class="kn">from</span> <span class="nn">pyspark.ml.feature</span> <span class="kn">import</span> <span class="n">Normalizer</span> <span class="n">dataFrame</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">read</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s">"libsvm"</span><span class="p">)</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s">"data/mllib/sample_libsvm_data.txt"</span><span class="p">)</span> <span class="c"># p=2则表示是l2归一化</span> <span class="n">normalizer</span> <span class="o">=</span> <span class="n">Normalizer</span><span class="p">(</span><span class="n">inputCol</span><span class="o">=</span><span class="s">"features"</span><span class="p">,</span> <span class="n">outputCol</span><span class="o">=</span><span class="s">"normFeatures"</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span> <span class="n">l1NormData</span> <span class="o">=</span> <span class="n">normalizer</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">dataFrame</span><span class="p">)</span> <span class="n">l1NormData</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
