上一篇文章讲解了区间缩放法处理数据，接下来就讲解二值化处理
这个应该很简单了，从字面意思就是将数据分为0或者1，联想到之前图像处理里面二值化处理变为黑白图片
下面还是进入主题吧
首先给出当前的二值化处理公式：
$$ y = \left\{ \begin{array}{ll} 0 & \textrm{if \(x<=\theta\)}\\ 1& \textrm{if \(x>\theta\)}\\ \end{array} \right. $$
上图中\(\theta\)是设定的阈值，特征值与阈值相比较，大于阈值则为1小于等于阈值为0
sklearn函数
from sklearn.preprocessing import Binarizer tmp=Binarizer().fit_transform(irisdata.data) print tmp[0:5]
输出结果
[[ 1. 1. 1. 1.] [ 1. 1. 1. 1.] [ 1. 1. 1. 1.] [ 1. 1. 1. 1.] [ 1. 1. 1. 1.]]
spark 函数
<span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">([(</span><span class="mf">0.5</span><span class="p">,)],</span> <span class="p">[</span><span class="s">"values"</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">binarizer</span> <span class="o">=</span> <span class="n">Binarizer</span><span class="p">(</span><span class="n">threshold</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">inputCol</span><span class="o">=</span><span class="s">"values"</span><span class="p">,</span> <span class="n">outputCol</span><span class="o">=</span><span class="s">"features"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">binarizer</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">df</span><span class="p">)</span><span class="o">.</span><span class="n">head</span><span class="p">()</span><span class="o">.</span><span class="n">features</span>
<span class="go">0.0
</span>#setParams是用来设置二值化参数
<span class="gp">>>> </span><span class="n">binarizer</span><span class="o">.</span><span class="n">setParams</span><span class="p">(</span><span class="n">outputCol</span><span class="o">=</span><span class="s">"freqs"</span><span class="p">)</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">df</span><span class="p">)</span><span class="o">.</span><span class="n">head</span><span class="p">()</span><span class="o">.</span><span class="n">freqs</span>
<span class="go">0.0</span>
<span class="gp">>>> </span><span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="n">binarizer</span><span class="o">.</span><span class="n">threshold</span><span class="p">:</span> <span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">binarizer</span><span class="o">.</span><span class="n">outputCol</span><span class="p">:</span> <span class="s">"vector"</span><span class="p">}</span>
<span class="gp">>>> </span><span class="n">binarizer</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">head</span><span class="p">()</span><span class="o">.</span><span class="n">vector</span>
<span class="go">1.0</span>
transform(dataset, params=None)，其中param可以是字典参数，字典的键是类对象成员
