127.0.0.1:9200<br>URL: http://127.0.0.1:9200/likecs_art_db/_search<br>REQUEST:<pre>Array
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</pre>RESPONSE:<pre>string(7509) "{"took":28,"timed_out":false,"_shards":{"total":1,"successful":1,"skipped":0,"failed":0},"hits":{"total":{"value":10000,"relation":"gte"},"max_score":36.49925,"hits":[{"_index":"likecs_art_db","_type":"_doc","_id":"62784","_score":36.49925,"_source":{"id":"62784","text":"KNN\u7b97\u6cd5\u2014\u2014\u5206\u7c7b\u90e8\u5206","intro":"\u76ee\u5f55\n\nECharts\n\u5f02\u6b65\u52a0\u8f7d\n\n\n\nECharts\r\n\u6570\u636e\u53ef\u89c6\u5316\u5728\u8fc7\u53bb\u51e0\u5e74\u4e2d\u53d6\u5f97\u4e86\u5de8\u5927\u8fdb\u5c55\u3002\u5f00\u53d1\u4eba\u5458\u5bf9\u53ef\u89c6\u5316\u4ea7\u54c1\u7684\u671f\u671b\u4e0d\u518d\u662f\u7b80\u5355\u7684\u56fe\u8868\u521b\u5efa\u5de5\u5177\uff0c\u800c\u662f\u5728\u4ea4\u4e92\u3001\u6027\u80fd\u3001\u6570\u636e\u5904\u7406\u7b49\u65b9\u9762\u6709\u66f4\u9ad8\u7684\u8981\u6c42\u3002\r\nchart.setOption({\r\n    color: [\r\n        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</pre>127.0.0.1:9200<br>URL: http://127.0.0.1:9200/likecs_art_db/_search<br>REQUEST:<pre>Array
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    [size] => 8
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</pre>RESPONSE:<pre>string(7825) "{"took":23,"timed_out":false,"_shards":{"total":1,"successful":1,"skipped":0,"failed":0},"hits":{"total":{"value":10000,"relation":"gte"},"max_score":36.49925,"hits":[{"_index":"likecs_art_db","_type":"_doc","_id":"203462219","_score":25.972942,"_source":{"id":"203462219","text":"\u673a\u5668\u5b66\u4e60\u7b97\u6cd5--KNN\u8fd1\u90bb\u5206\u7c7b\u7b97\u6cd5","intro":"\u76ee\u5f55\n\nECharts\n\u5f02\u6b65\u52a0\u8f7d\n\n\n\nECharts\r\n\u6570\u636e\u53ef\u89c6\u5316\u5728\u8fc7\u53bb\u51e0\u5e74\u4e2d\u53d6\u5f97\u4e86\u5de8\u5927\u8fdb\u5c55\u3002\u5f00\u53d1\u4eba\u5458\u5bf9\u53ef\u89c6\u5316\u4ea7\u54c1\u7684\u671f\u671b\u4e0d\u518d\u662f\u7b80\u5355\u7684\u56fe\u8868\u521b\u5efa\u5de5\u5177\uff0c\u800c\u662f\u5728\u4ea4\u4e92\u3001\u6027\u80fd\u3001\u6570\u636e\u5904\u7406\u7b49\u65b9\u9762\u6709\u66f4\u9ad8\u7684\u8981\u6c42\u3002\r\nchart.setOption({\r\n    color: [\r\n        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                        <h1 class="page-title"><a href="/show-62784.html" title="KNN算法——分类部分">KNN算法——分类部分</a></h1>
            
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            <footer class="tc"> weiying121 
                <time class="post-date" datetime="2019-06-29">2019-06-29</time>
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    <h3><a id="cb_post_title_url" href="/default/index/url?u=aHR0cHM6Ly93d3cuY25ibG9ncy5jb20vd2VpeWluZzEyMS9wLzExMTA2ODc3Lmh0bWw=" target="_blank">KNN算法——分类部分</a></h3>
    <small>2019-06-29 16:06 by ssswy, <span id="post_view_count">...</span> 阅读, <span id="post_comment_count">...</span> 评论, <a href="#" onclick="AddToWz(11106877);return false;" rel="nofollow" target="_blank">收藏</a>,  <a href="/default/index/url?u=aHR0cHM6Ly9pLmNuYmxvZ3MuY29tL0VkaXRQb3N0cy5hc3B4P3Bvc3RpZD0xMTEwNjg3Nw==" rel="nofollow" target="_blank">编辑</a></small>
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<p><strong>1.</strong><strong>核心思想</strong></p>
<p>如果一个样本在特征空间中的k个最相邻的样本中的大多数属于某一个类别，则该样本也属于这个类别，并具有这个类别上样本的特性。也就是说找出一个样本的k个最近邻居，将这些邻居的属性的平均值赋给该样本，就可以得到该样本的属性。</p>
<p>下面看一个例子，</p>
<p>  一个程序员面试结束后，想想知道是否拿到offer，他在网上找到几个人的工作经历和大概薪资，如下，X为年龄，Y为工资;</p>
<p>    <img src="/default/index/img?u=L2RlZmF1bHQvaW5kZXgvaW1nP3U9YUhSMGNITTZMeTlwYldjeU1ERTRMbU51WW14dlozTXVZMjl0TDJKc2IyY3ZNVE0yT1RBM055OHlNREU1TURZdk1UTTJPVEEzTnkweU1ERTVNRFl5T1RFMk1ETXdNakEyTUMweE5UVTNOakkzTVRNMUxuQnVadz09" alt=""></p>
<p> </p>
<p>当k取1的时候，我们可以看出距离最近的no offer，因此得到目标点为不被录用。</p>
<p>当k取3的时候，我们可以看出距离最近的三个，分别是有offer 和no offer，根据投票决定 offer的票数较高为2 ，所以被录用。</p>
<p> </p>
<p><strong>算法流程</strong></p>
<p>1. 准备数据，对数据进行预处理，常用方法，特征归一化、类别型特征的处理、高维组合特征的处理、组合特征的处理、文本表示模型的模型处理、Word2Vec、图像数据不足时的处理方法</p>
<p>    </p>
<p>2. 选用合适的数据结构存储训练数据和测试元组，根据模型验证方法，把样本划分不同的训练集和测试集，比如holdout只需要划分为两个部分，交叉验证划分为k个子集，自助法跟着模型来</p>
<p>3. 设定参数，如k的取值，这个涉及到超参数调优的问题，网络搜索、随机搜索、贝叶斯算法等</p>
<p>4.维护一个大小为k的的按距离由大到小的优先级队列，用于存储最近邻训练元组。随机从训练元组中选取k个元组作为初始的最近邻元组，分别计算测试元组到这k个元组的距离，将训练元组标号和距离存入优先级队列</p>
<p>5. 遍历训练元组集，计算当前训练元组与测试元组的距离，将所得距离L 与优先级队列中的最大距离Lmax</p>
<p>6. 进行比较。若L&gt;=Lmax，则舍弃该元组，遍历下一个元组。若L &lt; Lmax，删除优先级队列中最大距离的元组，将当前训练元组存入优先级队列。</p>
<p>7. 遍历完毕，计算优先级队列中k 个元组的多数类，并将其作为测试元组的类别。</p>
<p>8. 测试元组集测试完毕后计算误差率，继续设定不同的k值重新进行训练，最后取误差率最小的k 值。</p>
<p> </p>
<p>简单来说，knn算法最重要的是三个要素：K值选择,距离度量,分类决策规则，</p>
<p> </p>
<p><strong>K的选择</strong></p>
<p>  如k的取值，这个涉及到超参数调优的问题，k的取值对结果会有很大的影响。K值设置过小会降低分类精度，增加模型复杂度；若设置过大，且测试样本属于训练集中包含数据较少的类，则会增加噪声，降低分类效果。通常，K值的设定采用交叉检验的方式（以K=1，K=2，K=3依次进行），K折交叉验证如下：</p>
<p>  </p>
<p>    1）  将全部训练集S分成K个不相交的子集，假设S中的训练样例个数为m，那么每一个子集有m/k个训练样例。</p>
<p> </p>
<p>     （2）  每次从分好的子集中，选出一个作为测试集，另外k-1个作为训练集。</p>
<p> </p>
<p>     （3）  根据训练集得到模型。</p>
<p> </p>
<p>     （4）  根据模型对测试集进行测试，得到分类率。</p>
<p> </p>
<p>     （5）  计算k次求得的分类率的平均值，作为模型的最终分类率。</p>
<p>以五折交叉验证为例;</p>
<p> </p>
<p> <img src="/default/index/img?u=L2RlZmF1bHQvaW5kZXgvaW1nP3U9YUhSMGNITTZMeTlwYldjeU1ERTRMbU51WW14dlozTXVZMjl0TDJKc2IyY3ZNVE0yT1RBM055OHlNREU1TURZdk1UTTJPVEEzTnkweU1ERTVNRFl5T1RFMk1ETTBNVFkyTnkweU1EZ3lNRFExTXpRd0xuQnVadz09" alt=""></p>
<p> </p>
<p> </p>
<p>分别得出K=1时的平均分类准确度、K=1时的平均分类准确度……选出最优K值</p>
<p> </p>
<p> </p>
<p> </p>
<p><strong>距离度量</strong></p>
<p> </p>
<p>在KNN算法中，常用的距离有三种，分别为曼哈顿距离、欧式距离和闵可夫斯基距离。</p>
<p>距离通式：</p>
<p> </p>
<p>当p=1时，称为曼哈顿距离</p>
<p> </p>
<p> <img src="/default/index/img?u=L2RlZmF1bHQvaW5kZXgvaW1nP3U9YUhSMGNITTZMeTlwYldjeU1ERTRMbU51WW14dlozTXVZMjl0TDJKc2IyY3ZNVE0yT1RBM055OHlNREU1TURZdk1UTTJPVEEzTnkweU1ERTVNRFl5T1RFMk1ETTFORFUyTlMwNE16Z3pOVE0zTWpVdWNHNW4=" alt=""></p>
<p> </p>
<p>当p=2时，称为欧式距离</p>
<p> </p>
<p> <img src="/default/index/img?u=L2RlZmF1bHQvaW5kZXgvaW1nP3U9YUhSMGNITTZMeTlwYldjeU1ERTRMbU51WW14dlozTXVZMjl0TDJKc2IyY3ZNVE0yT1RBM055OHlNREU1TURZdk1UTTJPVEEzTnkweU1ERTVNRFl5T1RFMk1EUXdOelF4T1MweU1USXlNVGs0T1RNMUxuQnVadz09" alt=""></p>
<p> </p>
<p>当p=∞时，</p>
<p> </p>
<p> <img src="/default/index/img?u=L2RlZmF1bHQvaW5kZXgvaW1nP3U9YUhSMGNITTZMeTlwYldjeU1ERTRMbU51WW14dlozTXVZMjl0TDJKc2IyY3ZNVE0yT1RBM055OHlNREU1TURZdk1UTTJPVEEzTnkweU1ERTVNRFl5T1RFMk1EUXhOVGMxTnkwM05ETTNNek16TWpFdWNHNW4=" alt=""></p>
<p> </p>
<p> </p>
<p><strong>分类决策规则</strong></p>
<p>：</p>
<p> 1.多数表决：少数服从多数，即训练集里和预测的样本特征最近的K个样本，预测为里面有最多类别数的类别</p>
<p> </p>
<p> </p>
<p>2.加权表决：根据各个邻居与测试对象距离的远近来分配相应的投票权重。最简单的就是取两者距离之间的倒数，距离越小，越相似，权重越大，将权重累加，最后选择累加值最高类别属性作为该待测样本点的类别，类似大众评审和专家评审。</p>
<p> </p>
<p>     这两种确简单直接，在样本量少，样本特征少的时候有效，只适合数据量小的情况。因为我们经常碰到样本的特征数有上千以上，样本量有几十万以上，如果我们这要去预测少量的测试集样本，算法的时间效率很成问题。因此，这个方法我们一般称之为蛮力实现。比较适合于少量样本的简单模型的时候用。一个是KD树实现，一个是球树实现。</p>
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