机器学习weka，java api调用随机森林及保存模型

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工作需要，了解了一下weka的java api，主要是随机森林这一块，刚开始学习，记录下。
了解不多，直接上demo，里面有一些注释说明：

<span class="hljs-keyword">package</span> weka;

<span class="hljs-keyword">import</span> java.io.File;

<span class="hljs-keyword">import</span> weka.classifiers.Classifier;
<span class="hljs-keyword">import</span> weka.classifiers.trees.RandomForest;
<span class="hljs-keyword">import</span> weka.core.Instances;
<span class="hljs-keyword">import</span> weka.core.SerializationHelper;
<span class="hljs-keyword">import</span> weka.core.converters.ArffLoader;

<span class="hljs-keyword">public</span> <span class="hljs-class"><span class="hljs-keyword">class</span> <span class="hljs-title">demo</span> </span>{

    <span class="hljs-function"><span class="hljs-keyword">public</span> <span class="hljs-keyword">static</span> <span class="hljs-keyword">void</span> <span class="hljs-title">main</span><span class="hljs-params">(String[] args)</span> <span class="hljs-keyword">throws</span> Exception </span>{
        
        Classifier m_classifier = <span class="hljs-keyword">new</span> RandomForest();  
        File inputFile = <span class="hljs-keyword">new</span> File(<span class="hljs-string">"F:/java/weka/trainData.arff"</span>);<span class="hljs-comment">//训练语料文件  </span>
        ArffLoader atf = <span class="hljs-keyword">new</span> ArffLoader();   
        atf.setFile(inputFile);  
        Instances instancesTrain = atf.getDataSet(); <span class="hljs-comment">// 读入训练文件      </span>
        inputFile = <span class="hljs-keyword">new</span> File(<span class="hljs-string">"F:/java/weka/testData.arff"</span>);<span class="hljs-comment">//测试语料文件  </span>
        atf.setFile(inputFile);            
        Instances instancesTest = atf.getDataSet(); <span class="hljs-comment">// 读入测试文件  </span>
        instancesTest.setClassIndex(<span class="hljs-number">0</span>); <span class="hljs-comment">//设置分类属性所在行号（第一行为0号），instancesTest.numAttributes()可以取得属性总数  </span>
        <span class="hljs-keyword">double</span> sum = instancesTest.numInstances(),<span class="hljs-comment">//测试语料实例数  </span>
        right = <span class="hljs-number">0.0f</span>;  
        instancesTrain.setClassIndex(<span class="hljs-number">0</span>);  
        m_classifier.buildClassifier(instancesTrain); <span class="hljs-comment">//训练</span>
        System.out.println(m_classifier);
        
        <span class="hljs-comment">// 保存模型</span>
        SerializationHelper.write(<span class="hljs-string">"LibSVM.model"</span>, m_classifier);<span class="hljs-comment">//参数一为模型保存文件，classifier4为要保存的模型</span>
        
        <span class="hljs-keyword">for</span>(<span class="hljs-keyword">int</span>  i = <span class="hljs-number">0</span>;i&lt;sum;i++)<span class="hljs-comment">//测试分类结果  1</span>
        {  
            <span class="hljs-keyword">if</span>(m_classifier.classifyInstance(instancesTest.instance(i))==instancesTest.instance(i).classValue())<span class="hljs-comment">//如果预测值和答案值相等（测试语料中的分类列提供的须为正确答案，结果才有意义）  </span>
            {  
                right++;<span class="hljs-comment">//正确值加1  </span>
            }  
        } 
        
        <span class="hljs-comment">// 获取上面保存的模型</span>
        Classifier classifier8 = (Classifier) weka.core.SerializationHelper.read(<span class="hljs-string">"LibSVM.model"</span>); 
        <span class="hljs-keyword">double</span> right2 = <span class="hljs-number">0.0f</span>;  
        <span class="hljs-keyword">for</span>(<span class="hljs-keyword">int</span>  i = <span class="hljs-number">0</span>;i&lt;sum;i++)<span class="hljs-comment">//测试分类结果  2 (通过)</span>
        {  
            <span class="hljs-keyword">if</span>(classifier8.classifyInstance(instancesTest.instance(i))==instancesTest.instance(i).classValue())<span class="hljs-comment">//如果预测值和答案值相等（测试语料中的分类列提供的须为正确答案，结果才有意义）  </span>
            {  
                right2++;<span class="hljs-comment">//正确值加1  </span>
            }  
        } 
        System.out.println(right);
        System.out.println(right2);
        System.out.println(sum);
        System.out.println(<span class="hljs-string">"RandomForest classification precision:"</span>+(right/sum));  
    }
}