Java机器学习库ML之三Sampling(采样)

场景：从样本集中采样80%用于训练，20%用于验证。

参考代码如下：

package com.gddx;import java.io.File; import java.util.Map;import libsvm.LibSVM; import net.sf.javaml.classification.Classifier; import net.sf.javaml.classification.evaluation.EvaluateDataset; import net.sf.javaml.classification.evaluation.PerformanceMeasure; import net.sf.javaml.core.Dataset; import net.sf.javaml.sampling.Sampling; import net.sf.javaml.tools.data.FileHandler; import be.abeel.util.Pair;/*** Sample program illustrating how to use sampling.* * @author Thomas Abeel* */ public class TutorialSampling {public static void main(String[] args) throws Exception {Dataset data = FileHandler.loadDataset(new File("D:\\tmp\\javaml-0.1.7-src\\UCI-small\\iris\\iris.data"), 4, ",");Sampling s = Sampling.SubSampling;Pair<Dataset, Dataset> datass = s.sample(data, (int) (data.size() * 0.8));System.out.println(datass.x().instance(0));//训练集System.out.println(datass.y().instance(0));//测试集Classifier c = new LibSVM();c.buildClassifier(datass.x());Map<Object,PerformanceMeasure> pms = EvaluateDataset.testDataset(c, datass.y());System.out.println(pms);/*for (int i = 0; i < 5; i++) {Pair<Dataset, Dataset> datas = s.sample(data, (int) (data.size() * 0.8), i);Classifier c = new LibSVM();c.buildClassifier(datas.x());Map<Object,PerformanceMeasure> pms = EvaluateDataset.testDataset(c, datas.y());System.out.println(pms);}*/} }

《新程序员》：云原生和全面数字化实践50位技术专家共同创作，文字、视频、音频交互阅读

总结

以上是生活随笔为你收集整理的Java机器学习库ML之三Sampling(采样)的全部内容，希望文章能够帮你解决所遇到的问题。

如果觉得生活随笔网站内容还不错，欢迎将生活随笔推荐给好友。