本文整理汇总了Java中weka.clusterers.EM.setOptions方法的典型用法代码示例。如果您正苦于以下问题：Java EM.setOptions方法的具体用法？Java EM.setOptions怎么用？Java EM.setOptions使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类weka.clusterers.EM的用法示例。
在下文中一共展示了EM.setOptions方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: createClusterer
import weka.clusterers.EM; //导入方法依赖的package包/类
/**
*
* @param trainingData
* @param round
* @throws Exception
*/
protected AbstractClusterer createClusterer(MarkovAttributeSet aset, Instances trainingData) throws Exception {
if (trace.val) LOG.trace(String.format("Clustering %d %s instances with %d attributes", trainingData.numInstances(), CatalogUtil.getDisplayName(catalog_proc), aset.size()));
// Create the filter we need so that we only include the attributes in the given MarkovAttributeSet
Filter filter = aset.createFilter(trainingData);
// Using our training set to build the clusterer
int seed = this.rand.nextInt();
// SimpleKMeans inner_clusterer = new SimpleKMeans();
EM inner_clusterer = new EM();
String options[] = {
"-N", Integer.toString(1000), // num_partitions),
"-S", Integer.toString(seed),
"-I", Integer.toString(100),
};
inner_clusterer.setOptions(options);
FilteredClusterer filtered_clusterer = new FilteredClusterer();
filtered_clusterer.setFilter(filter);
filtered_clusterer.setClusterer(inner_clusterer);
AbstractClusterer clusterer = filtered_clusterer;
clusterer.buildClusterer(trainingData);
return (clusterer);
}
示例2: performClustering
import weka.clusterers.EM; //导入方法依赖的package包/类
@Override
public ClusteringResult performClustering(Instances dataset,
ParameterSet parameters) {
List<Integer> clusters = new ArrayList<Integer>();
String[] options = new String[2];
EM clusterer = new EM();
int numberOfIterations = parameters.getParameter(
EMClustererParameters.numberOfIterations).getValue();
options[0] = "-I";
options[1] = String.valueOf(numberOfIterations);
try {
clusterer.setOptions(options);
clusterer.buildClusterer(dataset);
Enumeration<?> e = dataset.enumerateInstances();
while (e.hasMoreElements()) {
clusters.add(clusterer.clusterInstance((Instance) e
.nextElement()));
}
ClusteringResult result = new ClusteringResult(clusters, null,
clusterer.numberOfClusters(), parameters.getParameter(
EMClustererParameters.visualization).getValue());
return result;
} catch (Exception ex) {
logger.log(Level.SEVERE, null, ex);
return null;
}
}
示例3: getClusterer
import weka.clusterers.EM; //导入方法依赖的package包/类
/**
* returns a configured cluster algorithm
*/
protected Clusterer getClusterer() {
EM c = new EM();
try {
c.setOptions(new String[0]);
}
catch (Exception e) {
e.printStackTrace();
}
return c;
}