本文整理汇总了Java中weka.clusterers.AbstractClusterer类的典型用法代码示例。如果您正苦于以下问题:Java AbstractClusterer类的具体用法?Java AbstractClusterer怎么用?Java AbstractClusterer使用的例子?那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
AbstractClusterer类属于weka.clusterers包,在下文中一共展示了AbstractClusterer类的20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,您的评价将有助于我们的系统推荐出更棒的Java代码示例。
示例1: setOptions
import weka.clusterers.AbstractClusterer; //导入依赖的package包/类
/**
* Parses a given list of options. Valid options are:
* <p>
*
* -W classname <br>
* Specify the full class name of the clusterer to evaluate.
* <p>
*
* All option after -- will be passed to the classifier.
*
* @param options the list of options as an array of strings
* @exception Exception if an option is not supported
*/
@Override
public void setOptions(String[] options) throws Exception {
m_NoSizeDetermination = Utils.getFlag("no-size", options);
String cName = Utils.getOption('W', options);
if (cName.length() == 0) {
throw new Exception("A clusterer must be specified with"
+ " the -W option.");
}
// Do it first without options, so if an exception is thrown during
// the option setting, listOptions will contain options for the actual
// Classifier.
setClusterer((DensityBasedClusterer) AbstractClusterer.forName(cName, null));
if (getClusterer() instanceof OptionHandler) {
((OptionHandler) getClusterer()).setOptions(Utils
.partitionOptions(options));
updateOptions();
}
}
开发者ID:mydzigear,项目名称:repo.kmeanspp.silhouette_score,代码行数:33,代码来源:DensityBasedClustererSplitEvaluator.java
示例2: setOptions
import weka.clusterers.AbstractClusterer; //导入依赖的package包/类
/**
* Parses a given list of options. Valid options are:<p>
*
* -W classname <br>
* Specify the full class name of the clusterer to evaluate. <p>
*
* All option after -- will be passed to the classifier.
*
* @param options the list of options as an array of strings
* @exception Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
m_NoSizeDetermination = Utils.getFlag("no-size", options);
String cName = Utils.getOption('W', options);
if (cName.length() == 0) {
throw new Exception("A clusterer must be specified with"
+ " the -W option.");
}
// Do it first without options, so if an exception is thrown during
// the option setting, listOptions will contain options for the actual
// Classifier.
setClusterer((DensityBasedClusterer)AbstractClusterer.forName(cName, null));
if (getClusterer() instanceof OptionHandler) {
((OptionHandler) getClusterer())
.setOptions(Utils.partitionOptions(options));
updateOptions();
}
}
开发者ID:dsibournemouth,项目名称:autoweka,代码行数:30,代码来源:DensityBasedClustererSplitEvaluator.java
示例3: setOptions
import weka.clusterers.AbstractClusterer; //导入依赖的package包/类
/**
* Parses a given list of options. Valid options are:
* <p>
*
* -W classname <br>
* Specify the full class name of the clusterer to evaluate.
* <p>
*
* All option after -- will be passed to the classifier.
*
* @param options the list of options as an array of strings
* @exception Exception if an option is not supported
*/
@Override
public void setOptions(String[] options) throws Exception {
m_NoSizeDetermination = Utils.getFlag("no-size", options);
String cName = Utils.getOption('W', options);
if (cName.length() == 0) {
throw new Exception("A clusterer must be specified with"
+ " the -W option.");
}
// Do it first without options, so if an exception is thrown during
// the option setting, listOptions will contain options for the actual
// Classifier.
setClusterer((DensityBasedClusterer) AbstractClusterer.forName(cName, null));
if (getClusterer() instanceof OptionHandler) {
((OptionHandler) getClusterer()).setOptions(Utils
.partitionOptions(options));
updateOptions();
}
}
开发者ID:umple,项目名称:umple,代码行数:33,代码来源:DensityBasedClustererSplitEvaluator.java
示例4: setOptions
import weka.clusterers.AbstractClusterer; //导入依赖的package包/类
/**
* Parses a given list of options. Valid options are:<p>
*
* -W classname <br>
* Specify the full class name of the clusterer to evaluate. <p>
*
* All option after -- will be passed to the classifier.
*
* @param options the list of options as an array of strings
* @exception Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
String cName = Utils.getOption('W', options);
if (cName.length() == 0) {
throw new Exception("A clusterer must be specified with"
+ " the -W option.");
}
// Do it first without options, so if an exception is thrown during
// the option setting, listOptions will contain options for the actual
// Classifier.
setClusterer((DensityBasedClusterer)AbstractClusterer.forName(cName, null));
if (getClusterer() instanceof OptionHandler) {
((OptionHandler) getClusterer())
.setOptions(Utils.partitionOptions(options));
updateOptions();
}
}
开发者ID:williamClanton,项目名称:jbossBA,代码行数:29,代码来源:DensityBasedClustererSplitEvaluator.java
示例5: createHistogram
import weka.clusterers.AbstractClusterer; //导入依赖的package包/类
private Map<Integer, Integer> createHistogram(AbstractClusterer clusterer, Instances instances) {
Map<Integer, Integer> histogram = new TreeMap<Integer, Integer>();
for (int i = 0; i < instances.numInstances(); i++) {
Instance currInst = instances.instance(i);
try {
int cluster = clusterer.clusterInstance(currInst);
histogram.put(cluster, histogram.containsKey(cluster) ? histogram.get(cluster) + 1 : 1);
} catch (Exception e) {
// Noise
e.printStackTrace();
histogram.put(-1, histogram.containsKey(-1) ? histogram.get(-1) + 1 : 1);
}
}
return histogram;
}
开发者ID:kokojumbo,项目名称:master-thesis,代码行数:19,代码来源:DBScanImbalancedAlgorithm.java
示例6: createHistogram
import weka.clusterers.AbstractClusterer; //导入依赖的package包/类
public static Map<Integer, Integer> createHistogram(AbstractClusterer clusterer, Instances instances) {
Map<Integer, Integer> histogram = new TreeMap<Integer, Integer>();
for (int i = 0; i < instances.numInstances(); i++) {
Instance currInst = instances.instance(i);
try {
int cluster = clusterer.clusterInstance(currInst);
histogram.put(cluster, histogram.containsKey(cluster) ? histogram.get(cluster) + 1 : 1);
} catch (Exception e) {
// Noise
//e.printStackTrace();
histogram.put(-1, histogram.containsKey(-1) ? histogram.get(-1) + 1 : 1);
}
}
return histogram;
}
开发者ID:kokojumbo,项目名称:master-thesis,代码行数:19,代码来源:ImbalancedUtils.java
示例7: createClusterer
import weka.clusterers.AbstractClusterer; //导入依赖的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);
}
开发者ID:s-store,项目名称:sstore-soft,代码行数:34,代码来源:FeatureClusterer.java
示例8: init
import weka.clusterers.AbstractClusterer; //导入依赖的package包/类
public void init(AbstractClusterer clusterer) {
this.clusterer = clusterer;
}
开发者ID:s-store,项目名称:sstore-soft,代码行数:4,代码来源:FeatureClusterer.java
示例9: generateDecisionTree
import weka.clusterers.AbstractClusterer; //导入依赖的package包/类
protected Classifier generateDecisionTree(AbstractClusterer clusterer, MarkovAttributeSet aset, Instances data) throws Exception {
// We need to create a new Attribute that has the ClusterId
Instances newData = data; // new Instances(data);
newData.insertAttributeAt(new Attribute("ClusterId"), newData.numAttributes());
Attribute cluster_attr = newData.attribute(newData.numAttributes()-1);
assert(cluster_attr != null);
assert(cluster_attr.index() > 0);
newData.setClass(cluster_attr);
// We will then tell the Classifier to predict that ClusterId based on the MarkovAttributeSet
ObjectHistogram<Integer> cluster_h = new ObjectHistogram<Integer>();
for (int i = 0, cnt = newData.numInstances(); i < cnt; i++) {
// Grab the Instance and throw it at the the clusterer to get the target cluster
Instance inst = newData.instance(i);
int c = (int)clusterer.clusterInstance(inst);
inst.setClassValue(c);
cluster_h.put(c);
} // FOR
System.err.println("Number of Elements: " + cluster_h.getValueCount());
System.err.println(cluster_h);
NumericToNominal filter = new NumericToNominal();
filter.setInputFormat(newData);
newData = Filter.useFilter(newData, filter);
String output = this.catalog_proc.getName() + "-labeled.arff";
FileUtil.writeStringToFile(output, newData.toString());
LOG.info("Wrote labeled data set to " + output);
// Decision Tree
J48 j48 = new J48();
String options[] = {
"-S", Integer.toString(this.rand.nextInt()),
};
j48.setOptions(options);
// Make sure we add the ClusterId attribute to a new MarkovAttributeSet so that
// we can tell the Classifier to classify that!
FilteredClassifier fc = new FilteredClassifier();
MarkovAttributeSet classifier_aset = new MarkovAttributeSet(aset);
classifier_aset.add(cluster_attr);
fc.setFilter(classifier_aset.createFilter(newData));
fc.setClassifier(j48);
// Bombs away!
fc.buildClassifier(newData);
return (fc);
}
开发者ID:s-store,项目名称:sstore-soft,代码行数:51,代码来源:FeatureClusterer.java
示例10: testCreateClusterer
import weka.clusterers.AbstractClusterer; //导入依赖的package包/类
/**
* testCreateClusterer
*/
@Test
public void testCreateClusterer() throws Exception {
// Construct a simple MarkovAttributeSet that only contains the BasePartitionFeature
MarkovAttributeSet base_aset = new MarkovAttributeSet(data, FeatureUtil.getFeatureKeyPrefix(BasePartitionFeature.class));
assertFalse(base_aset.isEmpty());
int base_partition_idx = CollectionUtil.first(base_aset).index();
AbstractClusterer clusterer = this.fclusterer.createClusterer(base_aset, data);
assertNotNull(clusterer);
// Make sure that each Txn gets mapped to the same cluster as its base partition
Map<Integer, Histogram<Integer>> p_c_xref = new HashMap<Integer, Histogram<Integer>>();
for (int i = 0, cnt = data.numInstances(); i < cnt; i++) {
Instance inst = data.instance(i);
assertNotNull(inst);
long txn_id = FeatureUtil.getTransactionId(inst);
TransactionTrace txn_trace = workload.getTransaction(txn_id);
assertNotNull(txn_trace);
Integer base_partition = p_estimator.getBasePartition(txn_trace);
assertNotNull(base_partition);
assertEquals(base_partition.intValue(), (int)inst.value(base_partition_idx));
int c = clusterer.clusterInstance(inst);
Histogram<Integer> h = p_c_xref.get(base_partition);
if (h == null) {
h = new ObjectHistogram<Integer>();
p_c_xref.put(base_partition, h);
}
h.put(c);
} // FOR
// System.err.println(StringUtil.formatMaps(p_c_xref));
// Set<Integer> c_p_xref = new HashSet<Integer>();
// for (Entry<Integer, Histogram> e : p_c_xref.entrySet()) {
// Set<Integer> clusters = e.getValue().values();
//
// // Make sure that each base partition is only mapped to one cluster
// assertEquals(e.getKey().toString(), 1, clusters.size());
//
// // Make sure that two different base partitions are not mapped to the same cluster
// assertFalse(c_p_xref.contains(CollectionUtil.getFirst(clusters)));
// c_p_xref.addAll(clusters);
// } // FOR
}
开发者ID:s-store,项目名称:sstore-soft,代码行数:49,代码来源:TestFeatureClusterer.java
示例11: batchFinished
import weka.clusterers.AbstractClusterer; //导入依赖的package包/类
/**
* Signify that this batch of input to the filter is finished.
*
* @return true if there are instances pending output
* @throws IllegalStateException if no input structure has been defined
*/
@Override
public boolean batchFinished() throws Exception {
if (getInputFormat() == null) {
throw new IllegalStateException("No input instance format defined");
}
Instances toFilter = getInputFormat();
if (!isFirstBatchDone()) {
// filter out attributes if necessary
Instances toFilterIgnoringAttributes = removeIgnored(toFilter);
// serialized model or build clusterer from scratch?
File file = getSerializedClustererFile();
if (!file.isDirectory()) {
ObjectInputStream ois = new ObjectInputStream(new FileInputStream(file));
m_ActualClusterer = (Clusterer) ois.readObject();
Instances header = null;
// let's see whether there's an Instances header stored as well
try {
header = (Instances) ois.readObject();
} catch (Exception e) {
// ignored
}
ois.close();
// same dataset format?
if ((header != null)
&& (!header.equalHeaders(toFilterIgnoringAttributes))) {
throw new WekaException(
"Training header of clusterer and filter dataset don't match:\n"
+ header.equalHeadersMsg(toFilterIgnoringAttributes));
}
} else {
m_ActualClusterer = AbstractClusterer.makeCopy(m_Clusterer);
m_ActualClusterer.buildClusterer(toFilterIgnoringAttributes);
}
// create output dataset with new attribute
Instances filtered = new Instances(toFilter, 0);
ArrayList<String> nominal_values = new ArrayList<String>(
m_ActualClusterer.numberOfClusters());
for (int i = 0; i < m_ActualClusterer.numberOfClusters(); i++) {
nominal_values.add("cluster" + (i + 1));
}
filtered.insertAttributeAt(new Attribute("cluster", nominal_values),
filtered.numAttributes());
setOutputFormat(filtered);
}
// build new dataset
for (int i = 0; i < toFilter.numInstances(); i++) {
convertInstance(toFilter.instance(i));
}
flushInput();
m_NewBatch = true;
m_FirstBatchDone = true;
return (numPendingOutput() != 0);
}
开发者ID:mydzigear,项目名称:repo.kmeanspp.silhouette_score,代码行数:68,代码来源:AddCluster.java
示例12: setOptions
import weka.clusterers.AbstractClusterer; //导入依赖的package包/类
/**
* Parses a given list of options.
* <p/>
*
* <!-- options-start --> Valid options are:
* <p/>
*
* <pre>
* -W <clusterer specification>
* Full class name of clusterer to use, followed
* by scheme options. eg:
* "weka.clusterers.SimpleKMeans -N 3"
* (default: weka.clusterers.SimpleKMeans)
* </pre>
*
* <pre>
* -serialized <file>
* Instead of building a clusterer on the data, one can also provide
* a serialized model and use that for adding the clusters.
* </pre>
*
* <pre>
* -I <att1,att2-att4,...>
* The range of attributes the clusterer should ignore.
* </pre>
*
* <!-- options-end -->
*
* @param options the list of options as an array of strings
* @throws Exception if an option is not supported
*/
@Override
public void setOptions(String[] options) throws Exception {
String tmpStr;
String[] tmpOptions;
File file;
boolean serializedModel;
serializedModel = false;
tmpStr = Utils.getOption("serialized", options);
if (tmpStr.length() != 0) {
file = new File(tmpStr);
if (!file.exists()) {
throw new FileNotFoundException("File '" + file.getAbsolutePath()
+ "' not found!");
}
if (file.isDirectory()) {
throw new FileNotFoundException("'" + file.getAbsolutePath()
+ "' points to a directory not a file!");
}
setSerializedClustererFile(file);
serializedModel = true;
} else {
setSerializedClustererFile(null);
}
if (!serializedModel) {
tmpStr = Utils.getOption('W', options);
if (tmpStr.length() == 0) {
tmpStr = weka.clusterers.SimpleKMeans.class.getName();
}
tmpOptions = Utils.splitOptions(tmpStr);
if (tmpOptions.length == 0) {
throw new Exception("Invalid clusterer specification string");
}
tmpStr = tmpOptions[0];
tmpOptions[0] = "";
setClusterer(AbstractClusterer.forName(tmpStr, tmpOptions));
}
setIgnoredAttributeIndices(Utils.getOption('I', options));
Utils.checkForRemainingOptions(options);
}
开发者ID:mydzigear,项目名称:repo.kmeanspp.silhouette_score,代码行数:75,代码来源:AddCluster.java
示例13: batchFinished
import weka.clusterers.AbstractClusterer; //导入依赖的package包/类
/**
* Signify that this batch of input to the filter is finished.
*
* @return true if there are instances pending output
* @throws IllegalStateException if no input structure has been defined
*/
public boolean batchFinished() throws Exception {
if (getInputFormat() == null)
throw new IllegalStateException("No input instance format defined");
Instances toFilter = getInputFormat();
if (!isFirstBatchDone()) {
// filter out attributes if necessary
Instances toFilterIgnoringAttributes = removeIgnored(toFilter);
// serialized model or build clusterer from scratch?
File file = getSerializedClustererFile();
if (!file.isDirectory()) {
ObjectInputStream ois = new ObjectInputStream(new FileInputStream(file));
m_ActualClusterer = (Clusterer) ois.readObject();
Instances header = null;
// let's see whether there's an Instances header stored as well
try {
header = (Instances) ois.readObject();
}
catch (Exception e) {
// ignored
}
ois.close();
// same dataset format?
if ((header != null) && (!header.equalHeaders(toFilterIgnoringAttributes)))
throw new WekaException(
"Training header of clusterer and filter dataset don't match:\n"
+ header.equalHeadersMsg(toFilterIgnoringAttributes));
}
else {
m_ActualClusterer = AbstractClusterer.makeCopy(m_Clusterer);
m_ActualClusterer.buildClusterer(toFilterIgnoringAttributes);
}
// create output dataset with new attribute
Instances filtered = new Instances(toFilter, 0);
FastVector nominal_values = new FastVector(m_ActualClusterer.numberOfClusters());
for (int i = 0; i < m_ActualClusterer.numberOfClusters(); i++) {
nominal_values.addElement("cluster" + (i+1));
}
filtered.insertAttributeAt(new Attribute("cluster", nominal_values),
filtered.numAttributes());
setOutputFormat(filtered);
}
// build new dataset
for (int i=0; i<toFilter.numInstances(); i++) {
convertInstance(toFilter.instance(i));
}
flushInput();
m_NewBatch = true;
m_FirstBatchDone = true;
return (numPendingOutput() != 0);
}
开发者ID:dsibournemouth,项目名称:autoweka,代码行数:65,代码来源:AddCluster.java
示例14: setOptions
import weka.clusterers.AbstractClusterer; //导入依赖的package包/类
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -W <clusterer specification>
* Full class name of clusterer to use, followed
* by scheme options. eg:
* "weka.clusterers.SimpleKMeans -N 3"
* (default: weka.clusterers.SimpleKMeans)</pre>
*
* <pre> -serialized <file>
* Instead of building a clusterer on the data, one can also provide
* a serialized model and use that for adding the clusters.</pre>
*
* <pre> -I <att1,att2-att4,...>
* The range of attributes the clusterer should ignore.
* </pre>
*
<!-- options-end -->
*
* @param options the list of options as an array of strings
* @throws Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
String tmpStr;
String[] tmpOptions;
File file;
boolean serializedModel;
serializedModel = false;
tmpStr = Utils.getOption("serialized", options);
if (tmpStr.length() != 0) {
file = new File(tmpStr);
if (!file.exists())
throw new FileNotFoundException(
"File '" + file.getAbsolutePath() + "' not found!");
if (file.isDirectory())
throw new FileNotFoundException(
"'" + file.getAbsolutePath() + "' points to a directory not a file!");
setSerializedClustererFile(file);
serializedModel = true;
}
else {
setSerializedClustererFile(null);
}
if (!serializedModel) {
tmpStr = Utils.getOption('W', options);
if (tmpStr.length() == 0)
tmpStr = weka.clusterers.SimpleKMeans.class.getName();
tmpOptions = Utils.splitOptions(tmpStr);
if (tmpOptions.length == 0) {
throw new Exception("Invalid clusterer specification string");
}
tmpStr = tmpOptions[0];
tmpOptions[0] = "";
setClusterer(AbstractClusterer.forName(tmpStr, tmpOptions));
}
setIgnoredAttributeIndices(Utils.getOption('I', options));
Utils.checkForRemainingOptions(options);
}
开发者ID:dsibournemouth,项目名称:autoweka,代码行数:66,代码来源:AddCluster.java
示例15: setOptions
import weka.clusterers.AbstractClusterer; //导入依赖的package包/类
/**
* Parses the options for this object. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -D
* If set, classifier is run in debug mode and
* may output additional info to the console</pre>
*
* <pre> -W
* Full name of clusterer.
* (default: weka.clusterers.SimpleKMeans)</pre>
*
* <pre>
* Options specific to clusterer weka.clusterers.SimpleKMeans:
* </pre>
*
* <pre> -N <num>
* number of clusters.
* (default 2).</pre>
*
* <pre> -V
* Display std. deviations for centroids.
* </pre>
*
* <pre> -M
* Replace missing values with mean/mode.
* </pre>
*
* <pre> -S <num>
* Random number seed.
* (default 10)</pre>
*
<!-- options-end -->
*
* @param options the options to use
* @throws Exception if setting of options fails
*/
public void setOptions(String[] options) throws Exception {
String tmpStr;
super.setOptions(options);
tmpStr = Utils.getOption('W', options);
if (tmpStr.length() > 0) {
// This is just to set the classifier in case the option
// parsing fails.
setClusterer(AbstractClusterer.forName(tmpStr, null));
setClusterer(AbstractClusterer.forName(tmpStr, Utils.partitionOptions(options)));
}
else {
// This is just to set the classifier in case the option
// parsing fails.
setClusterer(AbstractClusterer.forName(defaultClustererString(), null));
setClusterer(AbstractClusterer.forName(defaultClustererString(), Utils.partitionOptions(options)));
}
}
开发者ID:williamClanton,项目名称:jbossBA,代码行数:59,代码来源:ClassificationViaClustering.java
示例16: setOptions
import weka.clusterers.AbstractClusterer; //导入依赖的package包/类
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -W <clusterer specification>
* Full class name of clusterer to use, followed
* by scheme options. eg:
* "weka.clusterers.SimpleKMeans -N 3"
* (default: weka.clusterers.SimpleKMeans)</pre>
*
* <pre> -I <att1,att2-att4,...>
* The range of attributes the clusterer should ignore.
* </pre>
*
<!-- options-end -->
*
* @param options the list of options as an array of strings
* @throws Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
String clustererString = Utils.getOption('W', options);
if (clustererString.length() == 0)
clustererString = weka.clusterers.SimpleKMeans.class.getName();
String[] clustererSpec = Utils.splitOptions(clustererString);
if (clustererSpec.length == 0) {
throw new Exception("Invalid clusterer specification string");
}
String clustererName = clustererSpec[0];
clustererSpec[0] = "";
setClusterer(AbstractClusterer.forName(clustererName, clustererSpec));
setIgnoredAttributeIndices(Utils.getOption('I', options));
Utils.checkForRemainingOptions(options);
}
开发者ID:williamClanton,项目名称:jbossBA,代码行数:39,代码来源:AddCluster.java
示例17: setClusterer
import weka.clusterers.AbstractClusterer; //导入依赖的package包/类
@Override
public void setClusterer(AbstractClusterer clusteringAlgorithm) {
this.clusterer = clusteringAlgorithm;
}
开发者ID:kokojumbo,项目名称:master-thesis,代码行数:6,代码来源:DBScanImbalancedAlgorithm.java
示例18: setClusterer
import weka.clusterers.AbstractClusterer; //导入依赖的package包/类
public void setClusterer(AbstractClusterer clusterer) {
this.clusterer = clusterer;
}
开发者ID:kokojumbo,项目名称:master-thesis,代码行数:4,代码来源:ImbalancedClassifier.java
示例19: setClusterer
import weka.clusterers.AbstractClusterer; //导入依赖的package包/类
@Override
public void setClusterer(AbstractClusterer clusteringAlgorithm) {
this.clusterer = clusteringAlgorithm;
}
开发者ID:kokojumbo,项目名称:master-thesis,代码行数:8,代码来源:ClusterImbalancedAlgorithm.java
示例20: setClusterer
import weka.clusterers.AbstractClusterer; //导入依赖的package包/类
public void setClusterer(AbstractClusterer clusteringAlgorithm);
开发者ID:kokojumbo,项目名称:master-thesis,代码行数:2,代码来源:ImbalancedAlgorithm.java
注:本文中的weka.clusterers.AbstractClusterer类示例整理自Github/MSDocs等源码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。 |
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