java.lang.NullPointerException在尝试MOA流聚类算法时denstream.WithDBSCAN(如何正确使用?)
java.lang.NullPointerException when trying MOA stream clustering algorithm denstream.WithDBSCAN (How to properly use it?)
我是 moa 的新手,我很难理解如何使用聚类算法。 documentation lacks of sample code for common usages, and the implementation没有很好的解释和评论...也没有找到任何教程。
所以,这是我的代码:
import com.yahoo.labs.samoa.instances.DenseInstance;
import moa.cluster.Clustering;
import moa.clusterers.denstream.WithDBSCAN;
public class TestingDenstream {
static DenseInstance randomInstance(int size) {
DenseInstance instance = new DenseInstance(size);
for (int idx = 0; idx < size; idx++) {
instance.setValue(idx, Math.random());
}
return instance;
}
public static void main(String[] args) {
WithDBSCAN withDBSCAN = new WithDBSCAN();
withDBSCAN.resetLearningImpl();
for (int i = 0; i < 10; i++) {
DenseInstance d = randomInstance(2);
withDBSCAN.trainOnInstanceImpl(d);
}
Clustering clusteringResult = withDBSCAN.getClusteringResult();
Clustering microClusteringResult = withDBSCAN.getMicroClusteringResult();
System.out.println(clusteringResult);
}
}
这是我得到的错误:
任何关于如何使用该算法的见解都将不胜感激。谢谢!
我更新了代码。
正如我在 github 中提到的那样工作,您必须将 header 分配给您的实例。 See the github discussion
这是更新后的代码:
static DenseInstance randomInstance(int size) {
// generates the name of the features which is called as InstanceHeader
ArrayList<Attribute> attributes = new ArrayList<Attribute>();
for (int i = 0; i < size; i++) {
attributes.add(new Attribute("feature_" + i));
}
// create instance header with generated feature name
InstancesHeader streamHeader = new InstancesHeader(
new Instances("Mustafa Çelik Instance",attributes, size));
// generates random data
double[] data = new double[2];
Random random = new Random();
for (int i = 0; i < 2; i++) {
data[i] = random.nextDouble();
}
// creates an instance and assigns the data
DenseInstance inst = new DenseInstance(1.0, data);
// assigns the instanceHeader(feature name)
inst.setDataset(streamHeader);
return inst;
}
public static void main(String[] args) {
WithDBSCAN withDBSCAN = new WithDBSCAN();
withDBSCAN.resetLearningImpl();
withDBSCAN.initialDBScan();
for (int i = 0; i < 1500; i++) {
DenseInstance d = randomInstance(5);
withDBSCAN.trainOnInstanceImpl(d);
}
Clustering clusteringResult = withDBSCAN.getClusteringResult();
Clustering microClusteringResult = withDBSCAN.getMicroClusteringResult();
System.out.println(clusteringResult);
}
这里是调试过程的屏幕截图,如您所见生成了聚类结果:
图片 link 已损坏,您可以在 github github entry link
上找到它
我是 moa 的新手,我很难理解如何使用聚类算法。 documentation lacks of sample code for common usages, and the implementation没有很好的解释和评论...也没有找到任何教程。
所以,这是我的代码:
import com.yahoo.labs.samoa.instances.DenseInstance;
import moa.cluster.Clustering;
import moa.clusterers.denstream.WithDBSCAN;
public class TestingDenstream {
static DenseInstance randomInstance(int size) {
DenseInstance instance = new DenseInstance(size);
for (int idx = 0; idx < size; idx++) {
instance.setValue(idx, Math.random());
}
return instance;
}
public static void main(String[] args) {
WithDBSCAN withDBSCAN = new WithDBSCAN();
withDBSCAN.resetLearningImpl();
for (int i = 0; i < 10; i++) {
DenseInstance d = randomInstance(2);
withDBSCAN.trainOnInstanceImpl(d);
}
Clustering clusteringResult = withDBSCAN.getClusteringResult();
Clustering microClusteringResult = withDBSCAN.getMicroClusteringResult();
System.out.println(clusteringResult);
}
}
这是我得到的错误:
任何关于如何使用该算法的见解都将不胜感激。谢谢!
我更新了代码。 正如我在 github 中提到的那样工作,您必须将 header 分配给您的实例。 See the github discussion
这是更新后的代码:
static DenseInstance randomInstance(int size) {
// generates the name of the features which is called as InstanceHeader
ArrayList<Attribute> attributes = new ArrayList<Attribute>();
for (int i = 0; i < size; i++) {
attributes.add(new Attribute("feature_" + i));
}
// create instance header with generated feature name
InstancesHeader streamHeader = new InstancesHeader(
new Instances("Mustafa Çelik Instance",attributes, size));
// generates random data
double[] data = new double[2];
Random random = new Random();
for (int i = 0; i < 2; i++) {
data[i] = random.nextDouble();
}
// creates an instance and assigns the data
DenseInstance inst = new DenseInstance(1.0, data);
// assigns the instanceHeader(feature name)
inst.setDataset(streamHeader);
return inst;
}
public static void main(String[] args) {
WithDBSCAN withDBSCAN = new WithDBSCAN();
withDBSCAN.resetLearningImpl();
withDBSCAN.initialDBScan();
for (int i = 0; i < 1500; i++) {
DenseInstance d = randomInstance(5);
withDBSCAN.trainOnInstanceImpl(d);
}
Clustering clusteringResult = withDBSCAN.getClusteringResult();
Clustering microClusteringResult = withDBSCAN.getMicroClusteringResult();
System.out.println(clusteringResult);
}
这里是调试过程的屏幕截图,如您所见生成了聚类结果: