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Massive On-line Analysis is an environment for massive data mining. MOA
provides a framework for data stream mining and includes tools for evaluation
and a collection of machine learning algorithms. Related to the WEKA project,
also written in Java, while scaling to more demanding problems.
/*
* ISBIndex.java
* Copyright (C) 2013 Aristotle University of Thessaloniki, Greece
* @author D. Georgiadis, A. Gounaris, A. Papadopoulos, K. Tsichlas, Y. Manolopoulos
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
*
*/
package moa.clusterers.outliers.MCOD;
import java.util.ArrayList;
import java.util.Collections;
import java.util.HashMap;
import java.util.HashSet;
import java.util.Iterator;
import java.util.List;
import java.util.Map;
import java.util.Set;
import java.util.TreeSet;
import java.util.Vector;
import com.yahoo.labs.samoa.instances.Instance;
public class ISBIndex {
public static class ISBNode implements Comparable {
public static enum NodeType { OUTLIER, INLIER_MC, INLIER_PD }
public Instance inst;
public StreamObj obj;
public Long id;
public MicroCluster mc;
public Set Rmc;
public int count_after;
public NodeType nodeType;
private ArrayList nn_before;
// statistics
public int nOutlier;
public int nInlier;
public ISBNode(Instance inst, StreamObj obj, Long id) {
this.inst = inst;
this.obj = obj;
this.id = id;
// init statistics
nOutlier = 0;
nInlier = 0;
// init other fields
InitNode();
}
public void InitNode() {
this.mc = null;
this.Rmc = new TreeSet();
this.count_after = 1;
this.nodeType = NodeType.INLIER_PD;
this.nn_before = new ArrayList();
}
@Override
public int compareTo(ISBNode t) {
if (this.id > t.id)
return +1;
else if (this.id < t.id)
return -1;
return 0;
}
public void AddPrecNeigh(ISBNode node) {
int pos = Collections.binarySearch(nn_before, node);
if (pos < 0) {
// item does not exist, so add it to the right position
nn_before.add(-(pos + 1), node);
}
}
public void RemovePrecNeigh(ISBNode node) {
int pos = Collections.binarySearch(nn_before, node);
if (pos >= 0) {
// item exists
nn_before.remove(pos);
}
}
public ISBNode GetMinPrecNeigh(Long sinceId) {
if (nn_before.size() > 0) {
int startPos;
ISBNode dummy = new ISBNode(null, null, sinceId);
int pos = Collections.binarySearch(nn_before, dummy);
if (pos < 0) {
// item does not exist, should insert at position startPos
startPos = -(pos + 1);
} else {
// item exists at startPos
startPos = pos;
}
if (startPos < nn_before.size()) {
return nn_before.get(startPos);
}
}
return null;
}
public int CountPrecNeighs(Long sinceId) {
if (nn_before.size() > 0) {
// get number of neighs with id >= sinceId
int startPos;
ISBNode dummy = new ISBNode(null, null, sinceId);
int pos = Collections.binarySearch(nn_before, dummy);
if (pos < 0) {
// item does not exist, should insert at position startPos
startPos = -(pos + 1);
} else {
// item exists at startPos
startPos = pos;
}
if (startPos < nn_before.size()) {
return nn_before.size() - startPos;
}
}
return 0;
}
public List Get_nn_before() {
return nn_before;
}
}
MTreeStreamObjects mtree;
Map> mapNodes;
double m_radius;
int m_k; // k nearest neighbors
public ISBIndex(double radius, int k) {
mtree = new MTreeStreamObjects();
mapNodes = new HashMap>();
m_radius = radius;
m_k = k;
}
Vector GetAllNodes() {
Vector v = new Vector();
Iterator it = mapNodes.entrySet().iterator();
while (it.hasNext()) {
Map.Entry pairs = (Map.Entry) it.next();
Set setNodes = (Set) pairs.getValue();
for (ISBNode n : setNodes) {
v.add(n);
}
}
return v;
}
public static class ISBSearchResult {
public ISBNode node;
public double distance;
public ISBSearchResult(ISBNode n, double distance) {
this.node = n;
this.distance = distance;
}
}
public Vector RangeSearch(ISBNode node, double radius) {
Vector results = new Vector();
StreamObj obj;
double d;
MTreeStreamObjects.Query query = mtree.getNearestByRange(node.obj, radius);
for (MTreeStreamObjects.ResultItem q : query) {
// get next obj found within range
obj = q.data;
// get distance of obj from query
d = q.distance;
// get all nodes referencing obj
Vector nodes = MapGetNodes(obj);
for (int i = 0; i < nodes.size(); i++)
results.add(new ISBSearchResult(nodes.get(i), d));
}
return results;
}
public void Insert(ISBNode node) {
// insert object of node at mtree
mtree.add(node.obj);
// insert node at map
MapInsert(node);
}
public void Remove(ISBNode node) {
// remove from map
MapDelete(node);
// check if stream object at mtree is still being referenced
if (MapCountObjRefs(node.obj) <= 0) {
// delete stream object from mtree
mtree.remove(node.obj);
}
}
Vector MapGetNodes(StreamObj obj) {
int h = obj.hashCode();
Vector v = new Vector();
if (mapNodes.containsKey(h)) {
Set s = mapNodes.get(h);
ISBNode node;
Iterator i = s.iterator();
while (i.hasNext()) {
node = i.next();
if (node.obj.equals(obj))
v.add(node);
}
}
return v;
}
int MapCountObjRefs(StreamObj obj) {
int h = obj.hashCode();
int iCount = 0;
if (mapNodes.containsKey(h)) {
Set s = mapNodes.get(h);
ISBNode n;
Iterator i = s.iterator();
while (i.hasNext()) {
n = i.next();
if (n.obj.equals(obj))
iCount++;
}
}
return iCount;
}
void MapInsert(ISBNode node) {
int h = node.obj.hashCode();
Set s;
if (mapNodes.containsKey(h)) {
s = mapNodes.get(h);
s.add(node);
}
else {
s = new HashSet();
s.add(node);
mapNodes.put(h, s);
}
}
void MapDelete(ISBNode node) {
int h = node.obj.hashCode();
if (mapNodes.containsKey(h)) {
Set s = mapNodes.get(h);
s.remove(node);
if (s.isEmpty()) { // ### added
mapNodes.remove(h);
}
}
}
}
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