MOA 12.03
Real Time Analytics for Data Streams
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00001 /* 00002 * CFCluster.java 00003 * Copyright (C) 2010 RWTH Aachen University, Germany 00004 * @author Jansen ([email protected]) 00005 * 00006 * This program is free software; you can redistribute it and/or modify 00007 * it under the terms of the GNU General Public License as published by 00008 * the Free Software Foundation; either version 3 of the License, or 00009 * (at your option) any later version. 00010 * 00011 * This program is distributed in the hope that it will be useful, 00012 * but WITHOUT ANY WARRANTY; without even the implied warranty of 00013 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 00014 * GNU General Public License for more details. 00015 * 00016 * You should have received a copy of the GNU General Public License 00017 * along with this program. If not, see <http://www.gnu.org/licenses/>. 00018 * 00019 */ 00020 00021 package moa.cluster; 00022 import java.util.Arrays; 00023 import weka.core.Instance; 00024 00025 public abstract class CFCluster extends SphereCluster { 00026 00027 private static final long serialVersionUID = 1L; 00028 00029 protected double radiusFactor = 1.8; 00030 00034 protected double N; 00038 public double[] LS; 00042 public double[] SS; 00043 00049 public CFCluster(Instance instance, int dimensions) { 00050 this(instance.toDoubleArray(), dimensions); 00051 } 00052 00053 protected CFCluster(int dimensions) { 00054 this.N = 0; 00055 this.LS = new double[dimensions]; 00056 this.SS = new double[dimensions]; 00057 Arrays.fill(this.LS, 0.0); 00058 Arrays.fill(this.SS, 0.0); 00059 } 00060 00061 public CFCluster(double [] center, int dimensions) { 00062 this.N = 1; 00063 this.LS = center; 00064 this.SS = new double[dimensions]; 00065 for (int i = 0; i < SS.length; i++) { 00066 SS[i]=Math.pow(center[i], 2); 00067 } 00068 } 00069 00070 public CFCluster(CFCluster cluster) { 00071 this.N = cluster.N; 00072 this.LS = Arrays.copyOf(cluster.LS, cluster.LS.length); 00073 this.SS = Arrays.copyOf(cluster.SS, cluster.SS.length); 00074 } 00075 00076 public void add(CFCluster cluster ) { 00077 this.N += cluster.N; 00078 addVectors( this.LS, cluster.LS ); 00079 addVectors( this.SS, cluster.SS ); 00080 } 00081 00082 public abstract CFCluster getCF(); 00083 00087 @Override 00088 public double[] getCenter() { 00089 assert (this.N>0); 00090 double res[] = new double[this.LS.length]; 00091 for ( int i = 0; i < res.length; i++ ) { 00092 res[i] = this.LS[i] / N; 00093 } 00094 return res; 00095 } 00096 00097 00098 @Override 00099 public abstract double getInclusionProbability(Instance instance); 00100 00105 @Override 00106 public abstract double getRadius(); 00107 00113 @Override 00114 public double getWeight() { 00115 return N; 00116 } 00117 00118 public void setN(double N){ 00119 this.N = N; 00120 } 00121 00122 public double getN() { 00123 return N; 00124 } 00125 00132 public static void addVectors(double[] a1, double[] a2) { 00133 assert (a1 != null); 00134 assert (a2 != null); 00135 assert (a1.length == a2.length) : "Adding two arrays of different " 00136 + "length"; 00137 00138 for (int i = 0; i < a1.length; i++) { 00139 a1[i] += a2[i]; 00140 } 00141 } 00142 }