数据挖掘中决策树C4.5预测算法实现(半成品，还要写规则后煎支及对非离散数据信息增益计算)，下一篇博客讲原理
package org.struct.decisiontree;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import java.util.TreeSet;
/**
* @author Leon.Chen
*/
public class DecisionTreeBaseC4p5 {
/**
* root node
*/
private DecisionTreeNode root;
/**
* visableArray
*/
private boolean[] visable;
private static final int NOT_FOUND = -1;
private static final int DATA_START_LINE = 1;
private Object[] trainingArray;
private String[] columnHeaderArray;
/**
* forecast node index
*/
private int nodeIndex;
/**
* @param args
*/
@SuppressWarnings("boxing")
public static void main(String[] args) {
Object[] array = new Object[] {
new String[] { "age", "income", "student", "credit_rating", "buys_computer" },
new String[] { "youth", "high", "no", "fair", "no" },
new String[] { "youth", "high", "no", "excellent", "no" },
new String[] { "middle_aged", "high", "no", "fair", "yes" },
new String[] { "senior", "medium", "no", "fair", "yes" },
new String[] { "senior", "low", "yes", "fair", "yes" },
new String[] { "senior", "low", "yes", "excellent", "no" },
new String[] { "middle_aged", "low", "yes", "excellent", "yes" },
new String[] { "youth", "medium", "no", "fair", "no" },
new String[] { "youth", "low", "yes", "fair", "yes" },
new String[] { "senior", "medium", "yes", "fair", "yes" },
new String[] { "youth", "medium", "yes", "excellent", "yes" },
new String[] { "middle_aged", "medium", "no", "excellent", "yes" },
new String[] { "middle_aged", "high", "yes", "fair", "yes" },
new String[] { "senior", "medium", "no", "excellent", "no" },
};
DecisionTreeBaseC4p5 tree = new DecisionTreeBaseC4p5();
tree.create(array, 4);
System.out.println("===============END PRINT TREE===============");
System.out.println("===============DECISION RESULT===============");
//tree.forecast(printData, tree.root);
}
/**
* @param printData
* @param node
*/
public void forecast(String[] printData, DecisionTreeNode node) {
int index = getColumnHeaderIndexByName(node.nodeName);
if (index == NOT_FOUND) {
System.out.println(node.nodeName);
}
DecisionTreeNode[] childs = node.childNodesArray;
for (int i = 0; i < childs.length; i++) {
if (childs[i] != null) {
if (childs[i].parentArrtibute.equals(printData[index])) {
forecast(printData, childs[i]);
}
}
}
}
/**
* @param array
* @param index
*/
public void create(Object[] array, int index) {
this.trainingArray = Arrays.copyOfRange(array, DATA_START_LINE,
array.length);
init(array, index);
createDecisionTree(this.trainingArray);
printDecisionTree(root);
}
/**
* @param array
* @return Object[]
*/
@SuppressWarnings("boxing")
public Object[] getMaxGain(Object[] array) {
Object[] result = new Object[2];
double gain = 0;
int index = -1;
for (int i = 0; i < visable.length; i++) {
if (!visable[i]) {
//TODO ID3 change to C4.5
double value = gainRatio(array, i, this.nodeIndex);
System.out.println(value);
if (gain < value) {
gain = value;
index = i;
}
}
}
result[0] = gain;
result[1] = index;
// TODO throws can't forecast this model exception
if (index != -1) {
visable[index] = true;
}
return result;
}
/**
* @param array
*/
public void createDecisionTree(Object[] array) {
Object[] maxgain = getMaxGain(array);
if (root == null) {
root = new DecisionTreeNode();
root.parentNode = null;
root.parentArrtibute = null;
root.arrtibutesArray = getArrtibutesArray(((Integer) maxgain[1])
.intValue());
root.nodeName = getColumnHeaderNameByIndex(((Integer) maxgain[1])
.intValue());
root.childNodesArray = new DecisionTreeNode[root.arrtibutesArray.length];
insertDecisionTree(array, root);
}
}
/**
* @param array
* @param parentNode
*/
public void insertDecisionTree(Object[] array, DecisionTreeNode parentNode) {
String[] arrtibutes = parentNode.arrtibutesArray;
for (int i = 0; i < arrtibutes.length; i++) {
Object[] pickArray = pickUpAndCreateSubArray(array, arrtibutes[i],
getColumnHeaderIndexByName(parentNode.nodeName));
Object[] info = getMaxGain(pickArray);
double gain = ((Double) info[0]).doubleValue();
if (gain != 0) {
int index = ((Integer) info[1]).intValue();
DecisionTreeNode currentNode = new DecisionTreeNode();
currentNode.parentNode = parentNode;
currentNode.parentArrtibute = arrtibutes[i];
currentNode.arrtibutesArray = getArrtibutesArray(index);
currentNode.nodeName = getColumnHeaderNameByIndex(index);
currentNode.childNodesArray = new DecisionTreeNode[currentNode.arrtibutesArray.length];
parentNode.childNodesArray[i] = currentNode;
insertDecisionTree(pickArray, currentNode);
} else {
DecisionTreeNode leafNode = new DecisionTreeNode();
leafNode.parentNode = parentNode;
leafNode.parentArrtibute = arrtibutes[i];
leafNode.arrtibutesArray = new String[0];
leafNode.nodeName = getLeafNodeName(pickArray,this.nodeIndex);
leafNode.childNodesArray = new DecisionTreeNode[0];
parentNode.childNodesArray[i] = leafNode;
}
}
}
/**
* @param node
*/
public void printDecisionTree(DecisionTreeNode node) {
System.out.println(node.nodeName);
DecisionTreeNode[] childs = node.childNodesArray;
for (int i = 0; i < childs.length; i++) {
if (childs[i] != null) {
System.out.println(childs[i].parentArrtibute);
printDecisionTree(childs[i]);
}
}
}
/**
* init data
*
* @param dataArray
* @param index
*/
public void init(Object[] dataArray, int index) {
this.nodeIndex = index;
// init data
this.columnHeaderArray = (String[]) dataArray[0];
visable = new boolean[((String[]) dataArray[0]).length];
for (int i = 0; i < visable.length; i++) {
if (i == index) {
visable[i] = true;
} else {
visable[i] = false;
}
}
}
/**
* @param array
* @param arrtibute
* @param index
* @return Object[]
*/
public Object[] pickUpAndCreateSubArray(Object[] array, String arrtibute,
int index) {
List<String[]> list = new ArrayList<String[]>();
for (int i = 0; i < array.length; i++) {
String[] strs = (String[]) array[i];
if (strs[index].equals(arrtibute)) {
list.add(strs);
}
}
return list.toArray();
}
/**
* gain(A)
*
* @param array
* @param index
* @return double
*/
public double gain(Object[] array, int index, int nodeIndex) {
int[] counts = separateToSameValueArrays(array, nodeIndex);
String[] arrtibutes = getArrtibutesArray(index);
double infoD = infoD(array, counts);
double infoaD = infoaD(array, index, nodeIndex, arrtibutes);
return infoD - infoaD;
}
/**
* @param array
* @param nodeIndex
* @return
*/
public int[] separateToSameValueArrays(Object[] array, int nodeIndex) {
String[] arrti = getArrtibutesArray(nodeIndex);
int[] counts = new int[arrti.length];
for (int i = 0; i < counts.length; i++) {
counts[i] = 0;
}
for (int i = 0; i < array.length; i++) {
String[] strs = (String[]) array[i];
for (int j = 0; j < arrti.length; j++) {
if (strs[nodeIndex].equals(arrti[j])) {
counts[j]++;
}
}
}
return counts;
}
/**
* gainRatio = gain(A)/splitInfo(A)
*
* @param array
* @param index
* @param nodeIndex
* @return
*/
public double gainRatio(Object[] array,int index,int nodeIndex){
double gain = gain(array,index,nodeIndex);
int[] counts = separateToSameValueArrays(array, index);
double splitInfo = splitInfoaD(array,counts);
if(splitInfo != 0){
return gain/splitInfo;
}
return 0;
}
/**
* infoD = -E(pi*log2 pi)
*
* @param array
* @param counts
* @return
*/
public double infoD(Object[] array, int[] counts) {
double infoD = 0;
for (int i = 0; i < counts.length; i++) {
infoD += DecisionTreeUtil.info(counts[i], array.length);
}
return infoD;
}
/**
* splitInfoaD = -E|Dj|/|D|*log2(|Dj|/|D|)
*
* @param array
* @param counts
* @return
*/
public double splitInfoaD(Object[] array, int[] counts) {
return infoD(array, counts);
}
/**
* infoaD = E(|Dj| / |D|) * info(Dj)
*
* @param array
* @param index
* @param arrtibutes
* @return
*/
public double infoaD(Object[] array, int index, int nodeIndex,
String[] arrtibutes) {
double sv_total = 0;
for (int i = 0; i < arrtibutes.length; i++) {
sv_total += infoDj(array, index, nodeIndex, arrtibutes[i],
array.length);
}
return sv_total;
}
/**
* ((|Dj| / |D|) * Info(Dj))
*
* @param array
* @param index
* @param arrtibute
* @param allTotal
* @return double
*/
public double infoDj(Object[] array, int index, int nodeIndex,
String arrtibute, int allTotal) {
String[] arrtibutes = getArrtibutesArray(nodeIndex);
int[] counts = new int[arrtibutes.length];
for (int i = 0; i < counts.length; i++) {
counts[i] = 0;
}
for (int i = 0; i < array.length; i++) {
String[] strs = (String[]) array[i];
if (strs[index].equals(arrtibute)) {
for (int k = 0; k < arrtibutes.length; k++) {
if (strs[nodeIndex].equals(arrtibutes[k])) {
counts[k]++;
}
}
}
}
int total = 0;
double infoDj = 0;
for (int i = 0; i < counts.length; i++) {
total += counts[i];
}
for (int i = 0; i < counts.length; i++) {
infoDj += DecisionTreeUtil.info(counts[i], total);
}
return DecisionTreeUtil.getPi(total, allTotal) * infoDj;
}
/**
* @param index
* @return String[]
*/
@SuppressWarnings("unchecked")
public String[] getArrtibutesArray(int index) {
TreeSet<String> set = new TreeSet<String>(new SequenceComparator());
for (int i = 0; i < trainingArray.length; i++) {
String[] strs = (String[]) trainingArray[i];
set.add(strs[index]);
}
String[] result = new String[set.size()];
return set.toArray(result);
}
/**
* @param index
* @return String
*/
public String getColumnHeaderNameByIndex(int index) {
for (int i = 0; i < columnHeaderArray.length; i++) {
if (i == index) {
return columnHeaderArray[i];
}
}
return null;
}
/**
* @param array
* @return String
*/
public String getLeafNodeName(Object[] array,int nodeIndex) {
if (array != null && array.length > 0) {
String[] strs = (String[]) array[0];
return strs[nodeIndex];
}
return null;
}
/**
* @param name
* @return int
*/
public int getColumnHeaderIndexByName(String name) {
for (int i = 0; i < columnHeaderArray.length; i++) {
if (name.equals(columnHeaderArray[i])) {
return i;
}
}
return NOT_FOUND;
}
}
package org.struct.decisiontree;
/**
* @author Leon.Chen
*/
public class DecisionTreeNode {
DecisionTreeNode parentNode;
String parentArrtibute;
String nodeName;
String[] arrtibutesArray;
DecisionTreeNode[] childNodesArray;
}
package org.struct.decisiontree;
/**
* @author Leon.Chen
*/
public class DecisionTreeUtil {
/**
* entropy:Info(T)=(i=1...k)pi*log（2）pi
*
* @param x
* @param total
* @return double
*/
public static double info(int x, int total) {
if (x == 0) {
return 0;
}
double x_pi = getPi(x, total);
return -(x_pi * logYBase2(x_pi));
}
/**
* log2y
*
* @param y
* @return double
*/
public static double logYBase2(double y) {
return Math.log(y) / Math.log(2);
}
/**
* pi=|C(i,d)|/|D|
*
* @param x
* @param total
* @return double
*/
public static double getPi(int x, int total) {
return x / (double) total;
}
}
package org.struct.decisiontree;
import java.util.Comparator;
/**
* @author Leon.Chen
*
*/
@SuppressWarnings("unchecked")
public class SequenceComparator implements Comparator {
public int compare(Object o1, Object o2) throws ClassCastException {
String str1 = (String) o1;
String str2 = (String) o2;
return str1.compareTo(str2);
}
}
