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求助 weka 的ID3演算法java源碼
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*/
/*
* Id3.java
* Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.trees;
import weka.classifiers.Classifier;
import weka.classifiers.Sourcable;
import weka.core.Attribute;
import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.NoSupportForMissingValuesException;
import weka.core.RevisionUtils;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.core.Capabilities.Capability;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import java.util.Enumeration;
/**
!– globalinfo-start —
* Class for constructing an unpruned decision tree based on the ID3 algorithm. Can only deal with nominal attributes. No missing values allowed. Empty leaves may result in unclassified instances. For more information see: br/
* br/
* R. Quinlan (1986). Induction of decision trees. Machine Learning. 1(1):81-106.
* p/
!– globalinfo-end —
*
!– technical-bibtex-start —
* BibTeX:
* pre
* #64;article{Quinlan1986,
* author = {R. Quinlan},
* journal = {Machine Learning},
* number = {1},
* pages = {81-106},
* title = {Induction of decision trees},
* volume = {1},
* year = {1986}
* }
* /pre
* p/
!– technical-bibtex-end —
*
!– 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
*
!– options-end —
*
* @author Eibe Frank (eibe@cs.waikato.ac.nz)
* @version $Revision: 6404 $
*/
public class Id3
extends Classifier
implements TechnicalInformationHandler, Sourcable {
/** for serialization */
static final long serialVersionUID = -2693678647096322561L;
/** The node’s successors. */
private Id3[] m_Successors;
/** Attribute used for splitting. */
private Attribute m_Attribute;
/** Class value if node is leaf. */
private double m_ClassValue;
/** Class distribution if node is leaf. */
private double[] m_Distribution;
/** Class attribute of dataset. */
private Attribute m_ClassAttribute;
/**
* Returns a string describing the classifier.
* @return a description suitable for the GUI.
*/
public String globalInfo() {
return “Class for constructing an unpruned decision tree based on the ID3 “
+ “algorithm. Can only deal with nominal attributes. No missing values “
+ “allowed. Empty leaves may result in unclassified instances. For more “
+ “information see: \n\n”
+ getTechnicalInformation().toString();
}
/**
* Returns an instance of a TechnicalInformation object, containing
* detailed information about the technical background of this class,
* e.g., paper reference or book this class is based on.
*
* @return the technical information about this class
*/
public TechnicalInformation getTechnicalInformation() {
TechnicalInformation result;
result = new TechnicalInformation(Type.ARTICLE);
result.setValue(Field.AUTHOR, “R. Quinlan”);
result.setValue(Field.YEAR, “1986”);
result.setValue(Field.TITLE, “Induction of decision trees”);
result.setValue(Field.JOURNAL, “Machine Learning”);
result.setValue(Field.VOLUME, “1”);
result.setValue(Field.NUMBER, “1”);
result.setValue(Field.PAGES, “81-106”);
return result;
}
/**
* Returns default capabilities of the classifier.
*
* @return the capabilities of this classifier
*/
public Capabilities getCapabilities() {
Capabilities result = super.getCapabilities();
result.disableAll();
// attributes
result.enable(Capability.NOMINAL_ATTRIBUTES);
// class
result.enable(Capability.NOMINAL_CLASS);
result.enable(Capability.MISSING_CLASS_VALUES);
// instances
result.setMinimumNumberInstances(0);
return result;
}
/**
* Builds Id3 decision tree classifier.
*
* @param data the training data
* @exception Exception if classifier can’t be built successfully
*/
public void buildClassifier(Instances data) throws Exception {
// can classifier handle the data?
getCapabilities().testWithFail(data);
// remove instances with missing class
data = new Instances(data);
data.deleteWithMissingClass();
makeTree(data);
}
/**
* Method for building an Id3 tree.
*
* @param data the training data
* @exception Exception if decision tree can’t be built successfully
*/
private void makeTree(Instances data) throws Exception {
// Check if no instances have reached this node.
if (data.numInstances() == 0) {
m_Attribute = null;
m_ClassValue = Instance.missingValue();
m_Distribution = new double[data.numClasses()];
return;
}
// Compute attribute with maximum information gain.
double[] infoGains = new double[data.numAttributes()];
Enumeration attEnum = data.enumerateAttributes();
while (attEnum.hasMoreElements()) {
Attribute att = (Attribute) attEnum.nextElement();
infoGains[att.index()] = computeInfoGain(data, att);
}
m_Attribute = data.attribute(Utils.maxIndex(infoGains));
// Make leaf if information gain is zero.
// Otherwise create successors.
if (Utils.eq(infoGains[m_Attribute.index()], 0)) {
m_Attribute = null;
m_Distribution = new double[data.numClasses()];
Enumeration instEnum = data.enumerateInstances();
while (instEnum.hasMoreElements()) {
Instance inst = (Instance) instEnum.nextElement();
m_Distribution[(int) inst.classValue()]++;
}
Utils.normalize(m_Distribution);
m_ClassValue = Utils.maxIndex(m_Distribution);
m_ClassAttribute = data.classAttribute();
} else {
Instances[] splitData = splitData(data, m_Attribute);
m_Successors = new Id3[m_Attribute.numValues()];
for (int j = 0; j m_Attribute.numValues(); j++) {
m_Successors[j] = new Id3();
m_Successors[j].makeTree(splitData[j]);
}
}
}
/**
* Classifies a given test instance using the decision tree.
*
* @param instance the instance to be classified
* @return the classification
* @throws NoSupportForMissingValuesException if instance has missing values
*/
public double classifyInstance(Instance instance)
throws NoSupportForMissingValuesException {
if (instance.hasMissingValue()) {
throw new NoSupportForMissingValuesException(“Id3: no missing values, “
+ “please.”);
}
if (m_Attribute == null) {
return m_ClassValue;
} else {
return m_Successors[(int) instance.value(m_Attribute)].
classifyInstance(instance);
}
}
/**
* Computes class distribution for instance using decision tree.
*
* @param instance the instance for which distribution is to be computed
* @return the class distribution for the given instance
* @throws NoSupportForMissingValuesException if instance has missing values
*/
public double[] distributionForInstance(Instance instance)
throws NoSupportForMissingValuesException {
if (instance.hasMissingValue()) {
throw new NoSupportForMissingValuesException(“Id3: no missing values, “
+ “please.”);
}
if (m_Attribute == null) {
return m_Distribution;
} else {
return m_Successors[(int) instance.value(m_Attribute)].
distributionForInstance(instance);
}
}
/**
* Prints the decision tree using the private toString method from below.
*
* @return a textual description of the classifier
*/
public String toString() {
if ((m_Distribution == null) (m_Successors == null)) {
return “Id3: No model built yet.”;
}
return “Id3\n\n” + toString(0);
}
/**
* Computes information gain for an attribute.
*
* @param data the data for which info gain is to be computed
* @param att the attribute
* @return the information gain for the given attribute and data
* @throws Exception if computation fails
*/
private double computeInfoGain(Instances data, Attribute att)
throws Exception {
double infoGain = computeEntropy(data);
Instances[] splitData = splitData(data, att);
for (int j = 0; j att.numValues(); j++) {
if (splitData[j].numInstances() 0) {
infoGain -= ((double) splitData[j].numInstances() /
(double) data.numInstances()) *
computeEntropy(splitData[j]);
}
}
return infoGain;
}
/**
* Computes the entropy of a dataset.
*
* @param data the data for which entropy is to be computed
* @return the entropy of the data’s class distribution
* @throws Exception if computation fails
*/
private double computeEntropy(Instances data) throws Exception {
double [] classCounts = new double[data.numClasses()];
Enumeration instEnum = data.enumerateInstances();
while (instEnum.hasMoreElements()) {
Instance inst = (Instance) instEnum.nextElement();
classCounts[(int) inst.classValue()]++;
}
double entropy = 0;
for (int j = 0; j data.numClasses(); j++) {
if (classCounts[j] 0) {
entropy -= classCounts[j] * Utils.log2(classCounts[j]);
}
}
entropy /= (double) data.numInstances();
return entropy + Utils.log2(data.numInstances());
}
/**
* Splits a dataset according to the values of a nominal attribute.
*
* @param data the data which is to be split
* @param att the attribute to be used for splitting
* @return the sets of instances produced by the split
*/
private Instances[] splitData(Instances data, Attribute att) {
Instances[] splitData = new Instances[att.numValues()];
for (int j = 0; j att.numValues(); j++) {
splitData[j] = new Instances(data, data.numInstances());
}
Enumeration instEnum = data.enumerateInstances();
while (instEnum.hasMoreElements()) {
Instance inst = (Instance) instEnum.nextElement();
splitData[(int) inst.value(att)].add(inst);
}
for (int i = 0; i splitData.length; i++) {
splitData[i].compactify();
}
return splitData;
}
/**
* Outputs a tree at a certain level.
*
* @param level the level at which the tree is to be printed
* @return the tree as string at the given level
*/
private String toString(int level) {
StringBuffer text = new StringBuffer();
if (m_Attribute == null) {
if (Instance.isMissingValue(m_ClassValue)) {
text.append(“: null”);
} else {
text.append(“: ” + m_ClassAttribute.value((int) m_ClassValue));
}
} else {
for (int j = 0; j m_Attribute.numValues(); j++) {
text.append(“\n”);
for (int i = 0; i level; i++) {
text.append(“| “);
}
text.append(m_Attribute.name() + ” = ” + m_Attribute.value(j));
text.append(m_Successors[j].toString(level + 1));
}
}
return text.toString();
}
/**
* Adds this tree recursively to the buffer.
*
* @param id the unqiue id for the method
* @param buffer the buffer to add the source code to
* @return the last ID being used
* @throws Exception if something goes wrong
*/
protected int toSource(int id, StringBuffer buffer) throws Exception {
int result;
int i;
int newID;
StringBuffer[] subBuffers;
buffer.append(“\n”);
buffer.append(” protected static double node” + id + “(Object[] i) {\n”);
// leaf?
if (m_Attribute == null) {
result = id;
if (Double.isNaN(m_ClassValue)) {
buffer.append(” return Double.NaN;”);
} else {
buffer.append(” return ” + m_ClassValue + “;”);
}
if (m_ClassAttribute != null) {
buffer.append(” // ” + m_ClassAttribute.value((int) m_ClassValue));
}
buffer.append(“\n”);
buffer.append(” }\n”);
} else {
buffer.append(” checkMissing(i, ” + m_Attribute.index() + “);\n\n”);
buffer.append(” // ” + m_Attribute.name() + “\n”);
// subtree calls
subBuffers = new StringBuffer[m_Attribute.numValues()];
newID = id;
for (i = 0; i m_Attribute.numValues(); i++) {
newID++;
buffer.append(” “);
if (i 0) {
buffer.append(“else “);
}
buffer.append(“if (((String) i[” + m_Attribute.index()
+ “]).equals(\”” + m_Attribute.value(i) + “\”))\n”);
buffer.append(” return node” + newID + “(i);\n”);
subBuffers[i] = new StringBuffer();
newID = m_Successors[i].toSource(newID, subBuffers[i]);
}
buffer.append(” else\n”);
buffer.append(” throw new IllegalArgumentException(\”Value ‘\” + i[“
+ m_Attribute.index() + “] + \”‘ is not allowed!\”);\n”);
buffer.append(” }\n”);
// output subtree code
for (i = 0; i m_Attribute.numValues(); i++) {
buffer.append(subBuffers[i].toString());
}
subBuffers = null;
result = newID;
}
return result;
}
/**
* Returns a string that describes the classifier as source. The
* classifier will be contained in a class with the given name (there may
* be auxiliary classes),
* and will contain a method with the signature:
* precode
* public static double classify(Object[] i);
* /code/pre
* where the array codei/code contains elements that are either
* Double, String, with missing values represented as null. The generated
* code is public domain and comes with no warranty. br/
* Note: works only if class attribute is the last attribute in the dataset.
*
* @param className the name that should be given to the source class.
* @return the object source described by a string
* @throws Exception if the source can’t be computed
*/
public String toSource(String className) throws Exception {
StringBuffer result;
int id;
result = new StringBuffer();
result.append(“class ” + className + ” {\n”);
result.append(” private static void checkMissing(Object[] i, int index) {\n”);
result.append(” if (i[index] == null)\n”);
result.append(” throw new IllegalArgumentException(\”Null values “
+ “are not allowed!\”);\n”);
result.append(” }\n\n”);
result.append(” public static double classify(Object[] i) {\n”);
id = 0;
result.append(” return node” + id + “(i);\n”);
result.append(” }\n”);
toSource(id, result);
result.append(“}\n”);
return result.toString();
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract(“$Revision: 6404 $”);
}
/**
* Main method.
*
* @param args the options for the classifier
*/
public static void main(String[] args) {
runClassifier(new Id3(), args);
}
}
是用python學數據挖掘好，還是用java學weka的開發好
你熟悉python 就用 python，
你熟悉java 就學weka, weka 一個軟體連續開發20多年，及其成熟而且做到極致，很多都已經能自動化，其擴展版本甚至演算法都能自己選擇，極致到完全不懂的人都可以用。
weka有損自以為高深的人的威嚴和神秘感。很多人特別喜歡反覆找輪子，寧願選擇用匕首去殺敵，即使匕首旁邊有一支子彈上了膛的AK47,也會選擇視而不見。當然如果你要做一些複雜的應用，熟悉java是必須的， 另python 也可以調用weka (好像叫jython)
北大青鳥java培訓：八個最佳的數據中心開源挖掘工具？
數據挖掘，又稱為資料探勘、數據採礦。
它是資料庫知識發現(英語：Knowledge-DiscoveryinDatabases，簡稱：KDD)中的一個步驟，是一個挖掘和分析大量數據並從中提取信息的過程。
其中一些應用包括市場細分-如識別客戶從特定品牌購買特定產品的特徵，欺詐檢測-識別可能導致在線欺詐的交易模式等。
在本文中，貴陽電腦培訓整理了進行數據挖掘的8個最佳開源工具。
1、WekaWEKA作為一個公開的數據挖掘工作平台，集合了大量能承擔數據挖掘任務的機器學習演算法，包括對數據進行預處理，分類，回歸、聚類、關聯規則以及在新的互動式界面上的可視化。
2、RapidMinerRapidMiner是世界領先的數據挖掘解決方案，在一個非常大的程度上有著先進技術。
它數據挖掘任務涉及範圍廣泛，包括各種數據藝術，能簡化數據挖掘過程的設計和評價。
3、OrangeOrange是一個基於組件的數據挖掘和機器學習軟體套裝，它的功能即友好，又很強大，快速而又多功能的可視化編程前端，以便瀏覽數據分析和可視化，基綁定了Python以進行腳本開發。
它包含了完整的一系列的組件以進行數據預處理，並提供了數據帳目，過渡，建模，模式評估和勘探的功能。
其由C++和Python開發，它的圖形庫是由跨平台的Qt框架開發。
4、KnimeKNIME(KonstanzInformationMiner)是一個用戶友好，智能的，並有豐演的開源的數據集成，數據處理，數據分析和數據勘探平台。
5、jHepWorkjHepWork是一套功能完整的面向對象科學數據分析框架。
Jython宏是用來展示一維和二維直方圖的數據。
該程序包括許多工具，可以用來和二維三維的科學圖形進行互動。
6、ApacheMahoutApacheMahout是ApacheSoftwareFoundation(ASF)開發的一個全新的開源項目，其主要目標是創建一些可伸縮的機器學習演算法，供開發人員在Apache在許可下免費使用。
該項目已經發展到了它的最二個年頭，目前只有一個公共發行版。
Mahout包含許多實現，包括集群、分類、CP和進化程序。
此外，通過使用ApacheHadoop庫，Mahout可以有效地擴展到雲中。
7、ELKIELKI(EnvironmentforDevelopingKDD-ApplicationsSupportedbyIndex-Structures)主要用來聚類和找離群點。
ELKI是類似於weka的數據挖掘平台，用java編寫，有GUI圖形界面。
可以用來尋找離群點。
原創文章，作者：小藍，如若轉載，請註明出處：https://www.506064.com/zh-tw/n/279714.html
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