wekajava
發布時間: 2021-03-28 13:06:31
『壹』 調用weka包的java文件如何編譯
很簡單，只要用eclipse把weka.jar添加到項目的構建路徑，就可以編譯了。
如果是用手工的javac來編譯,則要用-classpath將weka.jar包括進來才能正常編譯。
如：javac -classpath ".;D:\weka.jar" xxx.java
javac用法：javac <選項> <源文件>
其中，可能的選項包括：
-classpath <路徑> 指定查找用戶類文件和注釋處理程序的位置
-cp <路徑> 指定查找用戶類文件和注釋處理程序的位置
如果是想做更深入的開發，可以將weka-src.jar 解壓出來當成源碼導入到eclipse工程。
『貳』 如何使用Java Weka開源項目，實現J48決策樹、支持向量機演算法，在10個UCI數據集上對這兩個演算法進行性能
publicstaticvoidRegular()throwsException{
Fileinputfile=newFile("F:\weka\eucalyptus_Train.arff");
ArffLoaderloader=newArffLoader();
loader.setFile(inputfile);
InstancesinsTrain=loader.getDataSet();
insTrain.setClassIndex(insTrain.numAttributes()-1);
inputfile=newFile("F:\weka\eucalyptus_Test.arff");
loader.setFile(inputfile);
InstancesinsTest=loader.getDataSet();
insTest.setClassIndex(insTest.numAttributes()-1);
doublesum=insTest.numInstances();
intright=0;
Classifierclas=newJ48();
//Classifierclas=newweka.classifiers.bayes.BayesNet();
clas.buildClassifier(insTrain);
for(inti=0;i<sum;i++){
if(clas.classifyInstance(insTest.instance(i))==insTest.instance(i).classValue()){
right++;
}
System.out.println(clas.classifyInstance(insTest.instance(i))+":"+insTest.instance(i).classValue());
}
System.out.println("分類准確率："+right/sum);
}svm的話，要用一個wlsvm的包。 代碼是一樣的，就是Classifier class= new J48()這里要用svm的實例
『叄』 java1.8下載什麼版本的weka合適
什麼版本都可以,最新的是3.8,不過中文版的只有3.7
『肆』 Java怎麼執行weka裡面的命令行，也就是用Java語句將一個csv文件轉換成arff文件，跪求大神~~
FileReaderfr=newFileReader("F:\我的資料\數據統計\myfile.csv");
Instancesm_inInstances=newInstances(fr);
DataSink.write("F:\我的資料\數據統計\myfile.arff",m_inInstances);
『伍』 java怎麼調用weka使用模型
java文件讀取的時候有中文就很出現亂碼，通常獲取到的文件中通常都是「iso8859-1」格式，需要轉換為「UTF-8」格式。
如：String
str
=
new
String（str.getByte("iso8859-1"),"UTF-8"）;進行下強制轉換後在進行讀取即可。
『陸』 在cmd中用java調用weka.core.converters.TextDirectoryLoader時，說「找不到或無法載入主類weka」
環境變數沒有設置，win7：在我的電腦->屬性->高級系統設置->環境變數->系統變數里，找到classpath的變數，就是在設置jre環境變數的時候添加的那個屬性，後面加上;C:\Program Files\Weka-3-7\weka.jar;（前後倆分號都要！），然後點確定，重啟電腦，再試試
『柒』 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 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.;
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). Inction of decision trees. Machine Learning. 1(1):81-106.
* <p/>
<!-- globalinfo-end -->
*
<!-- technical-bibtex-start -->
* BibTeX:
* <pre>
* @article{Quinlan1986,
* author = {R. Quinlan},
* journal = {Machine Learning},
* number = {1},
* pages = {81-106},
* title = {Inction 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 ([email protected])
* @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: "
+ 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, "Inction 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 if instance has missing values
*/
public double classifyInstance(Instance instance)
throws {
if (instance.hasMissingValue()) {
throw new ("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 if instance has missing values
*/
public double[] distributionForInstance(Instance instance)
throws {
if (instance.hasMissingValue()) {
throw new ("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 " + 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 proced 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(" ");
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(" ");
buffer.append(" protected static double node" + id + "(Object[] i) { ");
// 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(" ");
buffer.append(" } ");
} else {
buffer.append(" checkMissing(i, " + m_Attribute.index() + "); ");
buffer.append(" // " + m_Attribute.name() + " ");
// 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) + "")) ");
buffer.append(" return node" + newID + "(i); ");
subBuffers[i] = new StringBuffer();
newID = m_Successors[i].toSource(newID, subBuffers[i]);
}
buffer.append(" else ");
buffer.append(" throw new IllegalArgumentException("Value '" + i["
+ m_Attribute.index() + "] + "' is not allowed!"); ");
buffer.append(" } ");
// 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:
* <pre><code>
* public static double classify(Object[] i);
* </code></pre>
* where the array <code>i</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 + " { ");
result.append(" private static void checkMissing(Object[] i, int index) { ");
result.append(" if (i[index] == null) ");
result.append(" throw new IllegalArgumentException("Null values "
+ "are not allowed!"); ");
result.append(" } ");
result.append(" public static double classify(Object[] i) { ");
id = 0;
result.append(" return node" + id + "(i); ");
result.append(" } ");
toSource(id, result);
result.append("} ");
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);
}
}
『捌』 java 怎樣用導入 weka
引入weka的jar就可以了，把jar加到classpath下，用eclipse就，右鍵點項目名 --> build path --> add external archives 加入相關的jar就可以！！
『玖』 可以用java直接調用weka里的分類器么
用weka的什麼演算法？訓練什麼樣的數據，應用到哪裡？目標是什麼？matlab是不是能夠調用weka?我印象中matlab裡面有好多與weka類似的演算法；java可以調用weka；據說matlab和java可以互相調用。