Java Normalize类代码示例

本文整理汇总了Java中weka.filters.unsupervised.attribute.Normalize类的典型用法代码示例。如果您正苦于以下问题：Java Normalize类的具体用法？Java Normalize怎么用？Java Normalize使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。

Normalize类属于weka.filters.unsupervised.attribute包，在下文中一共展示了Normalize类的10个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。

示例1: loadDataFile

import weka.filters.unsupervised.attribute.Normalize; //导入依赖的package包/类
@Override
public Instances loadDataFile(String filename) {

    String path = System.getProperty("user.dir") + "\\resources\\datasets\\";
    path = path.concat(filename);
    System.out.println("Path:\t\t" + path);
    System.out.println("Dataset:\t" + filename);
    ConverterUtils.DataSource source;
    try {
        source = new ConverterUtils.DataSource(path);
        data = source.getDataSet();
        System.out.println(filename + " -> Data loaded.");
        // Normalizacja atrybutów, domyslne ustawienia
        Normalize filterNorm = new Normalize();
        filterNorm.setInputFormat(data);
        data = Filter.useFilter(data, filterNorm);
        System.out.println("Data Normalized");
        System.out.println();
        return data;
    } catch (Exception e) {
        e.printStackTrace();

    }
    return null;

}

开发者ID:kokojumbo，项目名称:master-thesis，代码行数:27，代码来源:DBScanImbalancedAlgorithm.java

示例2: loadDataFile

import weka.filters.unsupervised.attribute.Normalize; //导入依赖的package包/类
@Override
public Instances loadDataFile(String filename) {
    this.filename = filename;
    String path = System.getProperty("user.dir") + "\\resources\\datasets\\";
    path = path.concat(filename);
    System.out.println("Path:\t\t" + path);
    System.out.println("Dataset:\t" + filename);
    ConverterUtils.DataSource source;
    try {
        source = new ConverterUtils.DataSource(path);
        data = source.getDataSet();
        System.out.println(filename + " -> Data loaded.");
        // Normalizacja atrybutów, domyslne ustawienia
        Normalize filterNorm = new Normalize();
        filterNorm.setInputFormat(data);
        data = Filter.useFilter(data, filterNorm);
        System.out.println("Data Normalized");
        System.out.println();
        return data;
    } catch (Exception e) {
        e.printStackTrace();

    }
    return null;

}

开发者ID:kokojumbo，项目名称:master-thesis，代码行数:27，代码来源:ClusterImbalancedAlgorithm.java

示例3: loadDataFile

import weka.filters.unsupervised.attribute.Normalize; //导入依赖的package包/类
public static Instances loadDataFile(String filename) {
        Instances data;
        String path = System.getProperty("user.dir") + "\\resources\\datasets\\";
        path = path.concat(filename);
//        System.out.println("Path:\t\t" + path);
//        System.out.println("Dataset:\t" + filename);
        ConverterUtils.DataSource source;
        try {
            source = new ConverterUtils.DataSource(path);
            data = source.getDataSet();
            //System.out.println(filename + " -> Data loaded.");
            // Normalizacja atrybutów, domyslne ustawienia
            Normalize filterNorm = new Normalize();
            filterNorm.setInputFormat(data);
            data = Filter.useFilter(data, filterNorm);
            //System.out.println("Data Normalized");
            //System.out.println();
            return data;
        } catch (Exception e) {
            e.printStackTrace();

        }
        return null;

    }

开发者ID:kokojumbo，项目名称:master-thesis，代码行数:26，代码来源:LoadUtils.java

示例4: loadDataFilePrint

import weka.filters.unsupervised.attribute.Normalize; //导入依赖的package包/类
public static Instances loadDataFilePrint(String filename) {
    Instances data;
    String path = System.getProperty("user.dir") + "\\resources\\datasets\\";
    path = path.concat(filename);
    System.out.println("Path:\t\t" + path);
    System.out.println("Dataset:\t" + filename);
    ConverterUtils.DataSource source;
    try {
        source = new ConverterUtils.DataSource(path);
        data = source.getDataSet();
        System.out.println(filename + " -> Data loaded.");
        // Normalizacja atrybutów, domyslne ustawienia
        Normalize filterNorm = new Normalize();
        filterNorm.setInputFormat(data);
        data = Filter.useFilter(data, filterNorm);
        System.out.println("Data Normalized");
        System.out.println();
        return data;
    } catch (Exception e) {
        e.printStackTrace();

    }
    return null;

}

开发者ID:kokojumbo，项目名称:master-thesis，代码行数:26，代码来源:LoadUtils.java

示例5: createNormalizationFilter

import weka.filters.unsupervised.attribute.Normalize; //导入依赖的package包/类
/**
 * Normalizes the given data
 * 
 * @param isTrainingSet
 *            the Instances to be normalized
 * @param fvAttributes
 *            List<Attribute> the list of attributes of the current dataset
 * @return the normalized Instances
 */
public Normalize createNormalizationFilter(Instances isTrainingSet) {


	// set the Normalize object
	Normalize norm = new Normalize();
	try {

		// set the parameters for norm object
		norm.setInputFormat(isTrainingSet);
		
		// set and print the normalization options
		
		String[] options = { "-S", "2.0", "-T", "-1.0" };
		norm.setOptions(options);
		//System.out.print("Normalization options:\t");
		/*for (int i = 0; i < norm.getOptions().length; i++) {
			System.out.print(norm.getOptions()[i] + "\t");
		}*/
		
		
		// normalized instances calculated
		/*isTrainingSet_norm = Filter.useFilter(isTrainingSet, norm);
		isTrainingSet_norm.setClassIndex(fvAttributes.size() - 1);*/

	} catch (Exception e) {
		System.out.println("Data Normalization filter cannot be created!");
		e.printStackTrace();
	}

	// System.out.println("-----TRAINING SET-------");
	// System.out.println(isTrainingSet_norm);

	return norm;
}

开发者ID:socialsensor，项目名称:computational-verification，代码行数:44，代码来源:DataHandler.java

示例6: normalizeData

import weka.filters.unsupervised.attribute.Normalize; //导入依赖的package包/类
public Instances normalizeData(Instances dataset, int classIndex, Normalize normFilter){
	
	Instances dataset_norm = null;
	try {
		dataset_norm = Filter.useFilter(dataset, normFilter);
		dataset_norm.setClassIndex(classIndex);	
	} catch (Exception e) {
		System.out.println("Data Normalization cannot be performed! Please check your data!");
		e.printStackTrace();
	}
	
	return dataset_norm;
}

开发者ID:socialsensor，项目名称:computational-verification，代码行数:14，代码来源:DataHandler.java

示例7: buildClassifier

import weka.filters.unsupervised.attribute.Normalize; //导入依赖的package包/类
/**
 * Method for building the classifier.
 * 
 * @param data the set of training instances.
 * @throws Exception if the classifier can't be built successfully.
 */
@Override
public void buildClassifier(Instances data) throws Exception {
  reset();

  // can classifier handle the data?
  getCapabilities().testWithFail(data);

  data = new Instances(data);
  data.deleteWithMissingClass();

  if (data.numInstances() > 0 && !m_dontReplaceMissing) {
    m_replaceMissing = new ReplaceMissingValues();
    m_replaceMissing.setInputFormat(data);
    data = Filter.useFilter(data, m_replaceMissing);
  }

  // check for only numeric attributes
  boolean onlyNumeric = true;
  for (int i = 0; i < data.numAttributes(); i++) {
    if (i != data.classIndex()) {
      if (!data.attribute(i).isNumeric()) {
        onlyNumeric = false;
        break;
      }
    }
  }

  if (!onlyNumeric) {
    if (data.numInstances() > 0) {
      m_nominalToBinary = new weka.filters.supervised.attribute.NominalToBinary();
    } else {
      m_nominalToBinary = new weka.filters.unsupervised.attribute.NominalToBinary();
    }
    m_nominalToBinary.setInputFormat(data);
    data = Filter.useFilter(data, m_nominalToBinary);
  }

  if (!m_dontNormalize && data.numInstances() > 0) {

    m_normalize = new Normalize();
    m_normalize.setInputFormat(data);
    data = Filter.useFilter(data, m_normalize);
  }

  m_numInstances = data.numInstances();

  m_weights = new double[data.numAttributes() + 1];
  m_data = new Instances(data, 0);

  if (data.numInstances() > 0) {
    data.randomize(new Random(getSeed())); // randomize the data
    train(data);
  }
}

开发者ID:mydzigear，项目名称:repo.kmeanspp.silhouette_score，代码行数:61，代码来源:SGD.java

示例8: buildClassifier

import weka.filters.unsupervised.attribute.Normalize; //导入依赖的package包/类
/**
 * Method for building the classifier.
 * 
 * @param data the set of training instances.
 * @throws Exception if the classifier can't be built successfully.
 */
public void buildClassifier(Instances data) throws Exception {
  reset();
  
  // can classifier handle the data?
  getCapabilities().testWithFail(data);
  
  data = new Instances(data);
  data.deleteWithMissingClass();
  
  if (data.numInstances() > 0 && !m_dontReplaceMissing) {
    m_replaceMissing = new ReplaceMissingValues();
    m_replaceMissing.setInputFormat(data);
    data = Filter.useFilter(data, m_replaceMissing);
  }
  
  // check for only numeric attributes
  boolean onlyNumeric = true;
  for (int i = 0; i < data.numAttributes(); i++) {
    if (i != data.classIndex()) {
      if (!data.attribute(i).isNumeric()) {
        onlyNumeric = false;
        break;
      }
    }
  }
  
  if (!onlyNumeric) {
    if (data.numInstances() > 0) {
      m_nominalToBinary = new weka.filters.supervised.attribute.NominalToBinary();
    } else {
      m_nominalToBinary = new weka.filters.unsupervised.attribute.NominalToBinary();
    }
    m_nominalToBinary.setInputFormat(data);
    data = Filter.useFilter(data, m_nominalToBinary);
  }
  
  if (!m_dontNormalize && data.numInstances() > 0) {

    m_normalize = new Normalize();
    m_normalize.setInputFormat(data);
    data = Filter.useFilter(data, m_normalize);
  }
  
  m_numInstances = data.numInstances();
  
  m_weights = new double[data.numAttributes() + 1];
  m_data = new Instances(data, 0);

  if (data.numInstances() > 0) {
    data.randomize(new Random(getSeed())); // randomize the data
    train(data);    
  }
}

开发者ID:dsibournemouth，项目名称:autoweka，代码行数:60，代码来源:SGD.java

示例9: buildClassifier

import weka.filters.unsupervised.attribute.Normalize; //导入依赖的package包/类
/**
 * Method for building the classifier.
 * 
 * @param data the set of training instances.
 * @throws Exception if the classifier can't be built successfully.
 */
public void buildClassifier(Instances data) throws Exception {
  reset();
  
  // can classifier handle the data?
  getCapabilities().testWithFail(data);
  
  data = new Instances(data);
  data.deleteWithMissingClass();
  
  if (data.numInstances() > 0 && !m_dontReplaceMissing) {
    m_replaceMissing = new ReplaceMissingValues();
    m_replaceMissing.setInputFormat(data);
    data = Filter.useFilter(data, m_replaceMissing);
  }
  
  // check for only numeric attributes
  boolean onlyNumeric = true;
  for (int i = 0; i < data.numAttributes(); i++) {
    if (i != data.classIndex()) {
      if (!data.attribute(i).isNumeric()) {
        onlyNumeric = false;
        break;
      }
    }
  }
  
  if (!onlyNumeric) {
    m_nominalToBinary = new NominalToBinary();
    m_nominalToBinary.setInputFormat(data);
    data = Filter.useFilter(data, m_nominalToBinary);
  }
  
  if (!m_dontNormalize && data.numInstances() > 0) {

    m_normalize = new Normalize();
    m_normalize.setInputFormat(data);
    data = Filter.useFilter(data, m_normalize);
  }
  
  m_weights = new double[data.numAttributes() + 1];
  m_data = new Instances(data, 0);
  
  if (data.numInstances() > 0) {
    train(data);    
  }
}

开发者ID:williamClanton，项目名称:jbossBA，代码行数:53，代码来源:SPegasos.java

示例10: main

import weka.filters.unsupervised.attribute.Normalize; //导入依赖的package包/类
public static void main(String[] args) {
    try {


        CSVLoader loader = new CSVLoader();
        loader.setSource(new File(OJOSECO_FILEPATH));



        Instances data = loader.getDataSet();

        Normalize normalize = new Normalize();
        normalize.setInputFormat(data);
        data = Filter.useFilter(data, normalize);

        data.setClassIndex(data.numAttributes() - 1);

        System.out.println(data.toSummaryString());



        data.randomize(new Random(0));

        int trainSize = Math.toIntExact(Math.round(data.numInstances() * RATIO_TEST));
        int testSize = data.numInstances() - trainSize;

        Instances train = new Instances(data, 0, trainSize);
        Instances test = new Instances(data, trainSize, testSize);



        MultilayerPerceptron mlp = new MultilayerPerceptron();
        mlp.setOptions(Utils.splitOptions("-L 0.3 -M 0.2 -N 500 -V 0 -S 0 -E 20 -H a"));
        mlp.buildClassifier(train);

        System.out.println(mlp.toString());



        Evaluation eval = new Evaluation(test);
        eval.evaluateModel(mlp, test);

        System.out.println(eval.toSummaryString());


    } catch (Exception e) {
        e.printStackTrace();
    }

}

开发者ID:garciparedes，项目名称:java-examples，代码行数:51，代码来源:WekaMultiLayerPerceptron.java

注：本文中的weka.filters.unsupervised.attribute.Normalize类示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。