Java Utils.removeSubstring方法代碼示例

import weka.core.Utils; //導入方法依賴的package包/類
/**
 * Returns a string descriptive of the resultset key column values for this
 * resultset
 * 
 * @return a value of type 'String'
 */
protected String templateString() {

  String result = "";
  String tempResult = "";
  for (int m_ResultsetKeyColumn : m_ResultsetKeyColumns) {
    tempResult = m_Template.toString(m_ResultsetKeyColumn) + ' ';

    // compact the string
    tempResult = Utils.removeSubstring(tempResult, "weka.classifiers.");
    tempResult = Utils.removeSubstring(tempResult, "weka.filters.");
    tempResult = Utils.removeSubstring(tempResult,
      "weka.attributeSelection.");
    result += tempResult;
  }
  return result.trim();
}
 

import weka.core.Utils; //導入方法依賴的package包/類
/**
 * Sets up the bounds on our x and y axes to fit the dataset. Also repaints
 * the x and y axes.
 */
private void computeBounds() {

  m_boundaryPanel.computeMinMaxAtts(); // delegate to the BoundaryPanel

  String xName = (String) m_xAttBox.getSelectedItem();
  if (xName == null) {
    return;
  }
  xName = Utils.removeSubstring(xName, "X: ");
  xName = Utils.removeSubstring(xName, " (Num)");
  String yName = (String) m_yAttBox.getSelectedItem();
  yName = Utils.removeSubstring(yName, "Y: ");
  yName = Utils.removeSubstring(yName, " (Num)");

  m_xIndex = -1;
  m_yIndex = -1;
  for (int i = 0; i < m_trainingInstances.numAttributes(); i++) {
    if (m_trainingInstances.attribute(i).name().equals(xName)) {
      m_xIndex = i;
    }
    if (m_trainingInstances.attribute(i).name().equals(yName)) {
      m_yIndex = i;
    }
  }

  m_minX = m_boundaryPanel.getMinXBound();
  m_minY = m_boundaryPanel.getMinYBound();
  m_maxX = m_boundaryPanel.getMaxXBound();
  m_maxY = m_boundaryPanel.getMaxYBound();
  // System.err.println("setting bounds to " + m_minX + " " + m_minY + " " +
  // m_maxX + " " + m_maxY);
  m_xAxisPanel.repaint(0, 0, 0, m_xAxisPanel.getWidth(),
    m_xAxisPanel.getHeight());
  m_yAxisPanel.repaint(0, 0, 0, m_yAxisPanel.getWidth(),
    m_yAxisPanel.getHeight());
}
 

import weka.core.Utils; //導入方法依賴的package包/類
/**
 * makes sure that the filename is valid, i.e., replaces slashes, backslashes
 * and colons with underscores ("_"). Also try to prevent filename from
 * becoming insanely long by removing package part of class names.
 * 
 * @param filename the filename to cleanse
 * @return the cleansed filename
 */
protected String sanitizeFilename(String filename) {
  filename = filename.replaceAll("\\\\", "_").replaceAll(":", "_")
    .replaceAll("/", "_");
  filename = Utils.removeSubstring(filename,
    "weka.filters.supervised.instance.");
  filename = Utils.removeSubstring(filename,
    "weka.filters.supervised.attribute.");
  filename = Utils.removeSubstring(filename,
    "weka.filters.unsupervised.instance.");
  filename = Utils.removeSubstring(filename,
    "weka.filters.unsupervised.attribute.");
  filename = Utils.removeSubstring(filename, "weka.clusterers.");
  filename = Utils.removeSubstring(filename, "weka.associations.");
  filename = Utils.removeSubstring(filename, "weka.attributeSelection.");
  filename = Utils.removeSubstring(filename, "weka.estimators.");
  filename = Utils.removeSubstring(filename, "weka.datagenerators.");

  if (!m_isDBSaver && !m_relationNameForFilename) {
    filename = "";
    try {
      if (m_Saver.filePrefix().equals("")) {
        m_Saver.setFilePrefix("no-name");
      }
    } catch (Exception ex) {
      System.err.println(ex);
    }
  }

  return filename;
}
 

import weka.core.Utils; //導入方法依賴的package包/類
/**
 * Gets the results for a specified run number. Different run numbers
 * correspond to different randomizations of the data. Results produced should
 * be sent to the current ResultListener
 * 
 * @param run the run number to get results for.
 * @throws Exception if a problem occurs while getting the results
 */
@Override
public void doRun(int run) throws Exception {

  if (getRawOutput()) {
    if (m_ZipDest == null) {
      m_ZipDest = new OutputZipper(m_OutputFile);
    }
  }

  if (m_Instances == null) {
    throw new Exception("No Instances set");
  }
  // Randomize on a copy of the original dataset
  Instances runInstances = new Instances(m_Instances);
  Random random = new Random(run);
  runInstances.randomize(random);
  if (runInstances.classAttribute().isNominal()) {
    runInstances.stratify(m_NumFolds);
  }
  for (int fold = 0; fold < m_NumFolds; fold++) {
    // Add in some fields to the key like run and fold number, dataset name
    Object[] seKey = m_SplitEvaluator.getKey();
    Object[] key = new Object[seKey.length + 3];
    key[0] = Utils.backQuoteChars(m_Instances.relationName());
    key[1] = "" + run;
    key[2] = "" + (fold + 1);
    System.arraycopy(seKey, 0, key, 3, seKey.length);
    if (m_ResultListener.isResultRequired(this, key)) {
      Instances train = runInstances.trainCV(m_NumFolds, fold, random);
      Instances test = runInstances.testCV(m_NumFolds, fold);
      try {
        Object[] seResults = m_SplitEvaluator.getResult(train, test);
        Object[] results = new Object[seResults.length + 1];
        results[0] = getTimestamp();
        System.arraycopy(seResults, 0, results, 1, seResults.length);
        if (m_debugOutput) {
          String resultName = ("" + run + "." + (fold + 1) + "."
            + Utils.backQuoteChars(runInstances.relationName()) + "." + m_SplitEvaluator
            .toString()).replace(' ', '_');
          resultName = Utils.removeSubstring(resultName, "weka.classifiers.");
          resultName = Utils.removeSubstring(resultName, "weka.filters.");
          resultName = Utils.removeSubstring(resultName,
            "weka.attributeSelection.");
          m_ZipDest.zipit(m_SplitEvaluator.getRawResultOutput(), resultName);
        }
        m_ResultListener.acceptResult(this, key, results);
      } catch (Exception ex) {
        // Save the train and test datasets for debugging purposes?
        throw ex;
      }
    }
  }
}
 

import weka.core.Utils; //導入方法依賴的package包/類
/**
 * Gets the results for a specified run number. Different run numbers
 * correspond to different randomizations of the data. Results produced should
 * be sent to the current ResultListener
 * 
 * @param run the run number to get results for.
 * @throws Exception if a problem occurs while getting the results
 */
@Override
public void doRun(int run) throws Exception {

  if (getRawOutput()) {
    if (m_ZipDest == null) {
      m_ZipDest = new OutputZipper(m_OutputFile);
    }
  }

  if (m_Instances == null) {
    throw new Exception("No Instances set");
  }

  // Compute run and fold number from given run
  int fold = (run - 1) % m_NumFolds;
  run = ((run - 1) / m_NumFolds) + 1;

  // Randomize on a copy of the original dataset
  Instances runInstances = new Instances(m_Instances);
  Random random = new Random(run);
  runInstances.randomize(random);
  if (runInstances.classAttribute().isNominal()) {
    runInstances.stratify(m_NumFolds);
  }

  // Add in some fields to the key like run and fold number, dataset name
  Object[] seKey = m_SplitEvaluator.getKey();
  Object[] key = new Object[seKey.length + 3];
  key[0] = Utils.backQuoteChars(m_Instances.relationName());
  key[1] = "" + run;
  key[2] = "" + (fold + 1);
  System.arraycopy(seKey, 0, key, 3, seKey.length);
  if (m_ResultListener.isResultRequired(this, key)) {

    // Just to make behaviour absolutely consistent with
    // CrossValidationResultProducer
    for (int tempFold = 0; tempFold < fold; tempFold++) {
      runInstances.trainCV(m_NumFolds, tempFold, random);
    }

    Instances train = runInstances.trainCV(m_NumFolds, fold, random);
    Instances test = runInstances.testCV(m_NumFolds, fold);
    try {
      Object[] seResults = m_SplitEvaluator.getResult(train, test);
      Object[] results = new Object[seResults.length + 1];
      results[0] = getTimestamp();
      System.arraycopy(seResults, 0, results, 1, seResults.length);
      if (m_debugOutput) {
        String resultName = ("" + run + "." + (fold + 1) + "."
          + Utils.backQuoteChars(runInstances.relationName()) + "." + m_SplitEvaluator
          .toString()).replace(' ', '_');
        resultName = Utils.removeSubstring(resultName, "weka.classifiers.");
        resultName = Utils.removeSubstring(resultName, "weka.filters.");
        resultName = Utils.removeSubstring(resultName,
          "weka.attributeSelection.");
        m_ZipDest.zipit(m_SplitEvaluator.getRawResultOutput(), resultName);
      }
      m_ResultListener.acceptResult(this, key, results);
    } catch (Exception ex) {
      // Save the train and test datasets for debugging purposes?
      throw ex;
    }
  }
}