bayesianlogisticregression.java

bayes算法在java环境下的开发和实现过程

    if (Thres.length() != 0) {
      Threshold = Double.parseDouble(Thres);
    }

    //Set Hyperparameter Type 
    String Hype = Utils.getOption('H', options);

    if (Hype.length() != 0) {
      HyperparameterSelection = Integer.parseInt(Hype);
    }

    //Set Hyperparameter Value 
    String HyperValue = Utils.getOption('V', options);

    if (HyperValue.length() != 0) {
      HyperparameterValue = Double.parseDouble(HyperValue);
    }

    // Set hyper parameter range or list.
    String HyperparameterRange = Utils.getOption("R", options);

    //Set Prior class.
    String strPrior = Utils.getOption('P', options);

    if (strPrior.length() != 0) {
      PriorClass = Integer.parseInt(strPrior);
    }

    String folds = Utils.getOption('F', options);

    if (folds.length() != 0) {
      NumFolds = Integer.parseInt(folds);
    }

    String iterations = Utils.getOption('I', options);

    if (iterations.length() != 0) {
      maxIterations = Integer.parseInt(iterations);
    }

    NormalizeData = Utils.getFlag('N', options);

    //TODO: Implement this method for other options.
    Utils.checkForRemainingOptions(options);
  }

  /**
   *
   */
  public String[] getOptions() {
    Vector result = new Vector();

    //Add Debug Mode to options.
    result.add("-D");

    //Add Tolerance value to options
    result.add("-Tl");
    result.add("" + Tolerance);

    //Add Threshold value to options
    result.add("-S");
    result.add("" + Threshold);

    //Add Hyperparameter value to options
    result.add("-H");
    result.add("" + HyperparameterSelection);

    result.add("-V");
    result.add("" + HyperparameterValue);

    result.add("-R");
    result.add("" + HyperparameterRange);

    //Add Prior Class to options
    result.add("-P");
    result.add("" + PriorClass);

    result.add("-F");
    result.add("" + NumFolds);

    result.add("-I");
    result.add("" + maxIterations);

    result.add("-N");

    return (String[]) result.toArray(new String[result.size()]);
  }

  /**
   * Main method for testing this class.
   *
   * @param argv the options
   */
  public static void main(String[] argv) {
    runClassifier(new BayesianLogisticRegression(), argv);
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String debugTipText() {
    return "Turns on debugging mode.";
  }

  /**
   *
   */
  public void setDebug(boolean debugMode) {
    debug = debugMode;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String hyperparameterSelectionTipText() {
    return "Select the type of Hyperparameter to be used.";
  }

  /**
   * Get the method used to select the hyperparameter
   *
   * @return the method used to select the hyperparameter
   */
  public SelectedTag getHyperparameterSelection() {
    return new SelectedTag(HyperparameterSelection, 
                           TAGS_HYPER_METHOD);
  }

  /**
   * Set the method used to select the hyperparameter
   *
   * @param newMethod the method used to set the hyperparameter
   */
  public void setHyperparameterSelection(SelectedTag newMethod) {
    if (newMethod.getTags() == TAGS_HYPER_METHOD) {
      int c = newMethod.getSelectedTag().getID();
      if (c >= 1 && c <= 3) {
        HyperparameterSelection = c;
      } else {
        throw new IllegalArgumentException("Wrong selection type, -H value should be: "
                                           + "1 for norm-based, 2 for CV-based and "
                                         + "3 for specific value");
      }
    }
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String priorClassTipText() {
    return "The type of prior to be used.";
  }

  /**
   * Set the type of prior to use.
   *
   * @param newMethod the type of prior to use.
   */
  public void setPriorClass(SelectedTag newMethod) {
    if (newMethod.getTags() == TAGS_PRIOR) {
      int c = newMethod.getSelectedTag().getID();
      if (c == GAUSSIAN || c == LAPLACIAN) {
        PriorClass = c;
      } else {
        throw new IllegalArgumentException("Wrong selection type, -P value should be: "
                                           + "1 for Gaussian or 2 for Laplacian");
      }
    }
  }

  /**
   * Get the type of prior to use.
   *
   * @return the type of prior to use
   */
  public SelectedTag getPriorClass() {
    return new SelectedTag(PriorClass,
                           TAGS_PRIOR);
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String thresholdTipText() {
    return "Set the threshold for classifiction. The logistic function doesn't "
      + "return a class label but an estimate of p(y=+1|B,x(i)). "
      + "These estimates need to be converted to binary class label predictions. "
      + "values above the threshold are assigned class +1.";
  }

  /**
   * Return the threshold being used.
   *
   * @return the threshold
   */
  public double getThreshold() {
    return Threshold;
  }

  /**
   * Set the threshold to use.
   *
   * @param threshold the threshold to use
   */
  public void setThreshold(double threshold) {
    Threshold = threshold;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String toleranceTipText() {
    return "This value decides the stopping criterion.";
  }

  /**
   * Get the tolerance value
   *
   * @return the tolerance value
   */
  public double getTolerance() {
    return Tolerance;
  }

  /**
   * Set the tolerance value
   *
   * @param tolerance the tolerance value to use
   */
  public void setTolerance(double tolerance) {
    Tolerance = tolerance;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String hyperparameterValueTipText() {
    return "Specific hyperparameter value. Used when the hyperparameter "
      + "selection method is set to specific value";
  }

  /**
   * Get the hyperparameter value. Used when the hyperparameter
   * selection method is set to specific value
   *
   * @return the hyperparameter value
   */
  public double getHyperparameterValue() {
    return HyperparameterValue;
  }

  /**
   * Set the hyperparameter value. Used when the hyperparameter
   * selection method is set to specific value
   *
   * @param hyperparameterValue the value of the hyperparameter
   */
  public void setHyperparameterValue(double hyperparameterValue) {
    HyperparameterValue = hyperparameterValue;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String numFoldsTipText() {
    return "The number of folds to use for CV-based hyperparameter selection.";
  }

  /**
   * Return the number of folds for CV-based hyperparameter selection
   *
   * @return the number of CV folds
   */
  public int getNumFolds() {
    return NumFolds;
  }

  /**
   * Set the number of folds to use for CV-based hyperparameter
   * selection
   *
   * @param numFolds number of folds to select
   */
  public void setNumFolds(int numFolds) {
    NumFolds = numFolds;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String maxIterationsTipText() {
    return "The maximum number of iterations to perform.";
  }

  /**
   * Get the maximum number of iterations to perform
   *
   * @return the maximum number of iterations
   */
  public int getMaxIterations() {
    return maxIterations;
  }

  /**
   * Set the maximum number of iterations to perform
   *
   * @param maxIterations maximum number of iterations
   */
  public void setMaxIterations(int maxIterations) {
    this.maxIterations = maxIterations;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String normalizeDataTipText() {
    return "Normalize the data.";
  }

  /**
   * Returns true if the data is to be normalized first
   *
   * @return true if the data is to be normalized
   */
  public boolean isNormalizeData() {
    return NormalizeData;
  }

  /**
   * Set whether to normalize the data or not
   *
   * @param normalizeData true if data is to be normalized
   */
  public void setNormalizeData(boolean normalizeData) {
    NormalizeData = normalizeData;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String hyperparameterRangeTipText() {
    return "Hyperparameter value range. In case of CV-based Hyperparameters, "
      + "you can specify the range in two ways: \n"
      + "Comma-Separated: L: 3,5,6 (This will be a list of possible values.)\n"
      + "Range: R:0.01-316,3.16 (This will take values from 0.01-316 (inclusive) "
      + "in multiplications of 3.16";
  }

  /**
   * Get the range of hyperparameter values to consider
   * during CV-based selection.
   *
   * @return the range of hyperparameters as a Stringe
   */
  public String getHyperparameterRange() {
    return HyperparameterRange;
  }

  /**
   * Set the range of hyperparameter values to consider
   * during CV-based selection
   *
   * @param hyperparameterRange the range of hyperparameter values
   */
  public void setHyperparameterRange(String hyperparameterRange) {
    HyperparameterRange = hyperparameterRange;
  }

  /**
   * Returns true if debug is turned on.
   *
   * @return true if debug is turned on
   */
  public boolean isDebug() {
    return debug;
  }
  
  /**
   * Returns the revision string.
   * 
   * @return		the revision
   */
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 1.3 $");
  }
}