libsvm.java

Java 编写的多种数据挖掘算法 包括聚类、分类、预处理等

      setKernelType(
          new SelectedTag(Integer.parseInt(tmpStr), TAGS_KERNELTYPE));
    else
      setKernelType(
          new SelectedTag(KERNELTYPE_RBF, TAGS_KERNELTYPE));
    
    tmpStr = Utils.getOption('D', options);
    if (tmpStr.length() != 0)
      setDegree(Integer.parseInt(tmpStr));
    else
      setDegree(3);
    
    tmpStr = Utils.getOption('G', options);
    if (tmpStr.length() != 0)
      setGamma(Double.parseDouble(tmpStr));
    else
      setGamma(0);
    
    tmpStr = Utils.getOption('R', options);
    if (tmpStr.length() != 0)
      setCoef0(Double.parseDouble(tmpStr));
    else
      setCoef0(0);
    
    tmpStr = Utils.getOption('N', options);
    if (tmpStr.length() != 0)
      setNu(Double.parseDouble(tmpStr));
    else
      setNu(0.5);
    
    tmpStr = Utils.getOption('M', options);
    if (tmpStr.length() != 0)
      setCacheSize(Double.parseDouble(tmpStr));
    else
      setCacheSize(40);
    
    tmpStr = Utils.getOption('C', options);
    if (tmpStr.length() != 0)
      setCost(Double.parseDouble(tmpStr));
    else
      setCost(1);
    
    tmpStr = Utils.getOption('E', options);
    if (tmpStr.length() != 0)
      setEps(Double.parseDouble(tmpStr));
    else
      setEps(1e-3);
    
    setNormalize(Utils.getFlag('Z', options));
    
    tmpStr = Utils.getOption('P', options);
    if (tmpStr.length() != 0)
      setLoss(Double.parseDouble(tmpStr));
    else
      setLoss(0.1);
    
    setShrinking(!Utils.getFlag('H', options));
    
    setWeights(Utils.getOption('W', options));
  }
  
  /**
   * Returns the current options
   * 
   * @return            the current setup
   */
  public String[] getOptions() {
    Vector        result;
    
    result  = new Vector();
    
    result.add("-S");
    result.add("" + m_SVMType);
    
    result.add("-K");
    result.add("" + m_KernelType);
    
    result.add("-D");
    result.add("" + getDegree());
    
    result.add("-G");
    result.add("" + getGamma());
    
    result.add("-R");
    result.add("" + getCoef0());
    
    result.add("-N");
    result.add("" + getNu());
    
    result.add("-M");
    result.add("" + getCacheSize());
    
    result.add("-C");
    result.add("" + getCost());
    
    result.add("-E");
    result.add("" + getEps());
    
    result.add("-P");
    result.add("" + getLoss());
    
    if (!getShrinking())
      result.add("-H");
    
    if (getNormalize())
      result.add("-Z");
    
    if (getWeights().length() != 0) {
      result.add("-W");
      result.add("" + getWeights());
    }
    
    return (String[]) result.toArray(new String[result.size()]);
  }
  
  /**
   * returns whether the libsvm classes are present or not, i.e. whether the 
   * classes are in the classpath or not
   *
   * @return whether the libsvm classes are available
   */
  public static boolean isPresent() {
    return m_Present;
  }
  
  /**
   * Sets type of SVM (default SVMTYPE_C_SVC)
   * 
   * @param value       the type of the SVM
   */
  public void setSVMType(SelectedTag value) {
    if (value.getTags() == TAGS_SVMTYPE)
      m_SVMType = value.getSelectedTag().getID();
  }
  
  /**
   * Gets type of SVM
   * 
   * @return            the type of the SVM
   */
  public SelectedTag getSVMType() {
    return new SelectedTag(m_SVMType, TAGS_SVMTYPE);
  }
  
  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for
   *         displaying in the explorer/experimenter gui
   */
  public String SVMTypeTipText() {
    return "The type of SVM to use.";
  }
  
  /**
   * Sets type of kernel function (default KERNELTYPE_RBF)
   * 
   * @param value       the kernel type
   */
  public void setKernelType(SelectedTag value) {
    if (value.getTags() == TAGS_KERNELTYPE)
      m_KernelType = value.getSelectedTag().getID();
  }
  
  /**
   * Gets type of kernel function
   * 
   * @return            the kernel type
   */
  public SelectedTag getKernelType() {
    return new SelectedTag(m_KernelType, TAGS_KERNELTYPE);
  }
  
  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for
   *         displaying in the explorer/experimenter gui
   */
  public String kernelTypeTipText() {
    return "The type of kernel to use";
  }
  
  /**
   * Sets the degree of the kernel
   * 
   * @param value       the degree of the kernel
   */
  public void setDegree(int value) {
    m_Degree = value;
  }
  
  /**
   * Gets the degree of the kernel
   * 
   * @return            the degree of the kernel
   */
  public int getDegree() {
    return m_Degree;
  }
  
  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for
   *         displaying in the explorer/experimenter gui
   */
  public String degreeTipText() {
    return "The degree of the kernel.";
  }
  
  /**
   * Sets gamma (default = 1/no of attributes)
   * 
   * @param value       the gamma value
   */
  public void setGamma(double value) {
    m_Gamma = value;
  }
  
  /**
   * Gets gamma
   * 
   * @return            the current gamma
   */
  public double getGamma() {
    return m_Gamma;
  }
  
  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for
   *         displaying in the explorer/experimenter gui
   */
  public String gammaTipText() {
    return "The gamma to use, if 0 then 1/max_index is used.";
  }
  
  /**
   * Sets coef (default 0)
   * 
   * @param value       the coef
   */
  public void setCoef0(double value) {
    m_Coef0 = value;
  }
  
  /**
   * Gets coef
   * 
   * @return            the coef
   */
  public double getCoef0() {
    return m_Coef0;
  }
  
  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for
   *         displaying in the explorer/experimenter gui
   */
  public String coef0TipText() {
    return "The coefficient to use.";
  }
  
  /**
   * Sets nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
   * 
   * @param value       the new nu value
   */
  public void setNu(double value) {
    m_nu = value;
  }
  
  /**
   * Gets nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
   * 
   * @return            the current nu value
   */
  public double getNu() {
    return m_nu;
  }
  
  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for
   *         displaying in the explorer/experimenter gui
   */
  public String nuTipText() {
    return "The value of nu for nu-SVC, one-class SVM and nu-SVR.";
  }
  
  /**
   * Sets cache memory size in MB (default 40)
   * 
   * @param value       the memory size in MB
   */
  public void setCacheSize(double value) {
    m_CacheSize = value;
  }
  
  /**
   * Gets cache memory size in MB
   * 
   * @return            the memory size in MB
   */
  public double getCacheSize() {
    return m_CacheSize;
  }
  
  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for
   *         displaying in the explorer/experimenter gui
   */
  public String cacheSizeTipText() {
    return "The cache size in MB.";
  }
  
  /**
   * Sets the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)
   * 
   * @param value       the cost value
   */
  public void setCost(double value) {
    m_Cost = value;
  }
  
  /**
   * Sets the parameter C of C-SVC, epsilon-SVR, and nu-SVR
   * 
   * @return            the cost value
   */
  public double getCost() {
    return m_Cost;
  }
  
  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for
   *         displaying in the explorer/experimenter gui
   */
  public String costTipText() {
    return "The cost parameter C for C-SVC, epsilon-SVR and nu-SVR.";
  }
  
  /**
   * Sets tolerance of termination criterion (default 0.001)
   * 
   * @param value       the tolerance
   */
  public void setEps(double value) {
    m_eps = value;
  }
  
  /**
   * Gets tolerance of termination criterion
   * 
   * @return            the current tolerance
   */
  public double getEps() {
    return m_eps;
  }
  
  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for
   *         displaying in the explorer/experimenter gui
   */
  public String epsTipText() {
    return "The tolerance of the termination criterion.";
  }
  
  /**
   * Sets the epsilon in loss function of epsilon-SVR (default 0.1)
   * 
   * @param value       the loss epsilon
   */
  public void setLoss(double value) {
    m_Loss = value;
  }
  
  /**
   * Gets the epsilon in loss function of epsilon-SVR
   * 
   * @return            the loss epsilon
   */
  public double getLoss() {
    return m_Loss;
  }
  
  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for
   *         displaying in the explorer/experimenter gui
   */
  public String lossTipText() {
    return "The epsilon for the loss function in epsilon-SVR.";
  }
  
  /**
   * whether to use the shrinking heuristics
   * 
   * @param value       true uses shrinking
   */
  public void setShrinking(boolean value) {
    m_Shrinking = value;
  }
  
  /**
   * whether to use the shrinking heuristics
   * 
   * @return            true, if shrinking is used
   */
  public boolean getShrinking() {
    return m_Shrinking;
  }
  
  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for
   *         displaying in the explorer/experimenter gui
   */
  public String shrinkingTipText() {
    return "Whether to use the shrinking heuristic.";
  }
  
  /**
   * whether to normalize input data
   * 
   * @param value       whether to normalize the data
   */
  public void setNormalize(boolean value) {
    m_Normalize = value;
  }
  
  /**
   * whether to normalize input data
   * 
   * @return            true, if the data is normalized
   */
  public boolean getNormalize() {
    return m_Normalize;
  }
  
  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for
   *         displaying in the explorer/experimenter gui
   */
  public String normalizeTipText() {
    return "Whether to normalize the data.";
  }
  
  /**
   * Sets the parameters C of class i to weight[i]*C, for C-SVC (default 1).
   * Blank separated list of doubles.
   * 
   * @param weightsStr          the weights (doubles, separated by blanks)
   */
  public void setWeights(String weightsStr) {
    StringTokenizer       tok;
    int                   i;
    
    tok           = new StringTokenizer(weightsStr, " ");
    m_Weight      = new double[tok.countTokens()];
    m_WeightLabel = new int[tok.countTokens()];
    
    if (m_Weight.length == 0)
      System.out.println(
          "Zero Weights processed. Default weights will be used");
    
    for (i = 0; i < m_Weight.length; i++) {
      m_Weight[i] = Double.parseDouble(tok.nextToken());
      if (i == 0)
        m_WeightLabel[i] = -1;  // label of first class
      else
        m_WeightLabel[i] = i;