libsvm.java

Weka

    String      result;
    int         i;
    
    result = "";
    for (i = 0; i < m_Weight.length; i++) {
      if (i > 0)
        result += " ";
      result += Double.toString(m_Weight[i]);
    }
    
    return result;
  }
  
  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for
   *         displaying in the explorer/experimenter gui
   */
  public String weightsTipText() {
    return "The weights to use for the classes, if empty 1 is used by default.";
  }
  
  /**
   * Returns whether probability estimates are generated instead of -1/+1 for 
   * classification problems.
   * 
   * @param value       whether to predict probabilities
   */
  public void setProbabilityEstimates(boolean value) {
    m_ProbabilityEstimates = value;
  }
  
  /**
   * Sets whether to generate probability estimates instead of -1/+1 for 
   * classification problems.
   * 
   * @return            true, if probability estimates should be returned
   */
  public boolean getProbabilityEstimates() {
    return m_ProbabilityEstimates;
  }
  
  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for
   *         displaying in the explorer/experimenter gui
   */
  public String probabilityEstimatesTipText() {
    return "Whether to generate probability estimates instead of -1/+1 for classification problems.";
  }
  
  /**
   * sets the specified field
   * 
   * @param o           the object to set the field for
   * @param name        the name of the field
   * @param value       the new value of the field
   */
  protected void setField(Object o, String name, Object value) {
    Field       f;
    
    try {
      f = o.getClass().getField(name);
      f.set(o, value);
    }
    catch (Exception e) {
      e.printStackTrace();
    }
  }
  
  /**
   * sets the specified field in an array
   * 
   * @param o           the object to set the field for
   * @param name        the name of the field
   * @param index       the index in the array
   * @param value       the new value of the field
   */
  protected void setField(Object o, String name, int index, Object value) {
    Field       f;
    
    try {
      f = o.getClass().getField(name);
      Array.set(f.get(o), index, value);
    }
    catch (Exception e) {
      e.printStackTrace();
    }
  }
  
  /**
   * returns the current value of the specified field
   * 
   * @param o           the object the field is member of
   * @param name        the name of the field
   * @return            the value
   */
  protected Object getField(Object o, String name) {
    Field       f;
    Object      result;
    
    try {
      f      = o.getClass().getField(name);
      result = f.get(o);
    }
    catch (Exception e) {
      e.printStackTrace();
      result = null;
    }
    
    return result;
  }
  
  /**
   * sets a new array for the field
   * 
   * @param o           the object to set the array for
   * @param name        the name of the field
   * @param type        the type of the array
   * @param length      the length of the one-dimensional array
   */
  protected void newArray(Object o, String name, Class type, int length) {
    newArray(o, name, type, new int[]{length});
  }
  
  /**
   * sets a new array for the field
   * 
   * @param o           the object to set the array for
   * @param name        the name of the field
   * @param type        the type of the array
   * @param dimensions  the dimensions of the array
   */
  protected void newArray(Object o, String name, Class type, int[] dimensions) {
    Field       f;
    
    try {
      f = o.getClass().getField(name);
      f.set(o, Array.newInstance(type, dimensions));
    }
    catch (Exception e) {
      e.printStackTrace();
    }
  }
  
  /**
   * executes the specified method and returns the result, if any
   * 
   * @param o                   the object the method should be called from
   * @param name                the name of the method
   * @param paramClasses        the classes of the parameters
   * @param paramValues         the values of the parameters
   * @return                    the return value of the method, if any (in that case null)
   */
  protected Object invokeMethod(Object o, String name, Class[] paramClasses, Object[] paramValues) {
    Method      m;
    Object      result;
    
    result = null;
    
    try {
      m      = o.getClass().getMethod(name, paramClasses);
      result = m.invoke(o, paramValues);
    }
    catch (Exception e) {
      e.printStackTrace();
      result = null;
    }
    
    return result;
  }
  
  /**
   * transfers the local variables into a svm_parameter object
   * 
   * @return the configured svm_parameter object
   */
  protected Object getParameters() {
    Object      result;
    int         i;
    
    try {
      result = Class.forName(CLASS_SVMPARAMETER).newInstance();
      
      setField(result, "svm_type", new Integer(m_SVMType));
      setField(result, "kernel_type", new Integer(m_KernelType));
      setField(result, "degree", new Integer(m_Degree));
      setField(result, "gamma", new Double(m_GammaActual));
      setField(result, "coef0", new Double(m_Coef0));
      setField(result, "nu", new Double(m_nu));
      setField(result, "cache_size", new Double(m_CacheSize));
      setField(result, "C", new Double(m_Cost));
      setField(result, "eps", new Double(m_eps));
      setField(result, "p", new Double(m_Loss));
      setField(result, "shrinking", new Integer(m_Shrinking ? 1 : 0));
      setField(result, "nr_weight", new Integer(m_Weight.length));
      setField(result, "probability", new Integer(m_ProbabilityEstimates ? 1 : 0));
      
      newArray(result, "weight", Double.TYPE, m_Weight.length);
      newArray(result, "weight_label", Integer.TYPE, m_Weight.length);
      for (i = 0; i < m_Weight.length; i++) {
        setField(result, "weight", i, new Double(m_Weight[i]));
        setField(result, "weight_label", i, new Integer(m_WeightLabel[i]));
      }
    }
    catch (Exception e) {
      e.printStackTrace();
      result = null;
    }
    
    return result;
  }
  
  /**
   * returns the svm_problem
   * 
   * @param vx the x values
   * @param vy the y values
   * @return the svm_problem object
   */
  protected Object getProblem(Vector vx, Vector vy) {
    Object      result;
    
    try {
      result = Class.forName(CLASS_SVMPROBLEM).newInstance();
      
      setField(result, "l", new Integer(vy.size()));
      
      newArray(result, "x", Class.forName(CLASS_SVMNODE), new int[]{vy.size(), 0});
      for (int i = 0; i < vy.size(); i++)
        setField(result, "x", i, vx.elementAt(i));
      
      newArray(result, "y", Double.TYPE, vy.size());
      for (int i = 0; i < vy.size(); i++)
        setField(result, "y", i, vy.elementAt(i));
    }
    catch (Exception e) {
      e.printStackTrace();
      result = null;
    }
    
    return result;
  }
  
  /**
   * returns an instance into a sparse libsvm array
   * 
   * @param instance	the instance to work on
   * @return		the libsvm array
   * @throws Exception	if setup of array fails
   */
  protected Object instanceToArray(Instance instance) throws Exception {
    int		index;
    int		count;
    int 	i;
    Object 	result;
    
    // determine number of non-zero attributes
    count = 0;
    for (i = 0; i < instance.numAttributes(); i++) {
      if (i == instance.classIndex())
	continue;
      if (instance.value(i) != 0)
	count++;
    }

    // fill array
    result = Array.newInstance(Class.forName(CLASS_SVMNODE), count);
    index  = 0;
    for (i = 0; i < instance.numAttributes(); i++) {
      if (i == instance.classIndex())
	continue;
      if (instance.value(i) == 0)
	continue;

      Array.set(result, index, Class.forName(CLASS_SVMNODE).newInstance());
      setField(Array.get(result, index), "index", new Integer(i + 1));
      setField(Array.get(result, index), "value", new Double(instance.value(i)));
      index++;
    }
    
    return result;
  }
  
  /**
   * Computes the distribution for a given instance. 
   * In case of 1-class classification, 1 is returned at index 0 if libsvm 
   * returns 1 and NaN (= missing) if libsvm returns -1.
   *
   * @param instance 		the instance for which distribution is computed
   * @return 			the distribution
   * @throws Exception 		if the distribution can't be computed successfully
   */
  public double[] distributionForInstance (Instance instance) throws Exception {	
    int[] labels = new int[instance.numClasses()];
    double[] prob_estimates = null;

    if (m_ProbabilityEstimates) {
      invokeMethod(
	  Class.forName(CLASS_SVM).newInstance(),
	  "svm_get_labels",
	  new Class[]{
	    Class.forName(CLASS_SVMMODEL), 
	    Array.newInstance(Integer.TYPE, instance.numClasses()).getClass()},
	    new Object[]{
	    m_Model, 
	    labels});

      prob_estimates = new double[instance.numClasses()];
    }

    if (m_Filter != null) {
      m_Filter.input(instance);
      m_Filter.batchFinished();
      instance = m_Filter.output();
    }

    Object x = instanceToArray(instance);
    double v;
    double[] result = new double[instance.numClasses()];
    if (    m_ProbabilityEstimates 
	 && ((m_SVMType == SVMTYPE_C_SVC) || (m_SVMType == SVMTYPE_NU_SVC)) ) {
      v = ((Double) invokeMethod(
          Class.forName(CLASS_SVM).newInstance(),
          "svm_predict_probability",
          new Class[]{
            Class.forName(CLASS_SVMMODEL), 
            Array.newInstance(Class.forName(CLASS_SVMNODE), Array.getLength(x)).getClass(),
            Array.newInstance(Double.TYPE, prob_estimates.length).getClass()},
          new Object[]{
            m_Model, 
            x,
            prob_estimates})).doubleValue();

      // Return order of probabilities to canonical weka attribute order
      for (int k = 0; k < prob_estimates.length; k++) {
        result[labels[k]] = prob_estimates[k];
      }
    }
    else {
      v = ((Double) invokeMethod(
          Class.forName(CLASS_SVM).newInstance(),
          "svm_predict",
          new Class[]{
            Class.forName(CLASS_SVMMODEL), 
            Array.newInstance(Class.forName(CLASS_SVMNODE), Array.getLength(x)).getClass()},
          new Object[]{
            m_Model, 
            x})).doubleValue();
      
      if (instance.classAttribute().isNominal()) {
	if (m_SVMType == SVMTYPE_ONE_CLASS_SVM) {
	  if (v > 0)
	    result[0] = 1;
	  else
	    result[0] = Double.NaN;  // outlier
	}
	else {
	  result[(int) v] = 1;
	}
      }
      else {
	result[0] = v;
      }
    }

    return result;                
  }

  /**
   * Returns default capabilities of the classifier.
   *
   * @return      the capabilities of this classifier
   */
  public Capabilities getCapabilities() {
    Capabilities result = super.getCapabilities();

    // attributes
    result.enable(Capability.NOMINAL_ATTRIBUTES);
    result.enable(Capability.NUMERIC_ATTRIBUTES);
    result.enable(Capability.DATE_ATTRIBUTES);

    // class
    result.enableDependency(Capability.UNARY_CLASS);
    result.enableDependency(Capability.NOMINAL_CLASS);
    result.enableDependency(Capability.NUMERIC_CLASS);
    result.enableDependency(Capability.DATE_CLASS);

    switch (m_SVMType) {
      case SVMTYPE_C_SVC:
      case SVMTYPE_NU_SVC:
	result.enable(Capability.NOMINAL_CLASS);
	break;
	
      case SVMTYPE_ONE_CLASS_SVM:
	result.enable(Capability.UNARY_CLASS);
	break;
	
      case SVMTYPE_EPSILON_SVR:
      case SVMTYPE_NU_SVR:
	result.enable(Capability.NUMERIC_CLASS);
	result.enable(Capability.DATE_CLASS);
	break;
	
      default:
	throw new IllegalArgumentException("SVMType " + m_SVMType + " is not supported!");
    }
    result.enable(Capability.MISSING_CLASS_VALUES);
    
    return result;
  }
  
  /**
   * builds the classifier
   * 
   * @param insts       the training instances
   * @throws Exception  if libsvm classes not in classpath or libsvm
   *                    encountered a problem
   */
  public void buildClassifier(Instances insts) throws Exception {
    
    if (!isPresent())
      throw new Exception("libsvm classes not in CLASSPATH!");

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

    // remove instances with missing class
    insts = new Instances(insts);
    insts.deleteWithMissingClass();
    
    if (getNormalize()) {
      m_Filter = new Normalize();
      m_Filter.setInputFormat(insts);
      insts = Filter.useFilter(insts, m_Filter);
    }
    
    Vector vy = new Vector();
    Vector vx = new Vector();
    int max_index = 0;
    
    for (int d = 0; d < insts.numInstances(); d++) {
      Instance inst = insts.instance(d);
      Object x = instanceToArray(inst);
      int m = Array.getLength(x);
      if (m > 0)
        max_index = Math.max(max_index, ((Integer) getField(Array.get(x, m - 1), "index")).intValue());
      vx.addElement(x);
      vy.addElement(new Double(inst.classValue()));
    }
    
    // calculate actual gamma
    if (getGamma() == 0)
      m_GammaActual = 1.0 / max_index;
    else
      m_GammaActual = m_Gamma;

    // check parameter
    String error_msg = (String) invokeMethod(
        Class.forName(CLASS_SVM).newInstance(), 
        "svm_check_parameter", 
        new Class[]{
          Class.forName(CLASS_SVMPROBLEM), 
          Class.forName(CLASS_SVMPARAMETER)},
        new Object[]{
          getProblem(vx, vy), 
          getParameters()});
    
    if (error_msg != null)
      throw new Exception("Error: " + error_msg);
    
    // train model
    m_Model = invokeMethod(
        Class.forName(CLASS_SVM).newInstance(), 
        "svm_train", 
        new Class[]{
          Class.forName(CLASS_SVMPROBLEM), 
          Class.forName(CLASS_SVMPARAMETER)},
        new Object[]{
          getProblem(vx, vy), 
          getParameters()});
  }
  
  /**
   * returns a string representation
   * 
   * @return            a string representation
   */
  public String toString() {
    return "LibSVM wrapper, original code by Yasser EL-Manzalawy (= WLSVM)";
  }
  
  /**
   * Main method for testing this class.
   * 
   * @param args the options
   */
  public static void main(String[] args) {
    runClassifier(new LibSVM(), args);
  }
}