示例#1
0
  /**
   * wrap up various variables to save memeory and do some housekeeping after optimization has
   * finished.
   *
   * @throws Exception if something goes wrong
   */
  protected void wrapUp() throws Exception {
    m_target = null;

    m_nEvals = m_kernel.numEvals();
    m_nCacheHits = m_kernel.numCacheHits();

    if ((m_SVM.getKernel() instanceof PolyKernel)
        && ((PolyKernel) m_SVM.getKernel()).getExponent() == 1.0) {
      // convert alpha's to weights
      double[] weights = new double[m_data.numAttributes()];
      for (int k = m_supportVectors.getNext(-1); k != -1; k = m_supportVectors.getNext(k)) {
        for (int j = 0; j < weights.length; j++) {
          if (j != m_classIndex) {
            weights[j] += (m_alpha[k] - m_alphaStar[k]) * m_data.instance(k).value(j);
          }
        }
      }
      m_weights = weights;

      // release memory
      m_alpha = null;
      m_alphaStar = null;
      m_kernel = null;
    }
    m_bModelBuilt = true;
  }
示例#2
0
  /**
   * initializes the algorithm
   *
   * @param data the data to work with
   * @throws Exception if m_SVM is null
   */
  protected void init(Instances data) throws Exception {
    if (m_SVM == null) {
      throw new Exception("SVM not initialized in optimizer. Use RegOptimizer.setSVMReg()");
    }
    m_C = m_SVM.getC();
    m_data = data;
    m_classIndex = data.classIndex();
    m_nInstances = data.numInstances();

    // Initialize kernel
    m_kernel = Kernel.makeCopy(m_SVM.getKernel());
    m_kernel.buildKernel(data);

    // init m_target
    m_target = new double[m_nInstances];
    for (int i = 0; i < m_nInstances; i++) {
      m_target[i] = data.instance(i).classValue();
    }

    m_random = new Random(m_nSeed);

    //		initialize alpha and alpha* array to all zero
    m_alpha = new double[m_target.length];
    m_alphaStar = new double[m_target.length];

    m_supportVectors = new SMOset(m_nInstances);

    m_b = 0.0;
    m_nEvals = 0;
    m_nCacheHits = -1;
  }