Package weka.classifiers.functions.supportVector

Examples of weka.classifiers.functions.supportVector.SMOset


  if (m_KernelIsLinear) {
    m_sparseWeights = new double[0];
    m_sparseIndices = new int[0];
    m_class = null;
  } else {
    m_supportVectors = new SMOset(0);
    m_alpha = new double[0];
    m_class = new double[0];
  }

  // Fit sigmoid if requested
  if (fitLogistic) {
    fitLogistic(insts, cl1, cl2, numFolds, new Random(randomSeed));
  }
  return;
      }
     
      // Set the reference to the data
      m_data = insts;

      // If machine is linear, reserve space for weights
      if (m_KernelIsLinear) {
  m_weights = new double[m_data.numAttributes()];
      } else {
  m_weights = null;
      }
     
      // Initialize alpha array to zero
      m_alpha = new double[m_data.numInstances()];
     
      // Initialize sets
      m_supportVectors = new SMOset(m_data.numInstances());
      m_I0 = new SMOset(m_data.numInstances());
      m_I1 = new SMOset(m_data.numInstances());
      m_I2 = new SMOset(m_data.numInstances());
      m_I3 = new SMOset(m_data.numInstances());
      m_I4 = new SMOset(m_data.numInstances());

      // Clean out some instance variables
      m_sparseWeights = null;
      m_sparseIndices = null;
     
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          m_b = 1;
        } else {
          m_class = null;
          return;
        }
        m_supportVectors = new SMOset(0);
        m_alpha = new double[0];
        m_class = new double[0];

        // Fit sigmoid if requested
        if (fitLogistic) {
          fitLogistic(insts, cl1, cl2, numFolds, new Random(randomSeed));
        }
        return;
      }

      // Set the reference to the data
      m_data = insts;
      m_weights = null;

      // Initialize alpha array to zero
      m_alpha = new double[m_data.numInstances()];

      // Initialize sets
      m_supportVectors = new SMOset(m_data.numInstances());
      m_I0 = new SMOset(m_data.numInstances());
      m_I1 = new SMOset(m_data.numInstances());
      m_I2 = new SMOset(m_data.numInstances());
      m_I3 = new SMOset(m_data.numInstances());
      m_I4 = new SMOset(m_data.numInstances());

      // Clean out some instance variables
      m_sparseWeights = null;
      m_sparseIndices = null;
View Full Code Here

  if (m_KernelIsLinear) {
    m_sparseWeights = new double[0];
    m_sparseIndices = new int[0];
    m_class = null;
  } else {
    m_supportVectors = new SMOset(0);
    m_alpha = new double[0];
    m_class = new double[0];
  }

  // Fit sigmoid if requested
  if (fitLogistic) {
    fitLogistic(insts, cl1, cl2, numFolds, new Random(randomSeed));
  }
  return;
      }
     
      // Set the reference to the data
      m_data = insts;

      // If machine is linear, reserve space for weights
      if (m_KernelIsLinear) {
  m_weights = new double[m_data.numAttributes()];
      } else {
  m_weights = null;
      }
     
      // Initialize alpha array to zero
      m_alpha = new double[m_data.numInstances()];
     
      // Initialize sets
      m_supportVectors = new SMOset(m_data.numInstances());
      m_I0 = new SMOset(m_data.numInstances());
      m_I1 = new SMOset(m_data.numInstances());
      m_I2 = new SMOset(m_data.numInstances());
      m_I3 = new SMOset(m_data.numInstances());
      m_I4 = new SMOset(m_data.numInstances());

      // Clean out some instance variables
      m_sparseWeights = null;
      m_sparseIndices = null;
     
View Full Code Here

    // Initialize fcache
    m_fcache = new double[m_data.numInstances()];

    // Initialize sets
    m_I0 = new SMOset(m_data.numInstances());
    m_I1 = new SMOset(m_data.numInstances());
    m_I2 = new SMOset(m_data.numInstances());
    m_I3 = new SMOset(m_data.numInstances());


    /* MAIN ROUTINE FOR MODIFICATION 1 */
    // Follows the specification of the first modification of Shevade's paper
   
View Full Code Here

          m_b = 1;
        } else {
          m_class = null;
          return;
        }
        m_supportVectors = new SMOset(0);
        m_alpha = new double[0];
        m_class = new double[0];

        // Fit sigmoid if requested
        if (fitLogistic) {
          fitLogistic(insts, cl1, cl2, numFolds, new Random(randomSeed));
        }
        return;
      }

      // Set the reference to the data
      m_data = insts;
      m_weights = null;

      // Initialize alpha array to zero
      m_alpha = new double[m_data.numInstances()];

      // Initialize sets
      m_supportVectors = new SMOset(m_data.numInstances());
      m_I0 = new SMOset(m_data.numInstances());
      m_I1 = new SMOset(m_data.numInstances());
      m_I2 = new SMOset(m_data.numInstances());
      m_I3 = new SMOset(m_data.numInstances());
      m_I4 = new SMOset(m_data.numInstances());

      // Clean out some instance variables
      m_sparseWeights = null;
      m_sparseIndices = null;
View Full Code Here

  if (m_KernelIsLinear) {
    m_sparseWeights = new double[0];
    m_sparseIndices = new int[0];
    m_class = null;
  } else {
    m_supportVectors = new SMOset(0);
    m_alpha = new double[0];
    m_class = new double[0];
  }

  // Fit sigmoid if requested
  if (fitLogistic) {
    fitLogistic(insts, cl1, cl2, numFolds, new Random(randomSeed));
  }
  return;
      }
     
      // Set the reference to the data
      m_data = insts;

      // If machine is linear, reserve space for weights
      if (m_KernelIsLinear) {
  m_weights = new double[m_data.numAttributes()];
      } else {
  m_weights = null;
      }
     
      // Initialize alpha array to zero
      m_alpha = new double[m_data.numInstances()];
     
      // Initialize sets
      m_supportVectors = new SMOset(m_data.numInstances());
      m_I0 = new SMOset(m_data.numInstances());
      m_I1 = new SMOset(m_data.numInstances());
      m_I2 = new SMOset(m_data.numInstances());
      m_I3 = new SMOset(m_data.numInstances());
      m_I4 = new SMOset(m_data.numInstances());

      // Clean out some instance variables
      m_sparseWeights = null;
      m_sparseIndices = null;
     
View Full Code Here

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