Java Code Examples for weka.core.SelectedTag#getTags()

/** 
 * Sets the distribution to use for calculating the random matrix
 * 
 * @param newDstr the distribution to use
 */
public void setDistribution(SelectedTag newDstr) {

    if (newDstr.getTags() == TAGS_DSTRS_TYPE) {
 m_distribution = newDstr.getSelectedTag().getID();
    }
}
 

/**
 * Sets how the training data will be transformed. Should be one of
 * FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.
 *
 * @param newType the new filtering mode
 */
public void setFilterType(SelectedTag newType) {

  if (newType.getTags() == TAGS_FILTER) {
    m_filterType = newType.getSelectedTag().getID();
  }
}
 

/**
 * Sets the attribute type to be deleted by the filter.
 *
 * @param type a TAGS_ATTRIBUTETYPE of the new type the filter should delete
 */
public void setAttributeType(SelectedTag type) {
  
  if (type.getTags() == TAGS_ATTRIBUTETYPE) {
    m_attTypeToDelete = type.getSelectedTag().getID();
  }
}
 

/**
 * Sets the confidence range correction mode used. Will be one of
 * RANGE_NONE, or RANGE_BOUNDS
 *
 * @param newMethod the new correciton mode.
 */
public void setRangeCorrection(SelectedTag newMethod) {
  
  if (newMethod.getTags() == TAGS_RANGE) {
    m_RangeMode = newMethod.getSelectedTag().getID();
  }
}
 

/**
 * Sets the evaluation mode used. Will be one of
 * EVAL_TRAINING, EVAL_TUNED_SPLIT, or EVAL_CROSS_VALIDATION
 *
 * @param newMethod the new evaluation mode.
 */
public void setEvaluationMode(SelectedTag newMethod) {
  
  if (newMethod.getTags() == TAGS_EVAL) {
    m_EvalMode = newMethod.getSelectedTag().getID();
  }
}
 

/**
 * Set the Functional Tree type.
 *
 * @param c Value corresponding to tree type.
 */
public void setModelType(SelectedTag newMethod){
  if (newMethod.getTags() == TAGS_MODEL) {
    int c = newMethod.getSelectedTag().getID();
    if (c==0 || c==1 || c==2) {
      m_modelType = c;
    } else  {
      throw new IllegalArgumentException("Wrong model type, -F value should be: 0, for FT, 1, " +
                                         "for FTLeaves, and 2, for FTInner "); 
    }
  }
}
 

/**
 * Sets the source location of the cost matrix. Values other than
 * MATRIX_ON_DEMAND or MATRIX_SUPPLIED will be ignored.
 *
 * @param newMethod the cost matrix location method.
 */
public void setCostMatrixSource(SelectedTag newMethod) {
  
  if (newMethod.getTags() == TAGS_MATRIX_SOURCE) {
    m_MatrixSource = newMethod.getSelectedTag().getID();
  }
}
 

/**
  * Sets the sort type to be used.
  *
  * @param type 	the type of sorting
  */
 public void setSortType(SelectedTag type) {
   if (type.getTags() == TAGS_SORTTYPE) {
     m_SortType = type.getSelectedTag().getID();
     
     if (m_SortType == SORT_CASESENSITIVE)
m_Comparator = new CaseSensitiveComparator();
     else if (m_SortType == SORT_CASEINSENSITIVE)
m_Comparator = new CaseInsensitiveComparator();
     else
throw new IllegalStateException("Unhandled sort type '" + type + "'!");
   }
 }
 

/**
 * Sets how the training data will be transformed. Should be one of
 * FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.
 *
 * @param newType the new filtering mode
 */
public void setFilterType(SelectedTag newType) {

  if (newType.getTags() == TAGS_FILTER) {
    m_filterType = newType.getSelectedTag().getID();
  }
}
 

/**
 * Sets the type of algorithm to use 
 *
 * @param value 	the algorithm type
 */
public void setAlgorithm(SelectedTag value) {
  if (value.getTags() == TAGS_ALGORITHM) {
    m_Algorithm = value.getSelectedTag().getID();
  }
}
 

/**
 * Sets the algorithm type.
 *
 * @param newType the new algorithm type
 */
public void setAlgorithmType(SelectedTag newType) {
  if (newType.getTags() == TAGS_ALGORITHMTYPE) {
    m_AlgorithmType = newType.getSelectedTag().getID();
  }
}
 

/**
 * Sets the type of preprocessing to use 
 *
 * @param value 	the preprocessing type
 */
public void setPreprocessing(SelectedTag value) {
  if (value.getTags() == TAGS_PREPROCESSING) {
    m_Preprocessing = value.getSelectedTag().getID();
  }
}
 

/** 
 * set measure used for determining threshold
 * 
 * @param newMeasure Tag representing measure to be used
 */
public void setMeasure(SelectedTag newMeasure) {
  if (newMeasure.getTags() == TAGS_MEASURE) {
    m_nMeasure = newMeasure.getSelectedTag().getID();
  }
}
 

/**
 * Sets the type of attribute to generate. 
 *
 * @param value 	the attribute type
 */
public void setAttributeType(SelectedTag value) {
  if (value.getTags() == TAGS_TYPE) {
    m_AttributeType = value.getSelectedTag().getID();
  }
}
 

public void setLinkType(SelectedTag newLinkType) {
  if (newLinkType.getTags() == TAGS_LINK_TYPE) {
    m_nLinkType = newLinkType.getSelectedTag().getID();
  }
}
 

/**
 * Set the type
 *
 * @param t
 *            the Linear Forward Selection type
 */
public void setType(SelectedTag t) {
  if (t.getTags() == TAGS_TYPE) {
    m_linearSelectionType = t.getSelectedTag().getID();
  }
}
 

/**
 * Sets the pattern type.
 *
 * @param value new pattern type 
 */
public void setPattern(SelectedTag value) {
  if (value.getTags() == TAGS_PATTERN)
    m_Pattern = value.getSelectedTag().getID();
}
 

/**
 * Sets how the training data will be transformed. Should be one of
 * FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.
 *
 * @param newType 	the new filtering mode
 */
public void setFilterType(SelectedTag newType) {
  if (newType.getTags() == TAGS_FILTER) {
    m_filterType = newType.getSelectedTag().getID();
  }
}
 

/**
 * Set the type
 *
 * @param t
 *            the Linear Forward Selection type
 */
public void setType(SelectedTag t) {
  if (t.getTags() == TAGS_TYPE) {
    m_linearSelectionType = t.getSelectedTag().getID();
  }
}
 

/**
 * Sets type of SVM (default SVMTYPE_L2)
 *
 * @param value       the type of the SVM
 */
public void setSVMType(SelectedTag value) {
  if (value.getTags() == TAGS_SVMTYPE)
    m_SVMType = value.getSelectedTag().getID();
}