Examples of weka.classifiers.functions.LinearRegression

weka.classifiers.functions.LinearRegression
Class for using linear regression for prediction. Uses the Akaike criterion for model selection, and is able to deal with weighted instances.
Valid options are:
```
 -D Produce debugging output. (default no debugging output)
```
```
 -S <number of selection method> Set the attribute selection method to use. 1 = None, 2 = Greedy. (default 0 = M5' method)
```
```
 -C Do not try to eliminate colinear attributes. 
```
```
 -R <double> Set ridge parameter (default 1.0e-8). 
```
@author Eibe Frank (eibe@cs.waikato.ac.nz) @author Len Trigg (trigg@cs.waikato.ac.nz) @version $Revision: 5928 $

   */
  public GridSearch() {
    super();
    
    // classifier
    m_Classifier = new LinearRegression();
    ((LinearRegression) m_Classifier).setAttributeSelectionMethod(new SelectedTag(LinearRegression.SELECTION_NONE, LinearRegression.TAGS_SELECTION));
    ((LinearRegression) m_Classifier).setEliminateColinearAttributes(false);
    
    // filter
    m_Filter = new PLSFilter();

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      ((Remove)m_filter).setAttributeIndicesArray(attributeList2);
      m_filter.setInputFormat(m_training);
      
      Instances temp2 = Filter.useFilter(m_training, m_filter);
      temp2.setClassIndex(classind);
      m_classObject = new LinearRegression();
      m_classObject.buildClassifier(temp2);
    }

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   * Returns default capabilities of the classifier, i.e., of LinearRegression.
   *
   * @return      the capabilities of this classifier
   */
  public Capabilities getCapabilities() {
    return new LinearRegression().getCapabilities();
  }

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    reducedInst = Filter.useFilter(reducedInst, attributeFilter);
    
    // build a linear regression for the training data using the
    // tested attributes
    LinearRegression temp = new LinearRegression();
    temp.buildClassifier(reducedInst);


    double [] lmCoeffs = temp.coefficients();
    double [] coeffs = new double [m_instances.numAttributes()];


    for (int i = 0; i < lmCoeffs.length - 1; i++) {
      if (indices[i] != m_classIndex) {
  coeffs[indices[i]] = lmCoeffs[i];

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   * Returns default capabilities of the classifier, i.e., of LinearRegression.
   *
   * @return      the capabilities of this classifier
   */
  public Capabilities getCapabilities() {
    return new LinearRegression().getCapabilities();
  }

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  {
    for (Dataset dataset : datasets)
    {
      // Set parameters
      int folds = 10;
      Classifier baseClassifier = new LinearRegression();
      
      // Set up the random number generator
        long seed = new Date().getTime();      
      Random random = new Random(seed);

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  {
    for (Dataset dataset : datasets)
    {
      // Set parameters
      int folds = 10;
      Classifier baseClassifier = new LinearRegression();
      
      // Set up the random number generator
        long seed = new Date().getTime();      
      Random random = new Random(seed);

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      ((Remove)m_filter).setAttributeIndicesArray(attributeList2);
      m_filter.setInputFormat(m_training);
      
      Instances temp2 = Filter.useFilter(m_training, m_filter);
      temp2.setClassIndex(classind);
      m_classObject = new LinearRegression();
      m_classObject.buildClassifier(temp2);
    }

View Full Code Here

   */
  public GridSearch() {
    super();
    
    // classifier
    m_Classifier = new LinearRegression();
    ((LinearRegression) m_Classifier).setAttributeSelectionMethod(new SelectedTag(LinearRegression.SELECTION_NONE, LinearRegression.TAGS_SELECTION));
    ((LinearRegression) m_Classifier).setEliminateColinearAttributes(false);
    
    // filter
    m_Filter = new PLSFilter();

View Full Code Here

   * Returns default capabilities of the classifier, i.e., of LinearRegression.
   *
   * @return      the capabilities of this classifier
   */
  public Capabilities getCapabilities() {
    return new LinearRegression().getCapabilities();
  }

View Full Code Here

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