Examples of PLSFilter

weka.filters.supervised.attribute.PLSFilter
tatsoft.com/textbook/stpls.html} } @misc{missing_id, author = {Bent Jorgensen and Yuri Goegebeur}, booktitle = {ST02: Multivariate Data Analysis and Chemometrics}, title = {Module 7: Partial least squares regression I}, HTTP = {http://statmaster.sdu.dk/courses/ST02/module07/} } @article{Jong1993, author = {S. de Jong}, journal = {Chemometrics and Intelligent Laboratory Systems}, pages = {251-263}, title = {SIMPLS: an alternative approach to partial least squares regression}, volume = {18}, year = {1993} }
Valid options are:
```
 -D Turns on output of debugging information.
```
```
 -C <num> The number of components to compute. (default: 20)
```
```
 -U Updates the class attribute as well. (default: off)
```
```
 -M Turns replacing of missing values on. (default: off)
```
```
 -A <SIMPLS|PLS1> The algorithm to use. (default: PLS1)
```
```
 -P <none|center|standardize> The type of preprocessing that is applied to the data. (default: center)
```
@author FracPete (fracpete at waikato dot ac dot nz) @version $Revision: 5541 $

Examples of weka.filters.supervised.attribute.PLSFilter


  /** Creates a default PLSClassifier */
  public Classifier getClassifier() {
    PLSClassifier classifier = new PLSClassifier();
    
    PLSFilter filter = new PLSFilter();
    filter.setReplaceMissing(true);
    filter.setPreprocessing(new SelectedTag(PLSFilter.PREPROCESSING_CENTER, PLSFilter.TAGS_PREPROCESSING));
    filter.setNumComponents(NUM_COMPONENTS);
    filter.setAlgorithm(new SelectedTag(PLSFilter.ALGORITHM_SIMPLS, PLSFilter.TAGS_ALGORITHM));
    try {
      classifier.setFilter(filter);
    }
    catch (Exception e) {
      e.printStackTrace();

View Full Code Here

Examples of weka.filters.supervised.attribute.PLSFilter

    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();
    PLSFilter filter = new PLSFilter();
    filter.setPreprocessing(new SelectedTag(PLSFilter.PREPROCESSING_STANDARDIZE, PLSFilter.TAGS_PREPROCESSING));
    filter.setReplaceMissing(true);
    
    try {
      m_BestClassifier = Classifier.makeCopy(m_Classifier);
    }
    catch (Exception e) {

View Full Code Here

Examples of weka.filters.supervised.attribute.PLSFilter

    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();
    PLSFilter filter = new PLSFilter();
    filter.setPreprocessing(new SelectedTag(PLSFilter.PREPROCESSING_STANDARDIZE, PLSFilter.TAGS_PREPROCESSING));
    filter.setReplaceMissing(true);
    
    try {
      m_BestClassifier = Classifier.makeCopy(m_Classifier);
    }
    catch (Exception e) {

View Full Code Here

TOP

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.