Java Remove.setAttributeIndicesArray方法代码示例

import weka.filters.unsupervised.attribute.Remove; //导入方法依赖的package包/类
private Instances removeIgnoreCols(Instances inst) {

    // If the user is doing classes to clusters evaluation and
    // they have opted to ignore the class, then unselect the class in
    // the ignore list
    if (m_ClassesToClustersBut.isSelected()) {
      int classIndex = m_ClassCombo.getSelectedIndex();
      if (m_ignoreKeyList.isSelectedIndex(classIndex)) {
        m_ignoreKeyList.removeSelectionInterval(classIndex, classIndex);
      }
    }
    int[] selected = m_ignoreKeyList.getSelectedIndices();
    Remove af = new Remove();
    Instances retI = null;

    try {
      af.setAttributeIndicesArray(selected);
      af.setInvertSelection(false);
      af.setInputFormat(inst);
      retI = Filter.useFilter(inst, af);
    } catch (Exception e) {
      e.printStackTrace();
    }

    return retI;
  }
 

import weka.filters.unsupervised.attribute.Remove; //导入方法依赖的package包/类
/**
 * Sets up a Remove filter to remove attributes that
 * are to be ignored by the clusterer. setHeader must
 * be called before this method.
 *
 * @param attsToIgnore any attributes to ignore during the scoring process
 */
public void setAttributesToIgnore(int[] attsToIgnore) throws Exception {
  Instances headerI = getHeader();
  m_ignoredAtts = new Remove();
  m_ignoredAtts.setAttributeIndicesArray(attsToIgnore);
  m_ignoredAtts.setInvertSelection(false);
  m_ignoredAtts.setInputFormat(headerI);

  StringBuffer temp = new StringBuffer();
  temp.append("Attributes ignored by clusterer:\n\n");
  for (int i = 0; i < attsToIgnore.length; i++) {
    temp.append(headerI.attribute(attsToIgnore[i]).name() + "\n");
  }
  temp.append("\n\n");
  m_ignoredString = temp.toString();
}
 

import weka.filters.unsupervised.attribute.Remove; //导入方法依赖的package包/类
private Classifier createClassifier() throws Exception {
   	Classifier baseClassifier = Classifier.forName(getAlgorithm(), getClassifierOptions());
   	FilteredClassifier result = new FilteredClassifier();
   	result.setClassifier(baseClassifier);
   	Remove filter = new Remove();
   	filter.setAttributeIndicesArray(new int[] { 0 });
   	result.setFilter(filter);
   	return result;
}
 

import weka.filters.unsupervised.attribute.Remove; //导入方法依赖的package包/类
/**
 * Build a linear model for this node using those attributes specified in
 * indices.
 * 
 * @param indices an array of attribute indices to include in the linear model
 * @throws Exception if something goes wrong
 */
private void buildLinearModel(int[] indices) throws Exception {
  // copy the training instances and remove all but the tested
  // attributes
  Instances reducedInst = new Instances(m_instances);
  Remove attributeFilter = new Remove();

  attributeFilter.setInvertSelection(true);
  attributeFilter.setAttributeIndicesArray(indices);
  attributeFilter.setInputFormat(reducedInst);

  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];
    }
  }
  m_nodeModel = new PreConstructedLinearModel(coeffs,
    lmCoeffs[lmCoeffs.length - 1]);
  m_nodeModel.buildClassifier(m_instances);
}
 

import weka.filters.unsupervised.attribute.Remove; //导入方法依赖的package包/类
/**
  * Build a linear model for this node using those attributes
  * specified in indices.
  *
  * @param indices an array of attribute indices to include in the linear
  * model
  * @throws Exception if something goes wrong
  */
 private void buildLinearModel(int [] indices) throws Exception {
   // copy the training instances and remove all but the tested
   // attributes
   Instances reducedInst = new Instances(m_instances);
   Remove attributeFilter = new Remove();
   
   attributeFilter.setInvertSelection(true);
   attributeFilter.setAttributeIndicesArray(indices);
   attributeFilter.setInputFormat(reducedInst);

   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];
     }
   }
   m_nodeModel = new PreConstructedLinearModel(coeffs, lmCoeffs[lmCoeffs.length - 1]);
   m_nodeModel.buildClassifier(m_instances);
 }
 

import weka.filters.unsupervised.attribute.Remove; //导入方法依赖的package包/类
@Override
public void buildClassifier(Instances data) throws Exception {
  	testCapabilities(data);

	int c = data.classIndex();

	if(getDebug()) System.out.print("-: Creating "+c+" models ("+m_Classifier.getClass().getName()+"): ");
	m_MultiClassifiers = AbstractClassifier.makeCopies(m_Classifier,c);

	Instances sub_data = null;

	for(int i = 0; i < c; i++) {

		int indices[][] = new int[c][c - 1];
		for(int j = 0, k = 0; j < c; j++) {
			if(j != i) {
				indices[i][k++] = j;
			}
		}

		//Select only class attribute 'i'
		Remove FilterRemove = new Remove();
		FilterRemove.setAttributeIndicesArray(indices[i]);
		FilterRemove.setInputFormat(data);
		FilterRemove.setInvertSelection(true);
		sub_data = Filter.useFilter(data, FilterRemove);
		sub_data.setClassIndex(0);
		/* BEGIN downsample for this link */
		sub_data.randomize(m_Random);
		int numToRemove = sub_data.numInstances() - (int)Math.round(sub_data.numInstances() * m_DownSampleRatio);
		for(int m = 0, removed = 0; m < sub_data.numInstances(); m++) {
			if (sub_data.instance(m).classValue() <= 0.0) {
				sub_data.instance(m).setClassMissing();
				if (++removed >= numToRemove)
					break;
			}
		}
		sub_data.deleteWithMissingClass();
		/* END downsample for this link */


		//Build the classifier for that class
		m_MultiClassifiers[i].buildClassifier(sub_data);
		if(getDebug()) System.out.print(" " + (i+1));

	}

	if(getDebug()) System.out.println(" :-");

	m_InstancesTemplate = new Instances(sub_data, 0);

}
 

import weka.filters.unsupervised.attribute.Remove; //导入方法依赖的package包/类
/**
  * Evaluates a subset of attributes
  *
  * @param subset a bitset representing the attribute subset to be 
  * evaluated 
  * @return the error rate
  * @throws Exception if the subset could not be evaluated
  */
 public double evaluateSubset (BitSet subset)
   throws Exception {
   int i,j;
   double errorRate = 0;
   int numAttributes = 0;
   Instances trainCopy=null;
   Instances testCopy=null;

   Remove delTransform = new Remove();
   delTransform.setInvertSelection(true);
   // copy the training instances
   trainCopy = new Instances(m_trainingInstances);
   
   if (!m_useTraining) {
     if (m_holdOutInstances == null) {
throw new Exception("Must specify a set of hold out/test instances "
		    +"with -H");
     } 
     // copy the test instances
     testCopy = new Instances(m_holdOutInstances);
   }
   
   // count attributes set in the BitSet
   for (i = 0; i < m_numAttribs; i++) {
     if (subset.get(i)) {
       numAttributes++;
     }
   }
   
   // set up an array of attribute indexes for the filter (+1 for the class)
   int[] featArray = new int[numAttributes + 1];
   
   for (i = 0, j = 0; i < m_numAttribs; i++) {
     if (subset.get(i)) {
       featArray[j++] = i;
     }
   }
   
   featArray[j] = m_classIndex;
   delTransform.setAttributeIndicesArray(featArray);
   delTransform.setInputFormat(trainCopy);
   trainCopy = Filter.useFilter(trainCopy, delTransform);
   if (!m_useTraining) {
     testCopy = Filter.useFilter(testCopy, delTransform);
   }

   // build the classifier
   m_Classifier.buildClassifier(trainCopy);

   m_Evaluation = new Evaluation(trainCopy);
   if (!m_useTraining) {
     m_Evaluation.evaluateModel(m_Classifier, testCopy);
   } else {
     m_Evaluation.evaluateModel(m_Classifier, trainCopy);
   }

   if (m_trainingInstances.classAttribute().isNominal()) {
     errorRate = m_Evaluation.errorRate();
   } else {
     errorRate = m_Evaluation.meanAbsoluteError();
   }

   m_Evaluation = null;
   // return the negative of the error rate as search methods  need to
   // maximize something
   return -errorRate;
 }
 

import weka.filters.unsupervised.attribute.Remove; //导入方法依赖的package包/类
/**
 * Evaluates a subset of attributes
 *
 * @param subset a bitset representing the attribute subset to be 
 * evaluated 
 * @return the error rate
 * @throws Exception if the subset could not be evaluated
 */
public double evaluateSubset (BitSet subset)
  throws Exception {
  double errorRate = 0;
  double[] repError = new double[5];
  int numAttributes = 0;
  int i, j;
  Random Rnd = new Random(m_seed);
  Remove delTransform = new Remove();
  delTransform.setInvertSelection(true);
  // copy the instances
  Instances trainCopy = new Instances(m_trainInstances);

  // count attributes set in the BitSet
  for (i = 0; i < m_numAttribs; i++) {
    if (subset.get(i)) {
      numAttributes++;
    }
  }

  // set up an array of attribute indexes for the filter (+1 for the class)
  int[] featArray = new int[numAttributes + 1];

  for (i = 0, j = 0; i < m_numAttribs; i++) {
    if (subset.get(i)) {
      featArray[j++] = i;
    }
  }

  featArray[j] = m_classIndex;
  delTransform.setAttributeIndicesArray(featArray);
  delTransform.setInputFormat(trainCopy);
  trainCopy = Filter.useFilter(trainCopy, delTransform);

  // max of 5 repititions ofcross validation
  for (i = 0; i < 5; i++) {
    m_Evaluation = new Evaluation(trainCopy);
    m_Evaluation.crossValidateModel(m_BaseClassifier, trainCopy, m_folds, Rnd);
    repError[i] = m_Evaluation.errorRate();

    // check on the standard deviation
    if (!repeat(repError, i + 1)) {
      i++;
      break;
    }
  }

  for (j = 0; j < i; j++) {
    errorRate += repError[j];
  }

  errorRate /= (double)i;
  m_Evaluation = null;
  return  m_trainInstances.classAttribute().isNumeric() ? -errorRate : 1.0 - errorRate;
}
 

import weka.filters.unsupervised.attribute.Remove; //导入方法依赖的package包/类
/**
  * Gets the results for the supplied train and test datasets.
  *
  * @param train the training Instances.
  * @param test the testing Instances.
  * @return the results stored in an array. The objects stored in
  * the array may be Strings, Doubles, or null (for the missing value).
  * @exception Exception if a problem occurs while getting the results
  */
 public Object [] getResult(Instances train, Instances test) 
   throws Exception {
   
   if (m_clusterer == null) {
     throw new Exception("No clusterer has been specified");
   }
   int addm = (m_additionalMeasures != null) 
     ? m_additionalMeasures.length 
     : 0;
   int overall_length = RESULT_SIZE+addm;

   if (m_removeClassColumn && train.classIndex() != -1) {
     // remove the class column from the training and testing data
     Remove r = new Remove();
     r.setAttributeIndicesArray(new int [] {train.classIndex()});
     r.setInvertSelection(false);
     r.setInputFormat(train);
     train = Filter.useFilter(train, r);
     
     test = Filter.useFilter(test, r);
   }
   train.setClassIndex(-1);
   test.setClassIndex(-1);
     

   ClusterEvaluation eval = new ClusterEvaluation();

   Object [] result = new Object[overall_length];
   long trainTimeStart = System.currentTimeMillis();
   m_clusterer.buildClusterer(train);
   double numClusters = m_clusterer.numberOfClusters();
   eval.setClusterer(m_clusterer);
   long trainTimeElapsed = System.currentTimeMillis() - trainTimeStart;
   long testTimeStart = System.currentTimeMillis();
   eval.evaluateClusterer(test);
   long testTimeElapsed = System.currentTimeMillis() - testTimeStart;
   //    m_result = eval.toSummaryString();

   // The results stored are all per instance -- can be multiplied by the
   // number of instances to get absolute numbers
   int current = 0;
   result[current++] = new Double(train.numInstances());
   result[current++] = new Double(test.numInstances());

   result[current++] = new Double(eval.getLogLikelihood());
   result[current++] = new Double(numClusters);
   
   // Timing stats
   result[current++] = new Double(trainTimeElapsed / 1000.0);
   result[current++] = new Double(testTimeElapsed / 1000.0);
   
   for (int i=0;i<addm;i++) {
     if (m_doesProduce[i]) {
try {
  double dv = ((AdditionalMeasureProducer)m_clusterer).
    getMeasure(m_additionalMeasures[i]);
  Double value = new Double(dv);
  
  result[current++] = value;
} catch (Exception ex) {
  System.err.println(ex);
}
     } else {
result[current++] = null;
     }
   }
   
   if (current != overall_length) {
     throw new Error("Results didn't fit RESULT_SIZE");
   }
   return result;
 }