Java LinearRegression.buildClassifier方法代码示例

import weka.classifiers.functions.LinearRegression; //导入方法依赖的package包/类
public void buildRegression(){	
	lReg = new LinearRegression();
	try {
		lReg.buildClassifier(cpu);
	} catch (Exception e) {
	} 
	System.out.println(lReg);
}
 

import weka.classifiers.functions.LinearRegression; //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.classifiers.functions.LinearRegression; //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.classifiers.functions.LinearRegression; //导入方法依赖的package包/类
/**
    * This function gets called to set the node to use a linear regression
    * and attribute filter.
    * @throws Exception If can't set a default linear egression model.
    */
   private void setLinear() throws Exception {
     //then set default behaviour for node.
     //set linear regression combined with attribute filter
     
     //find the attributes used for splitting.
     boolean[] attributeList = new boolean[m_training.numAttributes()];
     for (int noa = 0; noa < m_training.numAttributes(); noa++) {
attributeList[noa] = false;
     }
     
     TreeClass temp = this;
     attributeList[m_training.classIndex()] = true;
     while (temp != null) {
attributeList[temp.m_attrib1] = true;
attributeList[temp.m_attrib2] = true;
temp = temp.m_parent;
     }
     int classind = 0;
     
     
     //find the new class index
     for (int noa = 0; noa < m_training.classIndex(); noa++) {
if (attributeList[noa]) {
  classind++;
}
     }
     //count how many attribs were used
     int count = 0;
     for (int noa = 0; noa < m_training.numAttributes(); noa++) {
if (attributeList[noa]) {
  count++;
}
     }
     
     //fill an int array with the numbers of those attribs
     int[] attributeList2 = new int[count];
     count = 0;
     for (int noa = 0; noa < m_training.numAttributes(); noa++) {
if (attributeList[noa]) {
  attributeList2[count] = noa;
  count++;
}
     }
     
     m_filter = new Remove();
     ((Remove)m_filter).setInvertSelection(true);
     ((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);
   }