relieffattributeeval.java

wekaUT是 university texas austin 开发的基于weka的半指导学习(semi supervised learning)的分类器

      distance += diff;
    }
    
    //    return Math.sqrt(distance / m_NumAttributesUsed);
    return distance;
  }


  /**
   * update attribute weights given an instance when the class is numeric
   *
   * @param instNum the index of the instance to use when updating weights
   */
  private void updateWeightsNumericClass (int instNum) {
    int i, j;
    double temp,temp2;
    int[] tempSorted = null;
    double[] tempDist = null;
    double distNorm = 1.0;
    int firstI, secondI;

    Instance inst = m_trainInstances.instance(instNum);
   
    // sort nearest neighbours and set up normalization variable
    if (m_weightByDistance) {
      tempDist = new double[m_stored[0]];

      for (j = 0, distNorm = 0; j < m_stored[0]; j++) {
	// copy the distances
	tempDist[j] = m_karray[0][j][0];
	// sum normalizer
	distNorm += m_weightsByRank[j];
      }

      tempSorted = Utils.sort(tempDist);
    }

    for (i = 0; i < m_stored[0]; i++) {
      // P diff prediction (class) given nearest instances
      if (m_weightByDistance) {
	temp = difference(m_classIndex, 
			  inst.value(m_classIndex),
			  m_trainInstances.
			  instance((int)m_karray[0][tempSorted[i]][1]).
			  value(m_classIndex));
	temp *= (m_weightsByRank[i]/distNorm);
      }
      else {
	temp = difference(m_classIndex, 
			  inst.value(m_classIndex), 
			  m_trainInstances.
			  instance((int)m_karray[0][i][1]).
			  value(m_classIndex));
	temp *= (1.0/(double)m_stored[0]); // equal influence
      }

      m_ndc += temp;

      Instance cmp;
      cmp = (m_weightByDistance) 
	? m_trainInstances.instance((int)m_karray[0][tempSorted[i]][1])
	: m_trainInstances.instance((int)m_karray[0][i][1]);
 
      double temp_diffP_diffA_givNearest = 
	difference(m_classIndex, inst.value(m_classIndex),
		   cmp.value(m_classIndex));
      // now the attributes
      for (int p1 = 0, p2 = 0; 
	   p1 < inst.numValues() || p2 < cmp.numValues();) {
	if (p1 >= inst.numValues()) {
	  firstI = m_trainInstances.numAttributes();
	} else {
	  firstI = inst.index(p1); 
	}
	if (p2 >= cmp.numValues()) {
	  secondI = m_trainInstances.numAttributes();
	} else {
	  secondI = cmp.index(p2);
	}
	if (firstI == m_trainInstances.classIndex()) {
	  p1++; continue;
	} 
	if (secondI == m_trainInstances.classIndex()) {
	  p2++; continue;
	} 
	temp = 0.0;
	temp2 = 0.0;
      
	if (firstI == secondI) {
	  j = firstI;
	  temp = difference(j, inst.valueSparse(p1), cmp.valueSparse(p2)); 
	  p1++;p2++;
	} else if (firstI > secondI) {
	  j = secondI;
	  temp = difference(j, 0, cmp.valueSparse(p2));
	  p2++;
	} else {
	  j = firstI;
	  temp = difference(j, inst.valueSparse(p1), 0);
	  p1++;
	} 
       
	temp2 = temp_diffP_diffA_givNearest * temp; 
	// P of different prediction and different att value given
	// nearest instances
	if (m_weightByDistance) {
	  temp2 *= (m_weightsByRank[i]/distNorm);
	}
	else {
	  temp2 *= (1.0/(double)m_stored[0]); // equal influence
	}

	m_ndcda[j] += temp2;
       
	// P of different attribute val given nearest instances
	if (m_weightByDistance) {
	  temp *= (m_weightsByRank[i]/distNorm);
	}
	else {
	  temp *= (1.0/(double)m_stored[0]); // equal influence
	}

	m_nda[j] += temp;
      }
    }
  }


  /**
   * update attribute weights given an instance when the class is discrete
   *
   * @param instNum the index of the instance to use when updating weights
   */
  private void updateWeightsDiscreteClass (int instNum) {
    int i, j, k;
    int cl;
    double cc = m_numInstances;
    double temp, temp_diff, w_norm = 1.0;
    double[] tempDistClass;
    int[] tempSortedClass = null;
    double distNormClass = 1.0;
    double[] tempDistAtt;
    int[][] tempSortedAtt = null;
    double[] distNormAtt = null;
    int firstI, secondI;

    // store the indexes (sparse instances) of non-zero elements
    Instance inst = m_trainInstances.instance(instNum);

    // get the class of this instance
    cl = (int)m_trainInstances.instance(instNum).value(m_classIndex);

    // sort nearest neighbours and set up normalization variables
    if (m_weightByDistance) {
      // do class (hits) first
      // sort the distances
      tempDistClass = new double[m_stored[cl]];

      for (j = 0, distNormClass = 0; j < m_stored[cl]; j++) {
	// copy the distances
	tempDistClass[j] = m_karray[cl][j][0];
	// sum normalizer
	distNormClass += m_weightsByRank[j];
      }

      tempSortedClass = Utils.sort(tempDistClass);
      // do misses (other classes)
      tempSortedAtt = new int[m_numClasses][1];
      distNormAtt = new double[m_numClasses];

      for (k = 0; k < m_numClasses; k++) {
	if (k != cl) // already done cl
	  {
	    // sort the distances
	    tempDistAtt = new double[m_stored[k]];

	    for (j = 0, distNormAtt[k] = 0; j < m_stored[k]; j++) {
	      // copy the distances
	      tempDistAtt[j] = m_karray[k][j][0];
	      // sum normalizer
	      distNormAtt[k] += m_weightsByRank[j];
	    }

	    tempSortedAtt[k] = Utils.sort(tempDistAtt);
	  }
      }
    }

    if (m_numClasses > 2) {
      // the amount of probability space left after removing the
      // probability of this instance's class value
      w_norm = (1.0 - m_classProbs[cl]);
    }
    
    // do the k nearest hits of the same class
    for (j = 0, temp_diff = 0.0; j < m_stored[cl]; j++) {
      Instance cmp;
      cmp = (m_weightByDistance) 
	? m_trainInstances.
	instance((int)m_karray[cl][tempSortedClass[j]][1])
	: m_trainInstances.instance((int)m_karray[cl][j][1]);

      for (int p1 = 0, p2 = 0; 
	   p1 < inst.numValues() || p2 < cmp.numValues();) {
	if (p1 >= inst.numValues()) {
	  firstI = m_trainInstances.numAttributes();
	} else {
	  firstI = inst.index(p1); 
	}
	if (p2 >= cmp.numValues()) {
	  secondI = m_trainInstances.numAttributes();
	} else {
	  secondI = cmp.index(p2);
	}
	if (firstI == m_trainInstances.classIndex()) {
	  p1++; continue;
	} 
	if (secondI == m_trainInstances.classIndex()) {
	  p2++; continue;
	} 
	if (firstI == secondI) {
	  i = firstI;
	  temp_diff = difference(i, inst.valueSparse(p1), 
				 cmp.valueSparse(p2)); 
	  p1++;p2++;
	} else if (firstI > secondI) {
	  i = secondI;
	  temp_diff = difference(i, 0, cmp.valueSparse(p2));
	  p2++;
	} else {
	  i = firstI;
	  temp_diff = difference(i, inst.valueSparse(p1), 0);
	  p1++;
	} 
	
	if (m_weightByDistance) {
	  temp_diff *=
	    (m_weightsByRank[j]/distNormClass);
	} else {
	  if (m_stored[cl] > 0) {
	    temp_diff /= (double)m_stored[cl];
	  }
	}
	m_weights[i] -= temp_diff;

      }
    }
      

    // now do k nearest misses from each of the other classes
    temp_diff = 0.0;

    for (k = 0; k < m_numClasses; k++) {
      if (k != cl) // already done cl
	{
	  for (j = 0, temp = 0.0; j < m_stored[k]; j++) {
	    Instance cmp;
	    cmp = (m_weightByDistance) 
	      ? m_trainInstances.
	      instance((int)m_karray[k][tempSortedAtt[k][j]][1])
	      : m_trainInstances.instance((int)m_karray[k][j][1]);
	
	    for (int p1 = 0, p2 = 0; 
		 p1 < inst.numValues() || p2 < cmp.numValues();) {
	      if (p1 >= inst.numValues()) {
		firstI = m_trainInstances.numAttributes();
	      } else {
		firstI = inst.index(p1); 
	      }
	      if (p2 >= cmp.numValues()) {
		secondI = m_trainInstances.numAttributes();
	      } else {
		secondI = cmp.index(p2);
	      }
	      if (firstI == m_trainInstances.classIndex()) {
		p1++; continue;
	      } 
	      if (secondI == m_trainInstances.classIndex()) {
		p2++; continue;
	      } 
	      if (firstI == secondI) {
		i = firstI;
		temp_diff = difference(i, inst.valueSparse(p1), 
				       cmp.valueSparse(p2)); 
		p1++;p2++;
	      } else if (firstI > secondI) {
		i = secondI;
		temp_diff = difference(i, 0, cmp.valueSparse(p2));
		p2++;
	      } else {
		i = firstI;
		temp_diff = difference(i, inst.valueSparse(p1), 0);
		p1++;
	      } 

	      if (m_weightByDistance) {
		temp_diff *=
		  (m_weightsByRank[j]/distNormAtt[k]);
	      }
	      else {
		if (m_stored[k] > 0) {
		  temp_diff /= (double)m_stored[k];
		}
	      }
	      if (m_numClasses > 2) {
		m_weights[i] += ((m_classProbs[k]/w_norm)*temp_diff);
	      } else {
		m_weights[i] += temp_diff;
	      }
	    }
	  }
	}
    }
  }


  /**
   * Find the K nearest instances to supplied instance if the class is numeric,
   * or the K nearest Hits (same class) and Misses (K from each of the other
   * classes) if the class is discrete.
   *
   * @param instNum the index of the instance to find nearest neighbours of
   */
  private void findKHitMiss (int instNum) {
    int i, j;
    int cl;
    double ww;
    double temp_diff = 0.0;
    Instance thisInst = m_trainInstances.instance(instNum);

    for (i = 0; i < m_numInstances; i++) {
      if (i != instNum) {
	Instance cmpInst = m_trainInstances.instance(i);
	temp_diff = distance(cmpInst, thisInst);

	// class of this training instance or 0 if numeric
	if (m_numericClass) {
	  cl = 0;
	}
	else {
	  cl = (int)m_trainInstances.instance(i).value(m_classIndex);
	}

	// add this diff to the list for the class of this instance
	if (m_stored[cl] < m_Knn) {
	  m_karray[cl][m_stored[cl]][0] = temp_diff;
	  m_karray[cl][m_stored[cl]][1] = i;
	  m_stored[cl]++;

	  // note the worst diff for this class
	  for (j = 0, ww = -1.0; j < m_stored[cl]; j++) {
	    if (m_karray[cl][j][0] > ww) {
	      ww = m_karray[cl][j][0];
	      m_index[cl] = j;
	    }
	  }

	  m_worst[cl] = ww;
	}
	else 
	  /* if we already have stored knn for this class then check to
	     see if this instance is better than the worst */
	  {
	    if (temp_diff < m_karray[cl][m_index[cl]][0]) {
	      m_karray[cl][m_index[cl]][0] = temp_diff;
	      m_karray[cl][m_index[cl]][1] = i;

	      for (j = 0, ww = -1.0; j < m_stored[cl]; j++) {
		if (m_karray[cl][j][0] > ww) {
		  ww = m_karray[cl][j][0];
		  m_index[cl] = j;
		}
	      }

	      m_worst[cl] = ww;
	    }
	  }
      }
    }
  }


  // ============
  // Test method.
  // ============
  /**
   * Main method for testing this class.
   *
   * @param args the options
   */
  public static void main (String[] args) {
    try {
      System.out.println(AttributeSelection.
			 SelectAttributes(new ReliefFAttributeEval(), args));
    }
    catch (Exception e) {
      e.printStackTrace();
      System.out.println(e.getMessage());
    }
  }
}