Java Code Examples for weka.core.matrix.Matrix#getRowDimension()

/**
  * Compute the sIB score
  * @param m a term-cluster matrix, with m[i, j] is the probability of term i given cluster j  
  * @param Pt an array of cluster prior probabilities
  * @return the sIB score which indicates the quality of the partition
  */
 private double sIB_local_MI(Matrix m, double[] Pt) {
   double Hy = 0.0, Ht = 0.0;
   for (int i = 0; i < Pt.length; i++) {
     Ht += Pt[i] * Math.log(Pt[i]);
   }
   Ht = -Ht;
   
   for (int i = 0; i < m_numAttributes; i++) {
     double Py = 0.0;
     for (int j = 0; j < m_numCluster; j++) {
Py += m.get(i, j) * Pt[j];	
     }     
     if(Py == 0) continue;
     Hy += Py * Math.log(Py);
   }
   Hy = -Hy;
   
   double Hyt = 0.0, tmp = 0.0;
   for (int i = 0; i < m.getRowDimension(); i++) {
     for (int j = 0; j < m.getColumnDimension(); j++) {
if ((tmp = m.get(i, j)) == 0 || Pt[j] == 0) {
  continue;
}
tmp *= Pt[j];
Hyt += tmp * Math.log(tmp);
     }
   }
   return Hy + Ht + Hyt;
 }
 

/**
  * Compute the MI between instances and attributes
  * @param m the term-document matrix
  * @param input object that describes the statistics about the training data
  */
 private void MI(Matrix m, Input input){    
   int minDimSize = m.getColumnDimension() < m.getRowDimension() ? m.getColumnDimension() : m.getRowDimension();
   if(minDimSize < 2){
     System.err.println("Warning : This is not a JOINT distribution");
     input.Hx = Entropy (m);
     input.Hy = 0;
     input.Ixy = 0;
     return;
   }
   
   input.Hx = Entropy(input.Px);
   input.Hy = Entropy(input.Py);
   
   double entropy = input.Hx + input.Hy;    
   for (int i=0; i < m_numInstances; i++) {
     Instance inst = m_data.instance(i);
     for (int v = 0; v < inst.numValues(); v++) {
double tmp = m.get(inst.index(v), i);
if(tmp <= 0) continue;
entropy += tmp * Math.log(tmp);
     }
   }
   input.Ixy = entropy;
   if(m_verbose) {
     System.out.println("Ixy = " + input.Ixy);
   }
 }
 

/**
  * Compute the entropy score based on a matrix
  * @param p a matrix with non-negative and normalized probabilities
  * @return the entropy value
  */
 private double Entropy(Matrix p) {
   double mi = 0;
   for (int i = 0; i < p.getRowDimension(); i++) {
     for (int j = 0; j < p.getColumnDimension(); j++) {
if(p.get(i, j) == 0){
  continue;
}
mi += p.get(i, j) + Math.log(p.get(i, j)); 
     }
   }
   mi = -mi;
   return mi;
 }
 

/**
 * normalizes the given vector (inplace) 
 * 
 * @param v		the vector to normalize
 */
protected void normalizeVector(Matrix v) {
  double	sum;
  int		i;
  
  // determine length
  sum = 0;
  for (i = 0; i < v.getRowDimension(); i++)
    sum += v.get(i, 0) * v.get(i, 0);
  sum = StrictMath.sqrt(sum);
  
  // normalize content
  for (i = 0; i < v.getRowDimension(); i++)
    v.set(i, 0, v.get(i, 0) / sum);
}
 

private double getLogDeterminant(Matrix L) {
  double logDeterminant;
  double detL = 0;
  int n = L.getRowDimension();
  double[][] matrixAsArray = L.getArray();
  for (int i = 0; i < n; i++) {

    detL += Math.log(matrixAsArray[i][i]);
  }
  logDeterminant = detL * 2;

  return logDeterminant;
}
 

/**
 * Helper method that transforms a Matrix object to an Instances object.
 *
 * @param mat The Matrix to transform.
 * @param patternInst the Instances template to use
 * @return  The resulting Instances object.
 */
public static Instances matrixToInstances(Matrix mat, Instances patternInst){
	Instances result = new Instances(patternInst);
	for (int i = 0; i < mat.getRowDimension(); i++) {
		double[] row =  mat.getArray()[i];
		DenseInstance denseInst = new DenseInstance(1.0, row);
		result.add(denseInst);
	}

	return result;
}
 

/** Construct a PaceMatrix from a Matrix 
    @param X    Matrix 
*/
public PaceMatrix( Matrix X ) {
  super( X.getRowDimension(), X.getColumnDimension() );
  A = X.getArray();
}