Examples of cern.colt.matrix.impl.SparseDoubleMatrix2D

cern.colt.matrix.impl.SparseDoubleMatrix2D
Sparse hashed 2-d matrix holding double elements. First see the package summary and javadoc tree view to get the broad picture.
Implementation:
Note that this implementation is not synchronized. Uses a {@link cern.colt.map.OpenIntDoubleHashMap}, which is a compact and performant hashing technique.
Memory requirements:
Cells that
- are never set to non-zero values do not use any memory.
- switch from zero to non-zero state do use memory.
- switch back from non-zero to zero state also do use memory. However, their memory is automatically reclaimed from time to time. It can also manually be reclaimed by calling {@link #trimToSize()}.
worst case: memory [bytes] = (1/minLoadFactor) * nonZeros * 13.
best case: memory [bytes] = (1/maxLoadFactor) * nonZeros * 13.
Where nonZeros = cardinality() is the number of non-zero cells. Thus, a 1000 x 1000 matrix with minLoadFactor=0.25 and maxLoadFactor=0.5 and 1000000 non-zero cells consumes between 25 MB and 50 MB. The same 1000 x 1000 matrix with 1000 non-zero cells consumes between 25 and 50 KB.
Time complexity:
This class offers expected time complexity O(1) (i.e. constant time) for the basic operations get, getQuick, set, setQuick and size assuming the hash function disperses the elements properly among the buckets. Otherwise, pathological cases, although highly improbable, can occur, degrading performance to O(N) in the worst case. As such this sparse class is expected to have no worse time complexity than its dense counterpart {@link DenseDoubleMatrix2D}. However, constant factors are considerably larger.
Cells are internally addressed in row-major. Performance sensitive applications can exploit this fact. Setting values in a loop row-by-row is quicker than column-by-column, because fewer hash collisions occur. Thus
```
 for (int row=0; row < rows; row++) { for (int column=0; column < columns; column++) { matrix.setQuick(row,column,someValue); } } 
```
is quicker than
```
 for (int column=0; column < columns; column++) { for (int row=0; row < rows; row++) { matrix.setQuick(row,column,someValue); } } 
```
@see cern.colt.map @see cern.colt.map.OpenIntDoubleHashMap @author wolfgang.hoschek@cern.ch @version 1.0, 09/24/99

 *
 * @param values The values to be filled into the new matrix.
 * @throws IllegalArgumentException if <tt>for any 1 &lt;= row &lt; values.length: values[row].length != values[row-1].length</tt>.
 */
public DoubleMatrix2D make(double[][] values) {
  if (this==sparse) return new SparseDoubleMatrix2D(values);
  else return new DenseDoubleMatrix2D(values);
}

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}
/**
 * Constructs a matrix with the given shape, each cell initialized with zero.
 */
public DoubleMatrix2D make(int rows, int columns) {
  if (this==sparse) return new SparseDoubleMatrix2D(rows,columns);
  if (this==rowCompressed) return new RCDoubleMatrix2D(rows,columns);
  //if (this==rowCompressedModified) return new RCMDoubleMatrix2D(rows,columns);
  else return new DenseDoubleMatrix2D(rows,columns);
}

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 *
 * @param values The values to be filled into the new matrix.
 * @throws IllegalArgumentException if <tt>for any 1 &lt;= row &lt; values.length: values[row].length != values[row-1].length</tt>.
 */
public DoubleMatrix2D make(double[][] values) {
  if (this==sparse) return new SparseDoubleMatrix2D(values);
  else return new DenseDoubleMatrix2D(values);
}

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}
/**
 * Constructs a matrix with the given shape, each cell initialized with zero.
 */
public DoubleMatrix2D make(int rows, int columns) {
  if (this==sparse) return new SparseDoubleMatrix2D(rows,columns);
  if (this==rowCompressed) return new RCDoubleMatrix2D(rows,columns);
  //if (this==rowCompressedModified) return new RCMDoubleMatrix2D(rows,columns);
  else return new DenseDoubleMatrix2D(rows,columns);
}

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    System.out.println("Check 3: " + (Runtime.getRuntime().totalMemory() - Runtime.getRuntime().freeMemory()));


    nodearray = nodes.toArray(new Node[]{});
    
    // SET THE EDGES
    SparseDoubleMatrix2D Weight = new SparseDoubleMatrix2D(nodearray.length,nodearray.length);
    for(Resource res : bscrdf.listSubjects().toSet()){
      for(Statement st : res.listProperties().toSet()){
        if(relationsandweights.keySet().contains(st.getPredicate().getURI())){
          Weight.set(urisandnodes.get(res.getURI()).name, urisandnodes.get(st.getObject().toString()).name, relationsandweights.get(st.getPredicate().getURI()));
        }
      }
      //  Weight.set(urisandnodes.get(res.getURI()).name, urisandnodes.get(st.getObject().toString()).name, baseweight);
      //}
//      for(Statement st : res.listProperties(lowweight).toSet()){
//        Weight.set(urisandnodes.get(res.getURI()).name, urisandnodes.get(st.getObject().toString()).name, lowweightnum);
//        Weight.set(urisandnodes.get(st.getObject().toString()).name, urisandnodes.get(res.getURI()).name, lowweightnum);
//      }
//      for(Statement st : res.listProperties(fiveweight).toSet()){
//        Weight.set(urisandnodes.get(res.getURI()).name, urisandnodes.get(st.getObject().toString()).name, 5);
//        Weight.set(urisandnodes.get(st.getObject().toString()).name, urisandnodes.get(res.getURI()).name, 5);
//      }
    }
    
    Weight.getNonZeros(rowlist, columnlist, valuelist);
    
    System.out.println(nodearray.length);
    System.out.println(rowlist.size());
    setComboBoxData(autocompletelist);

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        currentRow.add(Double.parseDouble(token));
      }
      rows.add(currentRow);
    }
    int size = rows.size();
    DoubleMatrix2D matrix = new SparseDoubleMatrix2D(size, size);
    for (int i = 0; i < size; i++) {
      List<Double> currentRow = rows.get(i);
      if (currentRow.size() != size) {
        throw new IllegalArgumentException(
          "Matrix must have the same number of rows as columns");
      }
      for (int j = 0; j < size; j++) {
        double currentVal = currentRow.get(j);
        if (currentVal != 0) {
          matrix.setQuick(i, j, currentVal);
        }
      }
    }
    return matrix;
  }

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    public static <V,E> SparseDoubleMatrix2D graphToSparseMatrix(Graph<V,E> g, Map<E,Number> nev)
    {
        if (nev == null)
            nev = new ConstantMap<E,Number>(1);
        int numVertices = g.getVertexCount();
        SparseDoubleMatrix2D matrix = new SparseDoubleMatrix2D(numVertices,
                numVertices);


        BidiMap<V,Integer> indexer = Indexer.<V>create(g.getVertices());
        int i=0;
        
        for(V v : g.getVertices())
        {
            for (E e : g.getOutEdges(v))
            {
                V w = g.getOpposite(v,e);
                int j = indexer.get(w);
                matrix.set(i, j, matrix.getQuick(i,j) + nev.get(e).doubleValue());
            }
            i++;
        }
        return matrix;
    }

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     * @return SparseDoubleMatrix2D
     */
    public static <V,E> SparseDoubleMatrix2D createVertexDegreeDiagonalMatrix(Graph<V,E> graph)
    {
        int numVertices = graph.getVertexCount();
        SparseDoubleMatrix2D matrix = new SparseDoubleMatrix2D(numVertices,
                numVertices);
        int i = 0;
        for (V v : graph.getVertices())
        {
            matrix.set(i, i, graph.degree(v));
            i++;
        }
        return matrix;
    }

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        DoubleMatrix2D A = GraphMatrixOperations.graphToSparseMatrix(graph,
                null);
        //create diagonal matrix of vertex degrees
        DoubleMatrix2D D = GraphMatrixOperations
                .createVertexDegreeDiagonalMatrix(graph);
        DoubleMatrix2D temp = new SparseDoubleMatrix2D(numVertices - 1,
                numVertices - 1);
        //compute D - A except for last row and column
        for (int i = 0; i < numVertices - 1; i++)
        {
            for (int j = 0; j < numVertices - 1; j++)
            {
                temp.set(i, j, D.get(i, j) - A.get(i, j));
            }
        }
        Algebra algebra = new Algebra();
        DoubleMatrix2D tempInverse = algebra.inverse(temp);
        DoubleMatrix2D T = new SparseDoubleMatrix2D(numVertices, numVertices);
        //compute "voltage" matrix
        for (int i = 0; i < numVertices - 1; i++)
        {
            for (int j = 0; j < numVertices - 1; j++)
            {
                T.set(i, j, tempInverse.get(i, j));
            }
        }
        return T;
    }

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                    temp.set(i, j, value);
            }
        }
        Algebra algebra = new Algebra();
        DoubleMatrix2D fundamentalMatrix = algebra.inverse(temp);
        temp = new SparseDoubleMatrix2D(temp.rows(), temp.columns());
        for (int i = 0; i < temp.rows(); i++)
        {
            for (int j = 0; j < temp.columns(); j++)
            {
                double value = -1.0 * fundamentalMatrix.get(i, j);
                value += fundamentalMatrix.get(j, j);
                if (i == j)
                    value += 1;
                if (value != 0)
                    temp.set(i, j, value);
            }
        }
        DoubleMatrix2D stationaryMatrixDiagonal = new SparseDoubleMatrix2D(temp
                .rows(), temp.columns());
        int numVertices = stationaryDistribution.size();
        for (int i = 0; i < numVertices; i++)
            stationaryMatrixDiagonal.set(i, i, 1.0 / stationaryDistribution
                    .get(i));
        DoubleMatrix2D meanFirstPassageMatrix = algebra.mult(temp,
                stationaryMatrixDiagonal);
        return meanFirstPassageMatrix;
    }

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