Examples of cern.colt.matrix.impl.SparseDoubleMatrix1D

cern.colt.matrix.impl.SparseDoubleMatrix1D
Sparse hashed 1-d matrix (aka vector) 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 1000000 matrix with minLoadFactor=0.25 and maxLoadFactor=0.5 and 1000000 non-zero cells consumes between 25 MB and 50 MB. The same 1000000 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 DenseDoubleMatrix1D}. However, constant factors are considerably larger. @author wolfgang.hoschek@cern.ch @version 1.0, 09/24/99

 * The values are copied. So subsequent changes in <tt>values</tt> are not reflected in the matrix, and vice-versa.
 *
 * @param values The values to be filled into the new matrix.
 */
public DoubleMatrix1D make(double[] values) {
  if (this==sparse) return new SparseDoubleMatrix1D(values);
  else return new DenseDoubleMatrix1D(values);
}

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}
/**
 * Constructs a matrix with the given shape, each cell initialized with zero.
 */
public DoubleMatrix1D make(int size) {
  if (this==sparse) return new SparseDoubleMatrix1D(size);
  return new DenseDoubleMatrix1D(size);
}

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 * The values are copied. So subsequent changes in <tt>values</tt> are not reflected in the matrix, and vice-versa.
 *
 * @param values The values to be filled into the new matrix.
 */
public DoubleMatrix1D make(double[] values) {
  if (this==sparse) return new SparseDoubleMatrix1D(values);
  else return new DenseDoubleMatrix1D(values);
}

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}
/**
 * Constructs a matrix with the given shape, each cell initialized with zero.
 */
public DoubleMatrix1D make(int size) {
  if (this==sparse) return new SparseDoubleMatrix1D(size);
  return new DenseDoubleMatrix1D(size);
}

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        else
            setEdgeWeights(edgeWeightKey);
        normalizeEdgeTransitionWeights();


        mIndexer = Indexer.<V>create(graph.getVertices());
        mRankings = new SparseDoubleMatrix1D(graph.getVertexCount());
    }

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Related Classes of cern.colt.matrix.impl.SparseDoubleMatrix1D

cern.colt.map.OpenIntDoubleHashMap

cern.colt.matrix.DoubleFactory1D

edu.uci.ics.jung.algorithms.importance.MarkovCentrality

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