Documentation API of the 'cern.colt.matrix.impl.SparseDoubleMatrix2D' Java class
SparseDoubleMatrix2D
cern.colt.matrix.impl

Class SparseDoubleMatrix2D

  • All Implemented Interfaces:
    Serializable, Cloneable


    public class SparseDoubleMatrix2Dextends DoubleMatrix2D
    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 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 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 operationsget, getQuick, set, setQuick and sizeassuming 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 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 Also:
    cern.colt.map, OpenIntDoubleHashMap, Serialized Form

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