Documentation API of the 'cern.colt.matrix.tfloat.impl.SparseFloatMatrix3D' Java class
SparseFloatMatrix3D
cern.colt.matrix.tfloat.impl

Class SparseFloatMatrix3D

  • All Implemented Interfaces:
    Serializable, Cloneable


    public class SparseFloatMatrix3Dextends FloatMatrix3D
    Sparse hashed 3-d matrix holding float 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 OpenIntFloatHashMap, 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 100 x 100 x 100 matrix with minLoadFactor=0.25 and maxLoadFactor=0.5 and 1000000 non-zero cells consumes between 25 MB and 50 MB. The same 100 x 100 x 100 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 DenseFloatMatrix2D. However, constant factors are considerably larger.

    Cells are internally addressed in (in decreasing order of significance): slice major, row major, column major. Applications demanding utmost speed can exploit this fact. Setting/getting values in a loop slice-by-slice, row-by-row, column-by-column is quicker than, for example, column-by-column, row-by-row, slice-by-slice. Thus

     for (int slice = 0; slice < slices; slice++) {     for (int row = 0; row < rows; row++) {         for (int column = 0; column < columns; column++) {             matrix.setQuick(slice, row, column, someValue);         }     } } 
    is quicker than
     for (int column = 0; column < columns; column++) {     for (int row = 0; row < rows; row++) {         for (int slice = 0; slice < slices; slice++) {             matrix.setQuick(slice, row, column, someValue);         }     } } 
    See Also:
    cern.colt.map, OpenIntFloatHashMap, Serialized Form

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