Module « scipy.sparse »

Classe « dia_matrix »

Informations générales

Héritage

builtins.object
    spmatrix
        _data_matrix
            dia_matrix

Définition

class dia_matrix(_data_matrix):

Description _{[extrait de dia_matrix.__doc__]}

Sparse matrix with DIAgonal storage

    This can be instantiated in several ways:
        dia_matrix(D)
            with a dense matrix

        dia_matrix(S)
            with another sparse matrix S (equivalent to S.todia())

        dia_matrix((M, N), [dtype])
            to construct an empty matrix with shape (M, N),
            dtype is optional, defaulting to dtype='d'.

        dia_matrix((data, offsets), shape=(M, N))
            where the ``data[k,:]`` stores the diagonal entries for
            diagonal ``offsets[k]`` (See example below)

    Attributes
    ----------
    dtype : dtype
        Data type of the matrix
    shape : 2-tuple
        Shape of the matrix
    ndim : int
        Number of dimensions (this is always 2)
    nnz
        Number of stored values, including explicit zeros
    data
        DIA format data array of the matrix
    offsets
        DIA format offset array of the matrix

    Notes
    -----

    Sparse matrices can be used in arithmetic operations: they support
    addition, subtraction, multiplication, division, and matrix power.

    Examples
    --------

    >>> import numpy as np
    >>> from scipy.sparse import dia_matrix
    >>> dia_matrix((3, 4), dtype=np.int8).toarray()
    array([[0, 0, 0, 0],
           [0, 0, 0, 0],
           [0, 0, 0, 0]], dtype=int8)

    >>> data = np.array([[1, 2, 3, 4]]).repeat(3, axis=0)
    >>> offsets = np.array([0, -1, 2])
    >>> dia_matrix((data, offsets), shape=(4, 4)).toarray()
    array([[1, 0, 3, 0],
           [1, 2, 0, 4],
           [0, 2, 3, 0],
           [0, 0, 3, 4]])

    >>> from scipy.sparse import dia_matrix
    >>> n = 10
    >>> ex = np.ones(n)
    >>> data = np.array([ex, 2 * ex, ex])
    >>> offsets = np.array([-1, 0, 1])
    >>> dia_matrix((data, offsets), shape=(n, n)).toarray()
    array([[2., 1., 0., ..., 0., 0., 0.],
           [1., 2., 1., ..., 0., 0., 0.],
           [0., 1., 2., ..., 0., 0., 0.],
           ...,
           [0., 0., 0., ..., 2., 1., 0.],
           [0., 0., 0., ..., 1., 2., 1.],
           [0., 0., 0., ..., 0., 1., 2.]])
    

Constructeur(s)

Signature du constructeur	Description
__init__(self, arg1, shape=None, dtype=None, copy=False)

Liste des opérateurs

Opérateurs hérités de la classe _data_matrix

__imul__, __itruediv__, __neg__

Liste des opérateurs

Opérateurs hérités de la classe spmatrix

__add__, __eq__, __ge__, __gt__, __iadd__, __isub__, __le__, __lt__, __matmul__, __mul__, __ne__, __pow__, __radd__, __rmul__, __rsub__, __rtruediv__, __sub__, __truediv__

Méthodes héritées de la classe _data_matrix

__abs__, __init_subclass__, __round__, __subclasshook__, astype, conj, copy, power

Méthodes héritées de la classe spmatrix

__bool__, __div__, __getattr__, __idiv__, __iter__, __len__, __nonzero__, __rdiv__, __rmatmul__, __str__, asformat, asfptype, conjugate, dot, get_shape, getcol, getformat, getH, getmaxprint, getrow, maximum, mean, minimum, multiply, nonzero, reshape, set_shape, setdiag, toarray, tobsr, tocsr, todense, todok, tolil

Méthodes héritées de la classe object

__delattr__, __dir__, __format__, __getattribute__, __hash__, __reduce__, __reduce_ex__, __setattr__, __sizeof__