Vous êtes un professionnel et vous avez besoin d'une formation ? Sensibilisation à
l'Intelligence Artificielle Voir le programme détaillé

Module « scipy.sparse »

Classe « lil_matrix »

Informations générales

Héritage

builtins.object
    IndexMixin
builtins.object
    _spbase
        _lil_base
    builtins.object
        spmatrix
            lil_matrix

Définition

class lil_matrix(spmatrix, _lil_base):

help(lil_matrix)

Row-based LIst of Lists sparse matrix.

This is a structure for constructing sparse matrices incrementally.
Note that inserting a single item can take linear time in the worst case;
to construct the matrix efficiently, make sure the items are pre-sorted by
index, per row.

This can be instantiated in several ways:
    lil_matrix(D)
        where D is a 2-D ndarray

    lil_matrix(S)
        with another sparse array or matrix S (equivalent to S.tolil())

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

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
size
data
    LIL format data array of the matrix
rows
    LIL format row index array of the matrix
T

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

Advantages of the LIL format
    - supports flexible slicing
    - changes to the matrix sparsity structure are efficient

Disadvantages of the LIL format
    - arithmetic operations LIL + LIL are slow (consider CSR or CSC)
    - slow column slicing (consider CSC)
    - slow matrix vector products (consider CSR or CSC)

Intended Usage
    - LIL is a convenient format for constructing sparse matrices
    - once a matrix has been constructed, convert to CSR or
      CSC format for fast arithmetic and matrix vector operations
    - consider using the COO format when constructing large matrices

Data Structure
    - An array (``self.rows``) of rows, each of which is a sorted
      list of column indices of non-zero elements.
    - The corresponding nonzero values are stored in similar
      fashion in ``self.data``.

Constructeur(s)

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

Liste des opérateurs

Opérateurs hérités de la classe _lil_base

__getitem__, __iadd__, __imul__, __isub__, __itruediv__, __setitem__, __truediv__

Liste des opérateurs

Opérateurs hérités de la classe _spbase

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

Méthodes héritées de la classe _lil_base

__init_subclass__, __subclasshook__, copy, count_nonzero, getrow, getrowview, reshape, resize, toarray, tocsr, tolil, transpose

Méthodes héritées de la classe _spbase

__abs__, __bool__, __div__, __idiv__, __iter__, __len__, __nonzero__, __rdiv__, __repr__, __rmatmul__, __round__, __str__, asformat, astype, conj, conjugate, diagonal, dot, maximum, mean, minimum, multiply, nonzero, power, setdiag, sum, tobsr, tocoo, tocsc, todense, todia, todok, trace

Méthodes héritées de la classe spmatrix

asfptype, get_shape, getcol, getformat, getH, getmaxprint, getnnz, set_shape

Méthodes héritées de la classe object

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

Vous êtes un professionnel et vous avez besoin d'une formation ? Deep Learning avec Python
et Keras et Tensorflow Voir le programme détaillé