Module « scipy.sparse »

Classe « csr_matrix »

Informations générales

Héritage

    builtins.object
        IndexMixin
    builtins.object
        _minmax_mixin
builtins.object
    spmatrix
        _data_matrix
            _cs_matrix
                csr_matrix

Définition

class csr_matrix(_cs_matrix):

Description _{[extrait de csr_matrix.__doc__]}

    Compressed Sparse Row matrix

    This can be instantiated in several ways:
        csr_matrix(D)
            with a dense matrix or rank-2 ndarray D

        csr_matrix(S)
            with another sparse matrix S (equivalent to S.tocsr())

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

        csr_matrix((data, (row_ind, col_ind)), [shape=(M, N)])
            where ``data``, ``row_ind`` and ``col_ind`` satisfy the
            relationship ``a[row_ind[k], col_ind[k]] = data[k]``.

        csr_matrix((data, indices, indptr), [shape=(M, N)])
            is the standard CSR representation where the column indices for
            row i are stored in ``indices[indptr[i]:indptr[i+1]]`` and their
            corresponding values are stored in ``data[indptr[i]:indptr[i+1]]``.
            If the shape parameter is not supplied, the matrix dimensions
            are inferred from the index arrays.

    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
        CSR format data array of the matrix
    indices
        CSR format index array of the matrix
    indptr
        CSR format index pointer array of the matrix
    has_sorted_indices
        Whether indices are sorted

    Notes
    -----

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

    Advantages of the CSR format
      - efficient arithmetic operations CSR + CSR, CSR * CSR, etc.
      - efficient row slicing
      - fast matrix vector products

    Disadvantages of the CSR format
      - slow column slicing operations (consider CSC)
      - changes to the sparsity structure are expensive (consider LIL or DOK)

    Examples
    --------

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

    >>> row = np.array([0, 0, 1, 2, 2, 2])
    >>> col = np.array([0, 2, 2, 0, 1, 2])
    >>> data = np.array([1, 2, 3, 4, 5, 6])
    >>> csr_matrix((data, (row, col)), shape=(3, 3)).toarray()
    array([[1, 0, 2],
           [0, 0, 3],
           [4, 5, 6]])

    >>> indptr = np.array([0, 2, 3, 6])
    >>> indices = np.array([0, 2, 2, 0, 1, 2])
    >>> data = np.array([1, 2, 3, 4, 5, 6])
    >>> csr_matrix((data, indices, indptr), shape=(3, 3)).toarray()
    array([[1, 0, 2],
           [0, 0, 3],
           [4, 5, 6]])

    Duplicate entries are summed together:

    >>> row = np.array([0, 1, 2, 0])
    >>> col = np.array([0, 1, 1, 0])
    >>> data = np.array([1, 2, 4, 8])
    >>> csr_matrix((data, (row, col)), shape=(3, 3)).toarray()
    array([[9, 0, 0],
           [0, 2, 0],
           [0, 4, 0]])

    As an example of how to construct a CSR matrix incrementally,
    the following snippet builds a term-document matrix from texts:

    >>> docs = [["hello", "world", "hello"], ["goodbye", "cruel", "world"]]
    >>> indptr = [0]
    >>> indices = []
    >>> data = []
    >>> vocabulary = {}
    >>> for d in docs:
    ...     for term in d:
    ...         index = vocabulary.setdefault(term, len(vocabulary))
    ...         indices.append(index)
    ...         data.append(1)
    ...     indptr.append(len(indices))
    ...
    >>> csr_matrix((data, indices, indptr), dtype=int).toarray()
    array([[2, 1, 0, 0],
           [0, 1, 1, 1]])

    

Liste des opérateurs

Opérateurs hérités de la classe _cs_matrix

__eq__, __ge__, __gt__, __le__, __lt__, __ne__

Liste des opérateurs

Opérateurs hérités de la classe IndexMixin

__getitem__, __setitem__

Liste des opérateurs

Opérateurs hérités de la classe _data_matrix

__imul__, __itruediv__, __neg__

Liste des opérateurs

Opérateurs hérités de la classe spmatrix

__add__, __iadd__, __isub__, __matmul__, __mul__, __pow__, __radd__, __rmul__, __rsub__, __rtruediv__, __sub__, __truediv__

Méthodes héritées de la classe _cs_matrix

__init_subclass__, __subclasshook__, check_format, diagonal, eliminate_zeros, getnnz, maximum, minimum, multiply, prune, resize, sort_indices, sorted_indices, sum, sum_duplicates, toarray, tocoo

Méthodes héritées de la classe _minmax_mixin

argmax, argmin, max, min

Méthodes héritées de la classe _data_matrix

__abs__, __round__, astype, conj, copy, count_nonzero, power

Méthodes héritées de la classe spmatrix

__bool__, __div__, __getattr__, __idiv__, __len__, __nonzero__, __rdiv__, __repr__, __rmatmul__, __str__, asformat, asfptype, conjugate, dot, get_shape, getformat, getH, getmaxprint, mean, nonzero, reshape, set_shape, setdiag, todense, todia, todok

Méthodes héritées de la classe object

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