python Sparse matrices 单位矩阵

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原创最新推荐文章于 2026-09-02 01:00:00 发布 3.5k 阅读 AI 写同款· GEO 优化 ›

python——稀疏矩阵scipy.sparse coo_matrix无法对矩阵的元素进行增删改等操作，一旦创建之后，除了将之转换成其它格式的矩阵，几乎无法对其做任何操作和矩阵运算。coo_matrix((M, N), [dtype])：生成形状为(M, N)的指定数据类型dtype的coo_matrix稀疏矩阵。最简单的存储方式，分别用row、col、val三个数组存储矩阵非零元素的行下标，列下标与值。coo_matrix(S)：将别的格式的稀疏矩阵S转成coo_matrix稀疏矩阵。row：矩阵非零元素的行下标数组。col：矩阵非零元素的列下标数组。阅读详情

identity(n[, dtype, format]) Identity matrix in sparse format Returns an identity matrix with shape (n,n) using a given sparse format and dtype.

创建n X n单位矩阵。

format：

bsr_matrix(arg1[, shape, dtype, copy, blocksize]) Block Sparse Row matrix This can be instantiated in several ways: bsr_matrix(D, [blocksize=(R,C)]) where D is a dense matrix or 2-D ndarray.
coo_matrix(arg1[, shape, dtype, copy]) A sparse matrix in COOrdinate format.
csc_matrix(arg1[, shape, dtype, copy]) Compressed Sparse Column matrix This can be instantiated in several ways: csc_matrix(D) with a dense matrix or rank-2 ndarray D csc_matrix(S) with another sparse matrix S (equivalent to S.tocsc()) csc_matrix((M, N), [dtype]) to construct an empty matrix with shape (M, N) dtype is optional, defaulting to dtype=’d’.
csr_matrix(arg1[, shape, dtype, copy]) 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’.
dia_matrix(arg1[, shape, dtype, copy]) 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’.
dok_matrix(arg1[, shape, dtype, copy]) Dictionary Of Keys based sparse matrix.
lil_matrix(arg1[, shape, dtype, copy]) Row-based linked list sparse matrix This is a structure for constructing sparse matrices incrementally.
spmatrix([maxprint]) This class provides a base class for all sparse matrices.

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Examples

>>> from scipy.sparse import identity
>>> identity(3).toarray()
array([[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]])
>>> identity(3, dtype='int8', format='dia')
<3x3 sparse matrix of type '<type 'numpy.int8'>'
with 3 stored elements (1 diagonals) in DIAgonal format>