Python稀疏矩阵运算库scipy.sparse用法精要

1、稀疏矩阵的常见存储形式

• bsr_matrix(arg1[, shape, dtype, copy, blocksize])
Block Sparse Row matrix
• coo_matrix(arg1[, shape, dtype, copy])
A sparse matrix in COOrdinate format.
• csc_matrix(arg1[, shape, dtype, copy])
Compressed Sparse Column matrix
• csr_matrix(arg1[, shape, dtype, copy])
Compressed Sparse Row matrix
• dia_matrix(arg1[, shape, dtype, copy])
Sparse matrix with DIAgonal storage
• dok_matrix(arg1[, shape, dtype, copy])
Dictionary Of Keys based sparse matrix.
• lil_matrix(arg1[, shape, dtype, copy])
Row-based linked list sparse matrix

2、不同存储形式的区别

>>> from scipy import sparse
>>> sparse.bsr_matrix([[1,0,0,0,0],[0,1,0,0,1]])
<2x5 sparse matrix of type '<class 'numpy.int32'>'
 with 3 stored elements (blocksize = 1x1) in Block Sparse Row format>
>>> sparse.coo_matrix([[1,0,0,0,0],[0,1,0,0,1]])
<2x5 sparse matrix of type '<class 'numpy.int32'>'
 with 3 stored elements in COOrdinate format>
>>> sparse.csc_matrix([[1,0,0,0,0],[0,1,0,0,1]])
<2x5 sparse matrix of type '<class 'numpy.int32'>'
 with 3 stored elements in Compressed Sparse Column format>
>>> sparse.csr_matrix([[1,0,0,0,0],[0,1,0,0,1]])
<2x5 sparse matrix of type '<class 'numpy.int32'>'
 with 3 stored elements in Compressed Sparse Row format>
>>> sparse.dia_matrix([[1,0,0,0,0],[0,1,0,0,1]])
<2x5 sparse matrix of type '<class 'numpy.int32'>'
 with 4 stored elements (2 diagonals) in DIAgonal format>
>>> sparse.dok_matrix([[1,0,0,0,0],[0,1,0,0,1]])
<2x5 sparse matrix of type '<class 'numpy.int32'>'
 with 3 stored elements in Dictionary Of Keys format>
>>> sparse.lil_matrix([[1,0,0,0,0],[0,1,0,0,1]])
<2x5 sparse matrix of type '<class 'numpy.int32'>'
 with 3 stored elements in LInked List format>

3、sparse模块中用于创建稀疏矩阵的函数

• eye(m[, n, k, dtype, format])
Sparse matrix with ones on diagonal
• identity(n[, dtype, format])
Identity matrix in sparse format
• kron(A, B[, format])
kronecker product of sparse matrices A and B
• kronsum(A, B[, format])
kronecker sum of sparse matrices A and B
• diags(diagonals[, offsets, shape, format, dtype])
Construct a sparse matrix from diagonals.
• spdiags(data, diags, m, n[, format]) 
Return a sparse matrix from diagonals.
• block_diag(mats[, format, dtype])
Build a block diagonal sparse matrix from provided matrices.
• tril(A[, k, format])
Return the lower triangular portion of a matrix in sparse format
• triu(A[, k, format])
Return the upper triangular portion of a matrix in sparse format
• bmat(blocks[, format, dtype])
Build a sparse matrix from sparse sub-blocks
• hstack(blocks[, format, dtype])
Stack sparse matrices horizontally (column wise)
• vstack(blocks[, format, dtype])
Stack sparse matrices vertically (row wise)
• rand(m, n[, density, format, dtype, ...])
Generate a sparse matrix of the given shape and density with uniformly distributed values.
• random(m, n[, density, format, dtype, ...])
Generate a sparse matrix of the given shape and density  with randomly distributed values.

4、用法演示（为了不影响排版，直接从我整理的PPT上截图过来了）