Python scipy.sparse.csr_matrix()[csc_matrix()]

本文以csr_matrix为例来说明sparse矩阵的使用方法，其他类型的sparse矩阵可以参考https://docs.scipy.org/doc/scipy/reference/sparse.html

csr_matrix是Compressed Sparse Row matrix的缩写组合，下面介绍其两种初始化方法

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.

上述官方文档给出了：稀疏矩阵的参数及其含义、稀疏矩阵的构造方式。阐述形式简单明了，读起来令人赏心悦目。

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)

上述官方文档时稀疏矩阵的一些特性以及csr_matrix的优缺点，并且在指明各种缺点的同时，提供了可以考虑的技术实现。

代码示例1

import numpy as np
from scipy.sparse import csr_matrix

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])
a = csr_matrix((data, (row, col)), shape=(3, 3)).toarray()print(a)

运行结果：

array([[1, 0, 2],
       [0, 0, 3],
       [4, 5, 6]])

代码示例2

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])
a = csr_matrix((data, indices, indptr), shape=(3, 3)).toarray()

print(a)

允许结果：

array([[1, 0, 2],
       [0, 0, 3],
       [4, 5, 6]])