sp.coo_matrix(), sp.eye()
sp.coo_matrix()
坐标格式的稀疏矩阵。
也被称为“ijv”或“三重”格式。
这可以通过几种方式实例化:
coo_matrix(D)
密集矩阵D;
coo_matrix(S)
与另一个稀疏矩阵S(等价于S.tocoo())
coo_matrix((M, N), [dtype])
构造一个形状为(M, N)的空矩阵
coo_matrix((data, (i, j)), [shape=(M, N)])
从三个数组构造:
1. data[:] the entries of the matrix, in any order 矩阵的元素,以任何顺序
2. i[:] the row indices of the matrix entries 矩阵项的行下标
3. j[:] the column indices of the matrix entries 矩阵项的列下标
参数:
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
COO format data array of the matrix
row
COO format row index array of the matrix
col
COO format column index array of the matrix
稀疏矩阵可以用于算术运算:它们支持加,减,乘,除,矩阵幂。
COO格式优势
方便稀疏格式之间的快速转换;
允许重复条目(见示例)
非常快速的转换CSR/CSC格式
>>> from scipy.sparse import coo_matrix
>>> import numpy as np
>>> coo_matrix((3, 4), dtype=np.int8).toarray()
array([[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]], dtype=int8)
>>> # Constructing a matrix using ijv format
>>> row = np.array([0, 3, 1, 0])
>>> col = np.array([0, 3, 1, 2])
>>> data = np.array([4, 5, 7, 9])
>>> coo_matrix((data, (row, col)), shape=(4, 4)).toarray()
array([[4, 0, 9, 0],
[0, 7, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 5]])
>>> # Constructing a matrix with duplicate indices
>>> row = np.array([0, 0, 1, 3, 1, 0, 0])
>>> col = np.array([0, 2, 1, 3, 1, 0, 0])
>>> data = np.array([1, 1, 1, 1, 1, 1, 1])
>>> coo = coo_matrix((data, (row, col)), shape=(4, 4))
>>> coo = coo_matrix((data, (row, col)), shape=(4, 4))
>>> coo.toarray()
array([[3, 0, 1, 0],
[0, 2, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 1]])
>>> #保持重复索引,直到隐式或显式求和
sp.eye()
eye(N, M=None, k=0, dtype=float) 是scipy包中的一个创建特殊矩阵(单位矩阵E)的方法
>>> from scipy import *
>>> eye(3)
[[1. 0. 0.]
[0. 1. 0.]
[0. 0. 1.]]
>>> eye(3,3)
array([[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]])
>>> eye(3,4)
array([[1., 0., 0., 0.],
[0., 1., 0., 0.],
[0., 0., 1., 0.]])
>>> eye(3,4,1)
array([[0., 1., 0., 0.],
[0., 0., 1., 0.],
[0., 0., 0., 1.]])
>>> eye(3,4,2)
array([[0., 0., 1., 0.],
[0., 0., 0., 1.],
[0., 0., 0., 0.]])