我在python中使用CSR稀疏格式的稀疏矩阵,我想将其导入MATLAB。 MATLAB没有CSR稀疏格式。它对所有类型的矩阵只有1种稀疏格式。由于矩阵在密集格式中非常大,我想知道如何将其作为MATLAB稀疏矩阵导入?
答案 0 :(得分:4)
scipy.io.savemat
以MATLAB兼容格式保存稀疏矩阵:
In [1]: from scipy.io import savemat
In [2]: from scipy import sparse
In [3]: M = sparse.csr_matrix(np.arange(12).reshape(3,4))
In [4]: savemat('temp', {'M':M})
In [8]: x=loadmat('temp.mat')
In [9]: x
Out[9]:
{'M': <3x4 sparse matrix of type '<type 'numpy.int32'>'
with 11 stored elements in Compressed Sparse Column format>,
'__globals__': [],
'__header__': 'MATLAB 5.0 MAT-file Platform: posix, Created on: Mon Sep 8 09:34:54 2014',
'__version__': '1.0'}
In [10]: x['M'].A
Out[10]:
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
请注意,savemat
已将其转换为csc
。它还透明地处理索引起点差异。
在Octave
:
octave:4> load temp.mat
octave:5> M
M =
Compressed Column Sparse (rows = 3, cols = 4, nnz = 11 [92%])
(2, 1) -> 4
(3, 1) -> 8
(1, 2) -> 1
(2, 2) -> 5
...
octave:8> full(M)
ans =
0 1 2 3
4 5 6 7
8 9 10 11
答案 1 :(得分:3)
Matlab和Scipy稀疏矩阵格式兼容。您需要在Scipy中获取矩阵的数据,索引和矩阵大小,并使用它们在Matlab中创建稀疏矩阵。这是一个例子:
from scipy.sparse import csr_matrix
from scipy import array
# create a sparse matrix
row = array([0,0,1,2,2,2])
col = array([0,2,2,0,1,2])
data = array([1,2,3,4,5,6])
mat = csr_matrix( (data,(row,col)), shape=(3,4) )
# get the data, shape and indices
(m,n) = mat.shape
s = mat.data
i = mat.tocoo().row
j = mat.indices
# display the matrix
print mat
打印出来:
(0, 0) 1
(0, 2) 2
(1, 2) 3
(2, 0) 4
(2, 1) 5
(2, 2) 6
使用Python中的m,n,s,i和j值在Matlab中创建一个矩阵:
m = 3;
n = 4;
s = [1, 2, 3, 4, 5, 6];
% Index from 1 in Matlab.
i = [0, 0, 1, 2, 2, 2] + 1;
j = [0, 2, 2, 0, 1, 2] + 1;
S = sparse(i, j, s, m, n, m*n)
它给出相同的矩阵,仅从1开始索引。
(1,1) 1
(3,1) 4
(3,2) 5
(1,3) 2
(2,3) 3
(3,3) 6