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 matr...
Scipy中的稀疏矩阵——Sparse Matrix in Scipy Sparse Matrix Types Block Sparse Row matrix class scipy.sparse.bsr_matrix(arg1, shape=None, dtype=None, copy=False, blocksize=None) The Block Compressed Row (BSR) format is very similar to the Compressed Sparse Row (CSR) format. BSR is appropriate...
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 matr...
>>>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'>'with3storedelements(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'<cl...
2. scipy.sparse的稀疏矩阵类型 2.1 bsr_matrix bsr_matrix(arg1[, shape, dtype, copy, blocksize]) Block Sparse Row matrix >>> '''BSR矩阵中的inptr列表的第i个元素与i+1个元素是储存第i行的数据的列索引以及数据的区间索引,即indices[indptr[i]:indptr[i+1]]为第i行元素的列索引,data[indptr[i...
一、根据坐标col,以及值进行表示生成矩阵。 代码 >>> 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]) >>>csr_matrix((data,(row,col)),shape=(3,3)).toarray()
importnumpy as npfromscipy.sparseimportcsr_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() ...
Pythonscipy.sparse矩阵使用方法 Pythonscipy.sparse矩阵使⽤⽅法 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 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)]) ...
>>> import numpy as np >>> from scipy.sparse import coo_matrix >>> _row = np.array([...
coo_matrix(S)：通过S.tocoo()转换来实现，主要用于稀疏矩阵S的存储。coo_matrix((M, N), [dtype])：根据指定类型dtype生成一个M行N列的空矩阵。coo_matrix((data, (i, j)), [shape=(M, N)])：数据由data、i和j三个数组构成，其中data存储非零数值，i存储对应数据的行索引，j存储对应...