从scipy CSR矩阵索引到numpy数组的最有效方法是使用`toarray()`方法将CSR矩阵转换为numpy数组。CSR矩阵是一种压缩稀疏行矩阵的存储格式,而numpy数组是一种常规的多维数组...
一、csr_matrix函数 from scipy.sparse import csr_matriximport numpy as np# data:代表的是稀疏矩阵中存储的所有元素data = np.array([1,2,3,4,5,6])# indices: 代表的是这6个元素所在的列的位置indices = np.array([0,2,2,0,1,2])# indptr: 游标,每一行起始元素的下标# 1 2|3|4 5 6的下...
import numpy as np from scipy.sparse import csr_matrix # 创建稀疏矩阵 sparse_matrix = csr_matrix([[1, 0, 0], [0, 0, 2], [0, 3, 0]]) # 将稀疏矩阵转换为基于索引的numpy数组 index_based_array = sparse_matrix.toarray() # 打印转换后的数组 print(index_based_array) 输出结果为: ...
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)]) wheredata,row_indandcol_indsatisfy the relationshipa[row_ind[k],col_ind[k]]=data[k]. csr_matrix((data, indices, indptr), [shape=(M, N)]) is t...
changes to the sparsity structure are expensive (consider LIL or DOK) Examples >>> import numpy as np >>> from scipy.sparse import csr_matrix >>> csr_matrix((3, 4), dtype=np.int8).toarray() array([[0, 0, 0, 0], [0, 0, 0, 0], ...
import numpy as np from scipy.sparse import csr_matrix # print(csr_matrix((3, 4), dtype=np.int8).toarray()) # 构建3*4的空矩阵 # [[0 0 0 0] # [0 0 0 0] # [0 0 0 0]] row = np.array([0, 0, 1, 2, 2, 2]) ...
csr_matrix((M, N), [dtype]) 构造一个shape为(M,N)的dtype类型空矩阵 举例如下: importnumpyasnpfromscipy.sparseimportcsr_matriximportnumpyasnpfromscipy.sparseimportcsr_matrixcsr_matrix((3,4),dtype=np.int8).toarray() 输出为: array([[0, 0, 0, 0], ...
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() ...
用numpy.save或保存三个数组,用numpy.savez加载它们numpy.load,然后用以下方法重新创建稀疏矩阵对象:new_csr = csr_matrix((data, indices, indptr), shape=(M, N))因此,例如:def save_sparse_csr(filename, array): np.savez(filename, data=array.data, indices=array.indices, &...
>>> import numpy as np >>> from scipy.sparse import csr_matrix >>> indptr = np.array([0...