print(bsr_matrix(array).toarray()) print(coo_matrix(array).toarray()) print(csc_matrix(array).toarray()) print(csr_matrix(array).toarray()) print(dia_matrix(array).toarray()) print(dok_matrix(array).toarray()) ''' [[4 0 9 0] [0 7 0 0] [0 0 0 0] [0 0 0 5]] [[...
csr_matrix同样由3个数组组成,values存储非0元素,column indices存储非0元素的列坐标,row offsets依次存储每行的首元素在values中的坐标,如果某行全是0则对应的row offsets值为-1。 这种方式的优点: 1.高效地按行切片 2.快速地计算矩阵与向量的内积 3.高效地进行矩阵的算术运行,CSR + CSR、CSR * CSR等 缺...
Another use case is that if each matrix corresponds to the “adjacency” matrix in a bipartite graph, then concatenating matrices means merging two bipartite graphs that share the column vertices but have different row vertices. For both use cases, CSR representation is quite natural so I think ...
self).__init__(aggr='add')self.lin=torch.nn.Linear(in_channels,out_channels)defforward(self,x,edge_index):# x has shape [N, in_channels]# edge_index has shape [2, E]# Step 1: Add self-loops to the adjacency matrix.edge_index,_=add_self_loops(edge_index...
scipy.sparse.coo_matrix是三元组,不能按行也不能按列切片 to_csr 是按行压缩的稀疏矩阵,按行切片比较快,可以按列切片 to_csc 是按列压缩的稀疏矩阵,按列切片比较快,可以按行切片 这篇文章介绍了稀疏矩阵的COO和CSR存储方式:https://blog.csdn.net/u012101561/article/details/90348288 ...
scipy.sparse.coo_matrix是三元组,不能按行也不能按列切片 to_csr 是按行压缩的稀疏矩阵,按行切片比较快,可以按列切片 to_csc 是按列压缩的稀疏矩阵,按列切片比较快,可以按行切片 这篇文章介绍了稀疏矩阵的COO和CSR存储方式:https://blog.csdn.net/u012101561/article/details/90348288 ...
from pathlib import Path # 引入提升路径的兼容性# 引入矩阵运算的相关库import numpy as npimport pandas as pdfrom scipy.sparse import coo_matrix,csr_matrix,diags,eye# 引入深度学习框架库import torchfrom torch import nnimport torch.nn.functional as F# 引入绘图库import matplotlib.pyplot as pltimport ...
•生成特征矩阵:读取点特征文件之后,通过scipy.sparse中的csr_matrix压缩存储特征数据,然后进行行归一化,最后转换成torch中的tensor格式 •生成标签:抽取点特征文件中的相应数据,然后使用函数encode_onehot生成每一行是独热向量的矩阵,然后再转换成torch中的tensor格式 •生成邻接矩阵:从边关系文件中抽取相应的关系,...
🐛 Describe the bug import torch device = torch.device("cuda:0") a = torch.tensor([ [ [1.0, 0.0], [2.0, 1.0] ], [ [0.1, 0.1], [0.0, 2.0], ] ]).to_sparse_csr().to(device) b = torch.randn(2, 2).to(device) print(torch.matmul(a, b)) This lead...
在PyTorch中,密集矩阵(dense matrix)和稀疏矩阵(sparse matrix)是两种不同的数据结构,用于存储和处理不同类型的数据。密集矩阵是一个二维数组,其中大部分元素都是非零的;而...