torch.matmul(tensor1, tensor2, out=None) → Tensor torch.matrix_power(input, n) → Tensor torch.matrix_rank(input, tol=None, bool symmetric=False) → Tensor torch.mm(mat1, mat2, out=None) → Tensor torch.mv(mat,
torch.matrix_power 是一个用于计算矩阵的幂(即矩阵自乘若干次)的函数 torch.matrix_power(input, n) input: 一个二维张量,表示要进行幂运算的矩阵。矩阵的形状必须是 (n,m) ,即一个方阵 n: 一个整数,表示矩阵幂的指数。可以是正整数、负整数或零 返回值:返回一个新的张量,表示输入矩阵 input 的n 次幂...
emb_user.shape[0], emb_anime.shape[0]) predicted = create_sparse_matrix(predict(df, emb_user, emb_anime), emb_user.shape[0], emb_anime.shape[0], 'prediction') return np.sum((Y-predicted).power(2))/df.shape[0]预测
矩阵乘法的成本很高,我假设特征分解和对角线化将加快整个过程。但是令我惊讶的是,这种据说改进的方法花费了更多时间。我在这里错了吗? import timeit mysetup = ''' import numpy as np from numpy import linalg as LA from numpy.linalg import matrix_power EXP = 5 # no. of time linear transformation i...
matrix_power(self, n) max(self, dim=None, keepdim=False) maximum(self, other) mean(self, dim=None, keepdim=False, *args, **kwargs) median(self, dim=None, keepdim=False) min(self, dim=None, keepdim=False) minimum(self, other) ...
>>> T_2 = torch.matrix_power(T, 2) >>> T_5 = torch.matrix_power(T, 5) >>> T_10 = torch.matrix_power(T, 10) >>> T_15 = torch.matrix_power(T, 15) >>> T_20 = torch.matrix_power(T, 20) 定义两个状态的初始分布: >>> v = torch.tensor([[0.7, 0.3]]) 在步骤...
🐛 Describe the bug A runtime error occurs when using the torch._C._linalg.linalg_matrix_power function withtorch.compile mode. The function works as expected outside of torch.compile, but raises an exception when compiled with specific s...
'matrix_power', 'max', 'mean', 'median', 'min', 'mm', 'mode', 'mul', 'mul_', 'multinomial', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'narrow', 'narrow_copy', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'nelement', 'new', 'new_empty', 'new_full', 'new_ones...
matrix_exp=torch.tensor([[1,2],[3,4]],dtype=torch.float)print(matrix_exp.matrix_power(3))#上面等价于矩阵做3次自身乘法torch.mm(matrix_exp,matrix_exp).mm(matrix_exp) tensor([[ 37., 54.], [ 81., 118.]]) tensor([[ 37., 54.], [ 81., 118.]]) # 向量点乘(相当于把其中...
tensor初始化 #定义一个tensor my_tensor=torch.tensor([[1,2,3],[4,5,6]]) print(my_tensor) tensor([[1, 2, 3], [4, 5, 6]]) #指定tensor的数据类型 my_tensor=torch.tensor([[1,2,3],[4,5,6]],dtype=torch.float32) print(my_tensor) ...