【发布时间】：2022-12-29 04:59:03
【问题描述】：
考虑以下代码，我在其中定义了一个函数来计算矩阵的克罗内克积。目标是使用 tensorly 库中的 tensor_dot 进行张量外推算法。
import numpy as np
import numpy.linalg as la
#%pip install tensorly
import pandas as pd
import tensorly as tl
import warnings
from scipy.stats import norm
def matrix_outer (A, B, C ) :
"""
Calculates Kronecker product of Matrices A, B, C
"""
n_A = A.shape[0]
n_B = B.shape[0]
n_C = C.shape[0]
k = A.shape[1]
tensor = tl.zeros(shape=(n_A, n_B, n_C))
for i in range(k):
coef_a = A[:,i]
coef_b = B[:,i]
coef_c = C[:,i]
tensor += tl.tenalg.tensor_dot(tl.tenalg.tensor_dot(coef_a, coef_b).reshape(n_a, n_B), coef_c).reshape(n_A, n_B, n_C)
return tensor
"""
Okay now we've defined our functions.
The next step is data generation.
"""
# Tensor size
R = 4
n_user = 160 # We have 100 units/users. This is a marketing application.
n_prod = 120 # Sales data, for example, of different products
n_time = 100 # Here are our time periods.
# Auxiliary function for normalizing vectors
# https://sparrow.dev/numpy-norm/
normalize_vec = lambda vec: vec/la.norm(vec)
"""
User participation is shown in matrix A.
"""
user_index = np.linspace(-3, 3, num=n_user)
user_bell = normalize_vec(norm.pdf(user_index, loc = 0, scale = 0.5))
user_bell2 = np.roll(user_bell,120)
user_bell3 = np.roll(user_bell,80 )
user_bell4 = np.roll(user_bell,40 )
A = np.c_[user_bell, user_bell2, user_bell3, user_bell4]
"""
Product participation is shown in matrix B.
"""
productl = normalize_vec(np.repeat([1, 2, 3 , 4], 30, axis=0))
product2 = np.roll(productl, 90 )
product3 = np.roll(productl, 60 )
product4 = np.roll(productl, 30 )
B = np.c_[productl, product2, product3, product4]
tseriesl = normalize_vec(0.3*np.sin(np.arange(0, n_time*2, step=2*np.pi/7))[0:n_time]+0.5)
tseries2 = normalize_vec(np.linspace(0, 1, n_time))
tseries3 = normalize_vec(np.repeat([0, 1], n_time/2, axis=0))
tseries4 = normalize_vec(np.repeat([1, 0], [n_time/4, 3*n_time/4], axis=0))
C = np.c_[tseriesl, tseries2, tseries3, tseries4]
#Aggregate Matrices and denoise them
np.random.seed(1512)
data = matrix_outer(A,B, C)
data_noisy= data + np.random.normal(loc=0,scale=0.5*tl.mean(datas), size=(data.shape))
在倒数第二行，Python 抛出一个异常，说
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 14, in matrix_outer
AttributeError: module 'tensorly.tenalg' has no attribute 'tensor_dot'
(1 line skipped)
当我查看 the official documentation 与 tensorly 时，tensorly.tenalg 实际上具有这样的属性。为什么 Python 会出现这个问题？也许我应该使用来自 GitHub 而不是来自 pip 的 tensorly 版本？如果有帮助的话，我的代码来自here，其中代码块 4 定义了感兴趣的函数。
【问题讨论】：