python - 使用 scikit-tensor 进行张量分析

Question

我在 scikit-tensor 中使用下面的代码进行 parafac 分解。此代码是 scikit-tensor 的示例。

from sktensor import dtensor, cp_als, parafac2, tucker_hooi
import numpy
import sktensor

T=dtensor(numpy.arange(100).reshape(2, 5,10))
print (type(T))

P, F, D, A, fit, itr, exectimes = parafac2.parafac2(T, 3, init=3, ma_iter=5, conv= 4)

当我运行这段代码时，输出是......

Traceback (most recent call last):
  File "C:/Users/meghdad/PycharmProjects/tensorInPython/dtensor1.py", line 17, in <module>
    P, F, D, A, fit, itr, exectimes = parafac2.parafac2(T, 3, init=3, ma_iter=5, conv= 4)
  File "C:\Anaconda3\lib\site-packages\scikit_tensor-0.1-py3.5.egg\sktensor\parafac2.py", line 50, in parafac2
  File "C:\Anaconda3\lib\site-packages\scikit_tensor-0.1-py3.5.egg\sktensor\parafac2.py", line 113, in __init
UnboundLocalError: local variable 'F' referenced before assignment

我该怎么做才能解决此错误？

score 1 · Accepted Answer

我查看了 0.1 版的源代码。“init”关键字的唯一有效值是“nvecs”或“random”。默认值为“nvecs”。如果您尝试其中任何一个，您将摆脱错误：

P, F, D, A, fit, itr, exectimes = parafac2.parafac2(T, 3, init='nvecs', ma_iter=5, conv= 4)

或者

P, F, D, A, fit, itr, exectimes = parafac2.parafac2(T, 3, init='random', ma_iter=5, conv= 4)

python - 使用 scikit-tensor 进行张量分析

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