我正在尝试比较测试和训练数据集中存在的特征向量。这些特征向量使用 scikitlearn 库 load_svmlight_file 以稀疏格式存储。两个数据集的特征向量的维度相同。但是，我收到此错误:“具有多个元素的数组的真值不明确。使用 a.any() 或 a.all()。”
为什么会出现此错误？ 我该如何解决？
提前致谢!
from sklearn.datasets import load_svmlight_file
pathToTrainData="../train.txt"
pathToTestData="../test.txt"
X_train,Y_train= load_svmlight_file(pathToTrainData);
X_test,Y_test= load_svmlight_file(pathToTestData);
for ele1 in X_train:
for ele2 in X_test:
if(ele1==ele2):
print "same vector"
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-3-c1f145f984a6> in <module>()
7 for ele1 in X_train:
8 for ele2 in X_test:
----> 9 if(ele1==ele2):
10 print "same vector"
/Users/rkasat/anaconda/lib/python2.7/site-packages/scipy/sparse/base.pyc in __bool__(self)
181 return True if self.nnz == 1 else False
182 else:
--> 183 raise ValueError("The truth value of an array with more than one "
184 "element is ambiguous. Use a.any() or a.all().")
185 __nonzero__ = __bool__
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all().
最佳答案
您可以使用此条件来检查两个稀疏数组是否完全相等，而无需对它们进行加密:
if (ele1 - ele2).nnz == 0:
# Matched, do something ...
nnz 属性给出了稀疏数组中非零元素的数量。
一些简单的测试运行来显示差异:
import numpy as np
from scipy import sparse
A = sparse.rand(10, 1000000).tocsr()
def benchmark1(A):
for s1 in A:
for s2 in A:
if (s1 - s2).nnz == 0:
pass
def benchmark2(A):
for s1 in A:
for s2 in A:
if (s1.toarray() == s2).all() == 0:
pass
%timeit benchmark1(A)
%timeit benchmark2(A)
一些结果:
# Computer 1
10 loops, best of 3: 36.9 ms per loop # with nnz
1 loops, best of 3: 734 ms per loop # with toarray
# Computer 2
10 loops, best of 3: 28 ms per loop
1 loops, best of 3: 312 ms per loop
关于python-2.7 - 如何比较使用 scikit-learn 库 load_svmlight_file 存储的 2 个稀疏矩阵？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/23124403/