Question

var response = this.ElasticClient.Search<ElasticListing>(s => s
            .AllTypes()
            .Query(query => query
                .Bool(b => b
                    .Must(must => must
                        .QueryString(qs => qs.Query(keyword))
                    )
                )                   
            )
            .Size(pageSize)
            .Explain()              
        );

我正在检查稀疏矩阵是否对称但我得到以下错误 - AttributeError：all not found

Answer 1

对于该主题的一些随机探索：

In [77]: from scipy import sparse

制作稀疏矩阵

In [78]: M = sparse.random(100,100,.2, 'csr')
In [79]: M
Out[79]: 
<100x100 sparse matrix of type '<class 'numpy.float64'>'
    with 2000 stored elements in Compressed Sparse Row format>

它不喜欢相等测试 - 它确实会发出警告，原来的2000非零值增加了3倍

In [80]: M==M.T
/usr/local/lib/python3.5/dist-packages/scipy/sparse/compressed.py:226: SparseEfficiencyWarning: Comparing sparse matrices using == is inefficient, try using != instead.
  " != instead.", SparseEfficiencyWarning)
Out[80]: 
<100x100 sparse matrix of type '<class 'numpy.bool_'>'
    with 6436 stored elements in Compressed Sparse Row format>

差异仍会增加非零术语的数量，但不会增加

In [81]: (M-M.T)
Out[81]: 
<100x100 sparse matrix of type '<class 'numpy.float64'>'
    with 3564 stored elements in Compressed Sparse Row format>

Python abs有效，因为它委托给稀疏方法：M.__abs__

In [85]: abs(M-M.T)
Out[85]: 
<100x100 sparse matrix of type '<class 'numpy.float64'>'
    with 3564 stored elements in Compressed Sparse Row format>

如果我们询问有多少是小的另一个警告 - 0的差异都是0：

In [86]: abs(M-M.T)<1e-10
/usr/local/lib/python3.5/dist-packages/scipy/sparse/compressed.py:274: SparseEfficiencyWarning: Comparing a sparse matrix with a scalar greater than zero using < is inefficient, try using >= instead.
  warn(bad_scalar_msg, SparseEfficiencyWarning)
Out[86]: 
<100x100 sparse matrix of type '<class 'numpy.bool_'>'
    with 6436 stored elements in Compressed Sparse Row format>

创建对称matix：

In [87]: Ms = (M+M.T)/2

现在所有条款都很小

In [88]: abs(Ms-Ms.T)<1e-10
/usr/local/lib/python3.5/dist-packages/scipy/sparse/compressed.py:274: SparseEfficiencyWarning: Comparing a sparse matrix with a scalar greater than zero using < is inefficient, try using >= instead.
  warn(bad_scalar_msg, SparseEfficiencyWarning)
Out[88]: 
<100x100 sparse matrix of type '<class 'numpy.bool_'>'
    with 10000 stored elements in Compressed Sparse Row format>

相反，我们可以检查有多少差异过大：

In [89]: abs(Ms-Ms.T)>1e-10
Out[89]: 
<100x100 sparse matrix of type '<class 'numpy.bool_'>'
    with 0 stored elements in Compressed Sparse Row format>
In [90]: abs(M-M.T)>1e-10
Out[90]: 
<100x100 sparse matrix of type '<class 'numpy.bool_'>'
    with 3564 stored elements in Compressed Sparse Row format>

因此矩阵是对称的，如果：

In [94]: (abs(Ms-Ms.T)>1e-10).nnz == 0
Out[94]: True

Answer 2

即使eq-operator在这里工作，它也很密集。与：比较：

import scipy.sparse as sp
import numpy as np
np.random.seed(1)

a = sp.random(5, 5, density=0.5)
blub = a == a.T
print(blub.shape)
(5, 5)

所以不要走这条路。

假设data.attribute可用（至少对于csc），当然还有对称性，我会这样做：

sym_err = a - a.T
sym_check_res = np.all(np.abs(sym_err.data) < 1e-10)  # tune this value

效果：

sym_err.data

的形状为(nnz,)，此处：(12,)（当然sym_err在温和条件下也很稀疏：见下一条注释）

在涉及fp-math的许多用例中，引入的阈值处理是必要的。只有当您知道自己在做什么时才跳过它或降低阈值。

检查对称稀疏矩阵时出错

2 个答案: