<p>为了完整起见,我将演示稀疏矩阵方法:</p>
<pre><code>In [57]: A = np.array([[False,True,True],[False,False,True]])
In [58]: A
Out[58]:
array([[False, True, True],
[False, False, True]])
In [59]: M = sparse.lil_matrix(A)
In [60]: M
Out[60]:
<2x3 sparse matrix of type '<class 'numpy.bool_'>'
with 3 stored elements in LInked List format>
In [61]: M.data
Out[61]: array([list([True, True]), list([True])], dtype=object)
In [62]: M.rows
Out[62]: array([list([1, 2]), list([2])], dtype=object)
</code></pre>
<p>做一个大的稀疏的:</p>
^{pr2}$
<p>粗略测试:</p>
<pre><code>In [66]: arr = BM.A
In [67]: timeit sparse.lil_matrix(arr)
19.5 ms ± 421 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [68]: timeit list(map(np.where,arr))
11 ms ± 55.6 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [69]: %%timeit
...: x,y = np.where(arr)
...: np.split(y, np.where(np.diff(x) != 0)[0] + 1)
...:
13.8 ms ± 24.3 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
</code></pre>
<p>生成<code>csr</code>稀疏格式矩阵的速度更快:</p>
<pre><code>In [70]: timeit sparse.csr_matrix(arr)
2.68 ms ± 120 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [71]: Mr = sparse.csr_matrix(arr)
In [72]: Mr.indices
Out[72]: array([ 3, 6, 15, ..., 8, 16, 11], dtype=int32)
In [73]: Mr.indptr
Out[73]: array([ 0, 1, 1, ..., 9999, 10000, 10000], dtype=int32)
In [74]: np.where(arr)[1]
Out[74]: array([ 3, 6, 15, ..., 8, 16, 11])
</code></pre>
<p>它的<code>indices</code>就像列<code>where</code>,而{<cd4>}则类似于{<cd5>}<code>indices</code>。在</p>