blocks|key|1168743|text|使用稀疏格式，它支持高效的索引，如矩阵|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1168744|这是一种增量构造稀疏矩阵的有效结构。
..。
允许有效访问单个元素的O(1)。不允许重复。一旦构造，就可以有效地转换为coo_matrix。|blockquote|1168745|最后一句可以概括为:如果需要，可以有效地转换为所有其他通用格式。|1168746|from+scipy.sparse+import+dok_matrix

M+=+dok_matrix((100,+100))++#+extra+brackets+needed+as+mentioned+in+comments
++++++++++++++++++++++++++++#+thanks+Daniel!
M[0,3]+=+5|code-block|syntax|javascript|1168747|entityMap|0|LINK|mutability|MUTABLE|url|https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.dok_matrix.html#scipy.sparse.dok_matrix^0|H|2|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|V|8|@]|9|@$A|W|B|X|1|Y]]|C|$]]|$1|D|3|E|5|F|7|Z|8|@]|9|@]|C|$]]|$1|G|3|H|5|6|7|10|8|@]|9|@]|C|$]]|$1|I|3|J|5|K|7|11|8|@]|9|@]|C|$L|M]]|$1|N|3|-4|5|6|7|12|8|@]|9|@]|C|$]]]|O|$P|$5|Q|R|S|C|$T|U]]]]

Use a sparse format, which supports efficient indexing, like <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.dok_matrix.html#scipy.sparse.dok_matrix" rel="noreferrer">dok_matrix</a>
<blockquote>
This is an efficient structure for constructing sparse matrices incrementally.
...
Allows for efficient O(1) access of individual elements. Duplicates are not allowed. Can be efficiently converted to a coo_matrix once constructed.
</blockquote>
The last sentence can be generalized to: can be efficiently converted to all the other common formats if needed.
<pre><code>from scipy.sparse import dok_matrix

M = dok_matrix((100, 100)) # extra brackets needed as mentioned in comments
 # thanks Daniel!
M[0,3] = 5
</code></pre>

blocks|key|1168777|text|使用coo_matrix构造此矩阵，使用(data，(row，cols))参数格式：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1168778|In+[2]:+from+scipy+import+sparse
In+[3]:+from+scipy+import+io
In+[4]:+data=np.array([[10,45],[11,12],[4,1]])
In+[5]:+data
Out[5]:+
array([[10,+45],
+++++++[11,+12],
+++++++[+4,++1]])
In+[6]:+rows+=+data[:,0]
In+[7]:+cols+=+data[:,1]
In+[8]:+data+=+np.ones(rows.shape,+dtype=int)
In+[9]:+M+=+sparse.coo_matrix((data,+(rows,+cols)),+shape=(100,100))
In+[10]:+M
Out[10]:+
<100x100+sparse+matrix+of+type+'<class+'numpy.int32'>'
++++with+3+stored+elements+in+COOrdinate+format>
In+[11]:+print(M)
++(10,+45)++1
++(11,+12)++1
++(4,+1)++++1|code-block|syntax|javascript|1168779|如果您将它保存到一个.mat文件中，以便在MATLAB中使用，它将以csc格式保存它(已经从coo中转换了它)：|1168780|In+[13]:+io.savemat('test.mat',{'M':M})
In+[14]:+d+=+io.loadmat('test.mat')
In+[15]:+d
Out[15]:+
{'M':+<100x100+sparse+matrix+of+type+'<class+'numpy.int32'>'
++++with+3+stored+elements+in+Compressed+Sparse+Column+format>,
+'__globals__':+[],
+'__header__':+b'MATLAB+5.0+MAT-file+Platform:+posix,+Created+on:+Mon+Aug++7+08:45:12+2017',
+'__version__':+'1.0'}|1168781|coo格式不实现项分配。csr和csc确实实现了它，但会抱怨。但它们是计算的正常格式。lil和dok是迭代分配的最佳格式。|1168782|entityMap^0|2|A|0|0|Y|3|1A|3|0|0|0|3|C|3|G|3|17|3|1B|3|0^^$0|@$1|2|3|4|5|6|7|S|8|@$9|T|A|U|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|V|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|W|8|@$9|X|A|Y|B|C]|$9|Z|A|10|B|C]]|D|@]|E|$]]|$1|M|3|N|5|H|7|11|8|@]|D|@]|E|$I|J]]|$1|O|3|P|5|6|7|12|8|@$9|13|A|14|B|C]|$9|15|A|16|B|C]|$9|17|A|18|B|C]|$9|19|A|1A|B|C]|$9|1B|A|1C|B|C]]|D|@]|E|$]]|$1|Q|3|-4|5|6|7|1D|8|@]|D|@]|E|$]]]|R|$]]

Constructing this matrix with <code>coo_matrix</code>, using the (data, (rows, cols))` parameter format:

<pre><code>In [2]: from scipy import sparse
In [3]: from scipy import io
In [4]: data=np.array([[10,45],[11,12],[4,1]])
In [5]: data
Out[5]: 
array([[10, 45],
 [11, 12],
 [ 4, 1]])
In [6]: rows = data[:,0]
In [7]: cols = data[:,1]
In [8]: data = np.ones(rows.shape, dtype=int)
In [9]: M = sparse.coo_matrix((data, (rows, cols)), shape=(100,100))
In [10]: M
Out[10]: 
&lt;100x100 sparse matrix of type '&lt;class 'numpy.int32'&gt;'
 with 3 stored elements in COOrdinate format&gt;
In [11]: print(M)
 (10, 45) 1
 (11, 12) 1
 (4, 1) 1
</code></pre>

If you save it to a .mat file for use in MATLAB, it will save it in <code>csc</code> format (having converted it from the <code>coo</code>):

<pre><code>In [13]: io.savemat('test.mat',{'M':M})
In [14]: d = io.loadmat('test.mat')
In [15]: d
Out[15]: 
{'M': &lt;100x100 sparse matrix of type '&lt;class 'numpy.int32'&gt;'
 with 3 stored elements in Compressed Sparse Column format&gt;,
 '__globals__': [],
 '__header__': b'MATLAB 5.0 MAT-file Platform: posix, Created on: Mon Aug 7 08:45:12 2017',
 '__version__': '1.0'}
</code></pre>

<code>coo</code> format does not implement item assignment. <code>csr</code> and <code>csc</code> do implement it, but will complain. But they are the normal formats for calculation. <code>lil</code> and <code>dok</code> are the best formats for iterative assignment.

I am trying to parse some real data into a <code>.mat</code> object to be loaded in my <a href="/questions/tagged/matlab" class="post-tag" title="show questions tagged &#39;matlab&#39;" rel="tag">matlab</a> script.

I am getting this error:

<blockquote>
 TypeError: 'coo_matrix' object does not support item assignment
</blockquote>

I found <a href="https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.sparse.coo_matrix.html#scipy.sparse.coo_matrix" rel="noreferrer">coo_matrix</a>. However, I am not able to assign values to it.

data.txt

<pre><code>10 45
11 12 
4 1
</code></pre>

I would like to get a sparse matrix of size 100x100. And to assign 1's to

<pre><code>Mat(10, 45) = 1
Mat(11, 12) = 1
Mat(4, 1) = 1
</code></pre>

<hr>

CODE

<pre><code>import numpy as np
from scipy.sparse import coo_matrix

def pdata(pathToFile):
 M = coo_matrix(100, 100)
 with open(pathToFile) as f:
 for line in f:
 s = line.split()
 x, y = [int(v) for v in s]
 M[x, y] = 1 
 return M

if __name__ == "__main__":
 M = pdata('small.txt') 
</code></pre>

Any suggestions please ?

Python create an empty sparse matrix

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我试图将一些真实的数据解析到一个.mat对象中，以便在我的脚本中加载。我得到了这个错误：TypeError：'coo_matrix‘对象不支持项分配我找到了。但是，我不能给它赋值。data.txt10 4511 12 4 1我想要一个大小为100x100的稀疏矩阵。并分配1给Mat(10, 45) = 1Mat(11,...

问Python创建一个空的稀疏矩阵
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