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<h1>Source code for scipy.sparse._csr</h1><div class="highlight"><pre>
<span></span><span class="sd">&quot;&quot;&quot;Compressed Sparse Row matrix format&quot;&quot;&quot;</span>
<span class="n">__docformat__</span> <span class="o">=</span> <span class="s2">&quot;restructuredtext en&quot;</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;csr_array&#39;</span><span class="p">,</span> <span class="s1">&#39;csr_matrix&#39;</span><span class="p">,</span> <span class="s1">&#39;isspmatrix_csr&#39;</span><span class="p">]</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">._matrix</span><span class="w"> </span><span class="kn">import</span> <span class="n">spmatrix</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">._base</span><span class="w"> </span><span class="kn">import</span> <span class="n">_spbase</span><span class="p">,</span> <span class="n">sparray</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">._sparsetools</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span><span class="n">csr_tocsc</span><span class="p">,</span> <span class="n">csr_tobsr</span><span class="p">,</span> <span class="n">csr_count_blocks</span><span class="p">,</span>
<span class="n">get_csr_submatrix</span><span class="p">,</span> <span class="n">csr_sample_values</span><span class="p">)</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">._sputils</span><span class="w"> </span><span class="kn">import</span> <span class="n">upcast</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">._compressed</span><span class="w"> </span><span class="kn">import</span> <span class="n">_cs_matrix</span>
<span class="k">class</span><span class="w"> </span><span class="nc">_csr_base</span><span class="p">(</span><span class="n">_cs_matrix</span><span class="p">):</span>
<span class="n">_format</span> <span class="o">=</span> <span class="s1">&#39;csr&#39;</span>
<span class="n">_allow_nd</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">transpose</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="k">if</span> <span class="n">axes</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">axes</span> <span class="o">!=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Sparse arrays/matrices do not support &quot;</span>
<span class="s2">&quot;an &#39;axes&#39; parameter because swapping &quot;</span>
<span class="s2">&quot;dimensions is the only logical permutation.&quot;</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> <span class="k">if</span> <span class="n">copy</span> <span class="k">else</span> <span class="bp">self</span>
<span class="n">M</span><span class="p">,</span> <span class="n">N</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_csc_container</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">indices</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">indptr</span><span class="p">),</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">N</span><span class="p">,</span> <span class="n">M</span><span class="p">),</span> <span class="n">copy</span><span class="o">=</span><span class="n">copy</span><span class="p">)</span>
<span class="n">transpose</span><span class="o">.</span><span class="vm">__doc__</span> <span class="o">=</span> <span class="n">_spbase</span><span class="o">.</span><span class="n">transpose</span><span class="o">.</span><span class="vm">__doc__</span>
<span class="k">def</span><span class="w"> </span><span class="nf">tolil</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Cannot convert a 1d sparse array to lil format&quot;</span><span class="p">)</span>
<span class="n">lil</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_lil_container</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sum_duplicates</span><span class="p">()</span>
<span class="n">ptr</span><span class="p">,</span><span class="n">ind</span><span class="p">,</span><span class="n">dat</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indptr</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">indices</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span>
<span class="n">rows</span><span class="p">,</span> <span class="n">data</span> <span class="o">=</span> <span class="n">lil</span><span class="o">.</span><span class="n">rows</span><span class="p">,</span> <span class="n">lil</span><span class="o">.</span><span class="n">data</span>
<span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
<span class="n">start</span> <span class="o">=</span> <span class="n">ptr</span><span class="p">[</span><span class="n">n</span><span class="p">]</span>
<span class="n">end</span> <span class="o">=</span> <span class="n">ptr</span><span class="p">[</span><span class="n">n</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span>
<span class="n">rows</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">ind</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="n">end</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
<span class="n">data</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">dat</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="n">end</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
<span class="k">return</span> <span class="n">lil</span>
<span class="n">tolil</span><span class="o">.</span><span class="vm">__doc__</span> <span class="o">=</span> <span class="n">_spbase</span><span class="o">.</span><span class="n">tolil</span><span class="o">.</span><span class="vm">__doc__</span>
<span class="k">def</span><span class="w"> </span><span class="nf">tocsr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="k">if</span> <span class="n">copy</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="n">tocsr</span><span class="o">.</span><span class="vm">__doc__</span> <span class="o">=</span> <span class="n">_spbase</span><span class="o">.</span><span class="n">tocsr</span><span class="o">.</span><span class="vm">__doc__</span>
<span class="k">def</span><span class="w"> </span><span class="nf">tocsc</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Cannot convert a 1d sparse array to csc format&quot;</span><span class="p">)</span>
<span class="n">M</span><span class="p">,</span> <span class="n">N</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span>
<span class="n">idx_dtype</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_index_dtype</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">indptr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">indices</span><span class="p">),</span>
<span class="n">maxval</span><span class="o">=</span><span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nnz</span><span class="p">,</span> <span class="n">M</span><span class="p">))</span>
<span class="n">indptr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">N</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">idx_dtype</span><span class="p">)</span>
<span class="n">indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nnz</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">idx_dtype</span><span class="p">)</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nnz</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">upcast</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">))</span>
<span class="n">csr_tocsc</span><span class="p">(</span><span class="n">M</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">indptr</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">idx_dtype</span><span class="p">),</span>
<span class="bp">self</span><span class="o">.</span><span class="n">indices</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">idx_dtype</span><span class="p">),</span>
<span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">,</span>
<span class="n">indptr</span><span class="p">,</span>
<span class="n">indices</span><span class="p">,</span>
<span class="n">data</span><span class="p">)</span>
<span class="n">A</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_csc_container</span><span class="p">((</span><span class="n">data</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">indptr</span><span class="p">),</span> <span class="n">shape</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">A</span><span class="o">.</span><span class="n">has_sorted_indices</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">return</span> <span class="n">A</span>
<span class="n">tocsc</span><span class="o">.</span><span class="vm">__doc__</span> <span class="o">=</span> <span class="n">_spbase</span><span class="o">.</span><span class="n">tocsc</span><span class="o">.</span><span class="vm">__doc__</span>
<span class="k">def</span><span class="w"> </span><span class="nf">tobsr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">blocksize</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Cannot convert a 1d sparse array to bsr format&quot;</span><span class="p">)</span>
<span class="k">if</span> <span class="n">blocksize</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">._spfuncs</span><span class="w"> </span><span class="kn">import</span> <span class="n">estimate_blocksize</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">tobsr</span><span class="p">(</span><span class="n">blocksize</span><span class="o">=</span><span class="n">estimate_blocksize</span><span class="p">(</span><span class="bp">self</span><span class="p">))</span>
<span class="k">elif</span> <span class="n">blocksize</span> <span class="o">==</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">):</span>
<span class="n">arg1</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">),</span><span class="bp">self</span><span class="o">.</span><span class="n">indices</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">indptr</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bsr_container</span><span class="p">(</span><span class="n">arg1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="n">copy</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">R</span><span class="p">,</span><span class="n">C</span> <span class="o">=</span> <span class="n">blocksize</span>
<span class="n">M</span><span class="p">,</span><span class="n">N</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span>
<span class="k">if</span> <span class="n">R</span> <span class="o">&lt;</span> <span class="mi">1</span> <span class="ow">or</span> <span class="n">C</span> <span class="o">&lt;</span> <span class="mi">1</span> <span class="ow">or</span> <span class="n">M</span> <span class="o">%</span> <span class="n">R</span> <span class="o">!=</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">N</span> <span class="o">%</span> <span class="n">C</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;invalid blocksize </span><span class="si">{</span><span class="n">blocksize</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="n">blks</span> <span class="o">=</span> <span class="n">csr_count_blocks</span><span class="p">(</span><span class="n">M</span><span class="p">,</span><span class="n">N</span><span class="p">,</span><span class="n">R</span><span class="p">,</span><span class="n">C</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">indptr</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">indices</span><span class="p">)</span>
<span class="n">idx_dtype</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_index_dtype</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">indptr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">indices</span><span class="p">),</span>
<span class="n">maxval</span><span class="o">=</span><span class="nb">max</span><span class="p">(</span><span class="n">N</span><span class="o">//</span><span class="n">C</span><span class="p">,</span> <span class="n">blks</span><span class="p">))</span>
<span class="n">indptr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">M</span><span class="o">//</span><span class="n">R</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">idx_dtype</span><span class="p">)</span>
<span class="n">indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">blks</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">idx_dtype</span><span class="p">)</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">blks</span><span class="p">,</span><span class="n">R</span><span class="p">,</span><span class="n">C</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
<span class="n">csr_tobsr</span><span class="p">(</span><span class="n">M</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">R</span><span class="p">,</span> <span class="n">C</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">indptr</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">idx_dtype</span><span class="p">),</span>
<span class="bp">self</span><span class="o">.</span><span class="n">indices</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">idx_dtype</span><span class="p">),</span>
<span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">,</span>
<span class="n">indptr</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">ravel</span><span class="p">())</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bsr_container</span><span class="p">(</span>
<span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">indptr</span><span class="p">),</span> <span class="n">shape</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span>
<span class="p">)</span>
<span class="n">tobsr</span><span class="o">.</span><span class="vm">__doc__</span> <span class="o">=</span> <span class="n">_spbase</span><span class="o">.</span><span class="n">tobsr</span><span class="o">.</span><span class="vm">__doc__</span>
<span class="c1"># these functions are used by the parent class (_cs_matrix)</span>
<span class="c1"># to remove redundancy between csc_matrix and csr_array</span>
<span class="nd">@staticmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_swap</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;swap the members of x if this is a column-oriented matrix</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">x</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">zero</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">type</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">u</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">v</span><span class="p">,</span> <span class="n">d</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">indices</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">):</span>
<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">v</span> <span class="o">-</span> <span class="n">u</span><span class="p">):</span>
<span class="k">yield</span> <span class="n">zero</span>
<span class="k">yield</span> <span class="n">d</span>
<span class="n">u</span> <span class="o">=</span> <span class="n">v</span> <span class="o">+</span> <span class="mi">1</span>
<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">u</span><span class="p">):</span>
<span class="k">yield</span> <span class="n">zero</span>
<span class="k">return</span>
<span class="n">indptr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">indptr</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
<span class="c1"># return 1d (sparray) or 2drow (spmatrix)</span>
<span class="n">shape</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sparray</span><span class="p">)</span> <span class="k">else</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="n">i0</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">i1</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">indptr</span><span class="p">[</span><span class="mi">1</span><span class="p">:]:</span>
<span class="n">indptr</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">i1</span> <span class="o">-</span> <span class="n">i0</span>
<span class="n">indices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indices</span><span class="p">[</span><span class="n">i0</span><span class="p">:</span><span class="n">i1</span><span class="p">]</span>
<span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">i0</span><span class="p">:</span><span class="n">i1</span><span class="p">]</span>
<span class="k">yield</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">((</span><span class="n">data</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">indptr</span><span class="p">),</span> <span class="n">shape</span><span class="o">=</span><span class="n">shape</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">i0</span> <span class="o">=</span> <span class="n">i1</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_getrow</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Returns a copy of row i of the matrix, as a (1 x n)</span>
<span class="sd"> CSR matrix (row vector).</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="k">if</span> <span class="n">i</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">IndexError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;index (</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">) out of range&#39;</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">copy</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">M</span><span class="p">,</span> <span class="n">N</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span>
<span class="n">i</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
<span class="k">if</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">i</span> <span class="o">+=</span> <span class="n">M</span>
<span class="k">if</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">i</span> <span class="o">&gt;=</span> <span class="n">M</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">IndexError</span><span class="p">(</span><span class="s1">&#39;index (</span><span class="si">%d</span><span class="s1">) out of range&#39;</span> <span class="o">%</span> <span class="n">i</span><span class="p">)</span>
<span class="n">indptr</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">data</span> <span class="o">=</span> <span class="n">get_csr_submatrix</span><span class="p">(</span>
<span class="n">M</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">indptr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">indices</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">N</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">((</span><span class="n">data</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">indptr</span><span class="p">),</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">N</span><span class="p">),</span>
<span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_getcol</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Returns a copy of column i. A (m x 1) sparse array (column vector).</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;getcol not provided for 1d arrays. Use indexing A[j]&quot;</span><span class="p">)</span>
<span class="n">M</span><span class="p">,</span> <span class="n">N</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span>
<span class="n">i</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
<span class="k">if</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">i</span> <span class="o">+=</span> <span class="n">N</span>
<span class="k">if</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">i</span> <span class="o">&gt;=</span> <span class="n">N</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">IndexError</span><span class="p">(</span><span class="s1">&#39;index (</span><span class="si">%d</span><span class="s1">) out of range&#39;</span> <span class="o">%</span> <span class="n">i</span><span class="p">)</span>
<span class="n">indptr</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">data</span> <span class="o">=</span> <span class="n">get_csr_submatrix</span><span class="p">(</span>
<span class="n">M</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">indptr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">indices</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">M</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">((</span><span class="n">data</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">indptr</span><span class="p">),</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">M</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
<span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_get_int</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">idx</span><span class="p">):</span>
<span class="n">spot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">flatnonzero</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">indices</span> <span class="o">==</span> <span class="n">idx</span><span class="p">)</span>
<span class="k">if</span> <span class="n">spot</span><span class="o">.</span><span class="n">size</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">spot</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">type</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_get_slice</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">idx</span><span class="p">):</span>
<span class="k">if</span> <span class="n">idx</span> <span class="o">==</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="k">if</span> <span class="n">idx</span><span class="o">.</span><span class="n">step</span> <span class="ow">in</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">):</span>
<span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_submatrix</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">idx</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">return</span> <span class="n">ret</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">ret</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_minor_slice</span><span class="p">(</span><span class="n">idx</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_get_array</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">idx</span><span class="p">):</span>
<span class="n">idx_dtype</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_index_dtype</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">indices</span><span class="p">)</span>
<span class="n">idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">idx_dtype</span><span class="p">)</span>
<span class="k">if</span> <span class="n">idx</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">([],</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
<span class="n">M</span><span class="p">,</span> <span class="n">N</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">row</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">idx_dtype</span><span class="p">)</span>
<span class="n">col</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">idx_dtype</span><span class="p">)</span>
<span class="n">val</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">row</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
<span class="n">csr_sample_values</span><span class="p">(</span><span class="n">M</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">indptr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">indices</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">,</span>
<span class="n">row</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">,</span> <span class="n">val</span><span class="p">)</span>
<span class="n">new_shape</span> <span class="o">=</span> <span class="n">col</span><span class="o">.</span><span class="n">shape</span> <span class="k">if</span> <span class="n">col</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="p">(</span><span class="n">col</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">(</span><span class="n">val</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">new_shape</span><span class="p">))</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_get_intXarray</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_getrow</span><span class="p">(</span><span class="n">row</span><span class="p">)</span><span class="o">.</span><span class="n">_minor_index_fancy</span><span class="p">(</span><span class="n">col</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_get_intXslice</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">):</span>
<span class="k">if</span> <span class="n">col</span><span class="o">.</span><span class="n">step</span> <span class="ow">in</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_submatrix</span><span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="c1"># TODO: uncomment this once it&#39;s faster:</span>
<span class="c1"># return self._getrow(row)._minor_slice(col)</span>
<span class="n">M</span><span class="p">,</span> <span class="n">N</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span>
<span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="p">,</span> <span class="n">stride</span> <span class="o">=</span> <span class="n">col</span><span class="o">.</span><span class="n">indices</span><span class="p">(</span><span class="n">N</span><span class="p">)</span>
<span class="n">ii</span><span class="p">,</span> <span class="n">jj</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indptr</span><span class="p">[</span><span class="n">row</span><span class="p">:</span><span class="n">row</span><span class="o">+</span><span class="mi">2</span><span class="p">]</span>
<span class="n">row_indices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indices</span><span class="p">[</span><span class="n">ii</span><span class="p">:</span><span class="n">jj</span><span class="p">]</span>
<span class="n">row_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">ii</span><span class="p">:</span><span class="n">jj</span><span class="p">]</span>
<span class="k">if</span> <span class="n">stride</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">ind</span> <span class="o">=</span> <span class="p">(</span><span class="n">row_indices</span> <span class="o">&gt;=</span> <span class="n">start</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">row_indices</span> <span class="o">&lt;</span> <span class="n">stop</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">ind</span> <span class="o">=</span> <span class="p">(</span><span class="n">row_indices</span> <span class="o">&lt;=</span> <span class="n">start</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">row_indices</span> <span class="o">&gt;</span> <span class="n">stop</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">abs</span><span class="p">(</span><span class="n">stride</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">ind</span> <span class="o">&amp;=</span> <span class="p">(</span><span class="n">row_indices</span> <span class="o">-</span> <span class="n">start</span><span class="p">)</span> <span class="o">%</span> <span class="n">stride</span> <span class="o">==</span> <span class="mi">0</span>
<span class="n">row_indices</span> <span class="o">=</span> <span class="p">(</span><span class="n">row_indices</span><span class="p">[</span><span class="n">ind</span><span class="p">]</span> <span class="o">-</span> <span class="n">start</span><span class="p">)</span> <span class="o">//</span> <span class="n">stride</span>
<span class="n">row_data</span> <span class="o">=</span> <span class="n">row_data</span><span class="p">[</span><span class="n">ind</span><span class="p">]</span>
<span class="n">row_indptr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">row_indices</span><span class="p">)])</span>
<span class="k">if</span> <span class="n">stride</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">row_data</span> <span class="o">=</span> <span class="n">row_data</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">row_indices</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="n">row_indices</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">stop</span> <span class="o">-</span> <span class="n">start</span><span class="p">)</span> <span class="o">/</span> <span class="n">stride</span><span class="p">))))</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">((</span><span class="n">row_data</span><span class="p">,</span> <span class="n">row_indices</span><span class="p">,</span> <span class="n">row_indptr</span><span class="p">),</span> <span class="n">shape</span><span class="o">=</span><span class="n">shape</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_get_sliceXint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">):</span>
<span class="k">if</span> <span class="n">row</span><span class="o">.</span><span class="n">step</span> <span class="ow">in</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_submatrix</span><span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_major_slice</span><span class="p">(</span><span class="n">row</span><span class="p">)</span><span class="o">.</span><span class="n">_get_submatrix</span><span class="p">(</span><span class="n">minor</span><span class="o">=</span><span class="n">col</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_get_sliceXarray</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_major_slice</span><span class="p">(</span><span class="n">row</span><span class="p">)</span><span class="o">.</span><span class="n">_minor_index_fancy</span><span class="p">(</span><span class="n">col</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_get_arrayXint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">):</span>
<span class="n">res</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_major_index_fancy</span><span class="p">(</span><span class="n">row</span><span class="p">)</span><span class="o">.</span><span class="n">_get_submatrix</span><span class="p">(</span><span class="n">minor</span><span class="o">=</span><span class="n">col</span><span class="p">)</span>
<span class="k">if</span> <span class="n">row</span><span class="o">.</span><span class="n">ndim</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
<span class="k">return</span> <span class="n">res</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">row</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="k">return</span> <span class="n">res</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_get_arrayXslice</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">):</span>
<span class="k">if</span> <span class="n">col</span><span class="o">.</span><span class="n">step</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">):</span>
<span class="n">col</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">*</span><span class="n">col</span><span class="o">.</span><span class="n">indices</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_arrayXarray</span><span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_major_index_fancy</span><span class="p">(</span><span class="n">row</span><span class="p">)</span><span class="o">.</span><span class="n">_get_submatrix</span><span class="p">(</span><span class="n">minor</span><span class="o">=</span><span class="n">col</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_set_int</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">idx</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_set_many</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">idx</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_set_array</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">idx</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">broadcast_to</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">idx</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_set_many</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">idx</span><span class="p">),</span> <span class="n">idx</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">isspmatrix_csr</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Is `x` of csr_matrix type?</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> x</span>
<span class="sd"> object to check for being a csr matrix</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> bool</span>
<span class="sd"> True if `x` is a csr matrix, False otherwise</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; from scipy.sparse import csr_array, csr_matrix, coo_matrix, isspmatrix_csr</span>
<span class="sd"> &gt;&gt;&gt; isspmatrix_csr(csr_matrix([[5]]))</span>
<span class="sd"> True</span>
<span class="sd"> &gt;&gt;&gt; isspmatrix_csr(csr_array([[5]]))</span>
<span class="sd"> False</span>
<span class="sd"> &gt;&gt;&gt; isspmatrix_csr(coo_matrix([[5]]))</span>
<span class="sd"> False</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">csr_matrix</span><span class="p">)</span>
<span class="c1"># This namespace class separates array from matrix with isinstance</span>
<span class="k">class</span><span class="w"> </span><span class="nc">csr_array</span><span class="p">(</span><span class="n">_csr_base</span><span class="p">,</span> <span class="n">sparray</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Compressed Sparse Row array.</span>
<span class="sd"> This can be instantiated in several ways:</span>
<span class="sd"> csr_array(D)</span>
<span class="sd"> where D is a 2-D ndarray</span>
<span class="sd"> csr_array(S)</span>
<span class="sd"> with another sparse array or matrix S (equivalent to S.tocsr())</span>
<span class="sd"> csr_array((M, N), [dtype])</span>
<span class="sd"> to construct an empty array with shape (M, N)</span>
<span class="sd"> dtype is optional, defaulting to dtype=&#39;d&#39;.</span>
<span class="sd"> csr_array((data, (row_ind, col_ind)), [shape=(M, N)])</span>
<span class="sd"> where ``data``, ``row_ind`` and ``col_ind`` satisfy the</span>
<span class="sd"> relationship ``a[row_ind[k], col_ind[k]] = data[k]``.</span>
<span class="sd"> csr_array((data, indices, indptr), [shape=(M, N)])</span>
<span class="sd"> is the standard CSR representation where the column indices for</span>
<span class="sd"> row i are stored in ``indices[indptr[i]:indptr[i+1]]`` and their</span>
<span class="sd"> corresponding values are stored in ``data[indptr[i]:indptr[i+1]]``.</span>
<span class="sd"> If the shape parameter is not supplied, the array dimensions</span>
<span class="sd"> are inferred from the index arrays.</span>
<span class="sd"> Attributes</span>
<span class="sd"> ----------</span>
<span class="sd"> dtype : dtype</span>
<span class="sd"> Data type of the array</span>
<span class="sd"> shape : 2-tuple</span>
<span class="sd"> Shape of the array</span>
<span class="sd"> ndim : int</span>
<span class="sd"> Number of dimensions (this is always 2)</span>
<span class="sd"> nnz</span>
<span class="sd"> size</span>
<span class="sd"> data</span>
<span class="sd"> CSR format data array of the array</span>
<span class="sd"> indices</span>
<span class="sd"> CSR format index array of the array</span>
<span class="sd"> indptr</span>
<span class="sd"> CSR format index pointer array of the array</span>
<span class="sd"> has_sorted_indices</span>
<span class="sd"> has_canonical_format</span>
<span class="sd"> T</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> Sparse arrays can be used in arithmetic operations: they support</span>
<span class="sd"> addition, subtraction, multiplication, division, and matrix power.</span>
<span class="sd"> Advantages of the CSR format</span>
<span class="sd"> - efficient arithmetic operations CSR + CSR, CSR * CSR, etc.</span>
<span class="sd"> - efficient row slicing</span>
<span class="sd"> - fast matrix vector products</span>
<span class="sd"> Disadvantages of the CSR format</span>
<span class="sd"> - slow column slicing operations (consider CSC)</span>
<span class="sd"> - changes to the sparsity structure are expensive (consider LIL or DOK)</span>
<span class="sd"> Canonical Format</span>
<span class="sd"> - Within each row, indices are sorted by column.</span>
<span class="sd"> - There are no duplicate entries.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; import numpy as np</span>
<span class="sd"> &gt;&gt;&gt; from scipy.sparse import csr_array</span>
<span class="sd"> &gt;&gt;&gt; csr_array((3, 4), dtype=np.int8).toarray()</span>
<span class="sd"> array([[0, 0, 0, 0],</span>
<span class="sd"> [0, 0, 0, 0],</span>
<span class="sd"> [0, 0, 0, 0]], dtype=int8)</span>
<span class="sd"> &gt;&gt;&gt; row = np.array([0, 0, 1, 2, 2, 2])</span>
<span class="sd"> &gt;&gt;&gt; col = np.array([0, 2, 2, 0, 1, 2])</span>
<span class="sd"> &gt;&gt;&gt; data = np.array([1, 2, 3, 4, 5, 6])</span>
<span class="sd"> &gt;&gt;&gt; csr_array((data, (row, col)), shape=(3, 3)).toarray()</span>
<span class="sd"> array([[1, 0, 2],</span>
<span class="sd"> [0, 0, 3],</span>
<span class="sd"> [4, 5, 6]])</span>
<span class="sd"> &gt;&gt;&gt; indptr = np.array([0, 2, 3, 6])</span>
<span class="sd"> &gt;&gt;&gt; indices = np.array([0, 2, 2, 0, 1, 2])</span>
<span class="sd"> &gt;&gt;&gt; data = np.array([1, 2, 3, 4, 5, 6])</span>
<span class="sd"> &gt;&gt;&gt; csr_array((data, indices, indptr), shape=(3, 3)).toarray()</span>
<span class="sd"> array([[1, 0, 2],</span>
<span class="sd"> [0, 0, 3],</span>
<span class="sd"> [4, 5, 6]])</span>
<span class="sd"> Duplicate entries are summed together:</span>
<span class="sd"> &gt;&gt;&gt; row = np.array([0, 1, 2, 0])</span>
<span class="sd"> &gt;&gt;&gt; col = np.array([0, 1, 1, 0])</span>
<span class="sd"> &gt;&gt;&gt; data = np.array([1, 2, 4, 8])</span>
<span class="sd"> &gt;&gt;&gt; csr_array((data, (row, col)), shape=(3, 3)).toarray()</span>
<span class="sd"> array([[9, 0, 0],</span>
<span class="sd"> [0, 2, 0],</span>
<span class="sd"> [0, 4, 0]])</span>
<span class="sd"> As an example of how to construct a CSR array incrementally,</span>
<span class="sd"> the following snippet builds a term-document array from texts:</span>
<span class="sd"> &gt;&gt;&gt; docs = [[&quot;hello&quot;, &quot;world&quot;, &quot;hello&quot;], [&quot;goodbye&quot;, &quot;cruel&quot;, &quot;world&quot;]]</span>
<span class="sd"> &gt;&gt;&gt; indptr = [0]</span>
<span class="sd"> &gt;&gt;&gt; indices = []</span>
<span class="sd"> &gt;&gt;&gt; data = []</span>
<span class="sd"> &gt;&gt;&gt; vocabulary = {}</span>
<span class="sd"> &gt;&gt;&gt; for d in docs:</span>
<span class="sd"> ... for term in d:</span>
<span class="sd"> ... index = vocabulary.setdefault(term, len(vocabulary))</span>
<span class="sd"> ... indices.append(index)</span>
<span class="sd"> ... data.append(1)</span>
<span class="sd"> ... indptr.append(len(indices))</span>
<span class="sd"> ...</span>
<span class="sd"> &gt;&gt;&gt; csr_array((data, indices, indptr), dtype=int).toarray()</span>
<span class="sd"> array([[2, 1, 0, 0],</span>
<span class="sd"> [0, 1, 1, 1]])</span>
<span class="sd"> &quot;&quot;&quot;</span>
<div class="viewcode-block" id="csr_matrix">
<a class="viewcode-back" href="../../../api/evennia.contrib.grid.xyzgrid.xymap.html#evennia.contrib.grid.xyzgrid.xymap.csr_matrix">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">csr_matrix</span><span class="p">(</span><span class="n">spmatrix</span><span class="p">,</span> <span class="n">_csr_base</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Compressed Sparse Row matrix.</span>
<span class="sd"> This can be instantiated in several ways:</span>
<span class="sd"> csr_matrix(D)</span>
<span class="sd"> where D is a 2-D ndarray</span>
<span class="sd"> csr_matrix(S)</span>
<span class="sd"> with another sparse array or matrix S (equivalent to S.tocsr())</span>
<span class="sd"> csr_matrix((M, N), [dtype])</span>
<span class="sd"> to construct an empty matrix with shape (M, N)</span>
<span class="sd"> dtype is optional, defaulting to dtype=&#39;d&#39;.</span>
<span class="sd"> csr_matrix((data, (row_ind, col_ind)), [shape=(M, N)])</span>
<span class="sd"> where ``data``, ``row_ind`` and ``col_ind`` satisfy the</span>
<span class="sd"> relationship ``a[row_ind[k], col_ind[k]] = data[k]``.</span>
<span class="sd"> csr_matrix((data, indices, indptr), [shape=(M, N)])</span>
<span class="sd"> is the standard CSR representation where the column indices for</span>
<span class="sd"> row i are stored in ``indices[indptr[i]:indptr[i+1]]`` and their</span>
<span class="sd"> corresponding values are stored in ``data[indptr[i]:indptr[i+1]]``.</span>
<span class="sd"> If the shape parameter is not supplied, the matrix dimensions</span>
<span class="sd"> are inferred from the index arrays.</span>
<span class="sd"> Attributes</span>
<span class="sd"> ----------</span>
<span class="sd"> dtype : dtype</span>
<span class="sd"> Data type of the matrix</span>
<span class="sd"> shape : 2-tuple</span>
<span class="sd"> Shape of the matrix</span>
<span class="sd"> ndim : int</span>
<span class="sd"> Number of dimensions (this is always 2)</span>
<span class="sd"> nnz</span>
<span class="sd"> size</span>
<span class="sd"> data</span>
<span class="sd"> CSR format data array of the matrix</span>
<span class="sd"> indices</span>
<span class="sd"> CSR format index array of the matrix</span>
<span class="sd"> indptr</span>
<span class="sd"> CSR format index pointer array of the matrix</span>
<span class="sd"> has_sorted_indices</span>
<span class="sd"> has_canonical_format</span>
<span class="sd"> T</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> Sparse matrices can be used in arithmetic operations: they support</span>
<span class="sd"> addition, subtraction, multiplication, division, and matrix power.</span>
<span class="sd"> Advantages of the CSR format</span>
<span class="sd"> - efficient arithmetic operations CSR + CSR, CSR * CSR, etc.</span>
<span class="sd"> - efficient row slicing</span>
<span class="sd"> - fast matrix vector products</span>
<span class="sd"> Disadvantages of the CSR format</span>
<span class="sd"> - slow column slicing operations (consider CSC)</span>
<span class="sd"> - changes to the sparsity structure are expensive (consider LIL or DOK)</span>
<span class="sd"> Canonical Format</span>
<span class="sd"> - Within each row, indices are sorted by column.</span>
<span class="sd"> - There are no duplicate entries.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; import numpy as np</span>
<span class="sd"> &gt;&gt;&gt; from scipy.sparse import csr_matrix</span>
<span class="sd"> &gt;&gt;&gt; csr_matrix((3, 4), dtype=np.int8).toarray()</span>
<span class="sd"> array([[0, 0, 0, 0],</span>
<span class="sd"> [0, 0, 0, 0],</span>
<span class="sd"> [0, 0, 0, 0]], dtype=int8)</span>
<span class="sd"> &gt;&gt;&gt; row = np.array([0, 0, 1, 2, 2, 2])</span>
<span class="sd"> &gt;&gt;&gt; col = np.array([0, 2, 2, 0, 1, 2])</span>
<span class="sd"> &gt;&gt;&gt; data = np.array([1, 2, 3, 4, 5, 6])</span>
<span class="sd"> &gt;&gt;&gt; csr_matrix((data, (row, col)), shape=(3, 3)).toarray()</span>
<span class="sd"> array([[1, 0, 2],</span>
<span class="sd"> [0, 0, 3],</span>
<span class="sd"> [4, 5, 6]])</span>
<span class="sd"> &gt;&gt;&gt; indptr = np.array([0, 2, 3, 6])</span>
<span class="sd"> &gt;&gt;&gt; indices = np.array([0, 2, 2, 0, 1, 2])</span>
<span class="sd"> &gt;&gt;&gt; data = np.array([1, 2, 3, 4, 5, 6])</span>
<span class="sd"> &gt;&gt;&gt; csr_matrix((data, indices, indptr), shape=(3, 3)).toarray()</span>
<span class="sd"> array([[1, 0, 2],</span>
<span class="sd"> [0, 0, 3],</span>
<span class="sd"> [4, 5, 6]])</span>
<span class="sd"> Duplicate entries are summed together:</span>
<span class="sd"> &gt;&gt;&gt; row = np.array([0, 1, 2, 0])</span>
<span class="sd"> &gt;&gt;&gt; col = np.array([0, 1, 1, 0])</span>
<span class="sd"> &gt;&gt;&gt; data = np.array([1, 2, 4, 8])</span>
<span class="sd"> &gt;&gt;&gt; csr_matrix((data, (row, col)), shape=(3, 3)).toarray()</span>
<span class="sd"> array([[9, 0, 0],</span>
<span class="sd"> [0, 2, 0],</span>
<span class="sd"> [0, 4, 0]])</span>
<span class="sd"> As an example of how to construct a CSR matrix incrementally,</span>
<span class="sd"> the following snippet builds a term-document matrix from texts:</span>
<span class="sd"> &gt;&gt;&gt; docs = [[&quot;hello&quot;, &quot;world&quot;, &quot;hello&quot;], [&quot;goodbye&quot;, &quot;cruel&quot;, &quot;world&quot;]]</span>
<span class="sd"> &gt;&gt;&gt; indptr = [0]</span>
<span class="sd"> &gt;&gt;&gt; indices = []</span>
<span class="sd"> &gt;&gt;&gt; data = []</span>
<span class="sd"> &gt;&gt;&gt; vocabulary = {}</span>
<span class="sd"> &gt;&gt;&gt; for d in docs:</span>
<span class="sd"> ... for term in d:</span>
<span class="sd"> ... index = vocabulary.setdefault(term, len(vocabulary))</span>
<span class="sd"> ... indices.append(index)</span>
<span class="sd"> ... data.append(1)</span>
<span class="sd"> ... indptr.append(len(indices))</span>
<span class="sd"> ...</span>
<span class="sd"> &gt;&gt;&gt; csr_matrix((data, indices, indptr), dtype=int).toarray()</span>
<span class="sd"> array([[2, 1, 0, 0],</span>
<span class="sd"> [0, 1, 1, 1]])</span>
<span class="sd"> &quot;&quot;&quot;</span></div>
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