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docs/pyerrors/covobs.html
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@ -131,6 +131,16 @@
<span class="k">return</span> <span class="nb">float</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">grad</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cov</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad</span><span class="p">)))</span>
<span class="k">def</span> <span class="nf">_set_cov</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">cov</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot; Set the covariance matrix of the covobs</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> cov : list or array</span>
<span class="sd"> Has to be either of:</span>
<span class="sd"> 0 dimensional number: variance of a single covobs,</span>
<span class="sd"> 1 dimensional list or array of lenght N: variances of multiple covobs</span>
<span class="sd"> 2 dimensional list or array (N x N): Symmetric, positive-semidefinite covariance matrix</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_cov</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="n">cov</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cov</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">N</span> <span class="o">=</span> <span class="mi">1</span>
@ -145,7 +155,26 @@
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;Covariance matrix has to be a 2 dimensional square matrix!&#39;</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</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">N</span><span class="p">):</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">i</span><span class="p">):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cov</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cov</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">i</span><span class="p">]:</span>
<span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;Covariance matrix is non-symmetric for (</span><span class="si">%d</span><span class="s1">, </span><span class="si">%d</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">))</span>
<span class="n">evals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">eigvalsh</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_cov</span><span class="p">)</span>
<span class="k">for</span> <span class="n">ev</span> <span class="ow">in</span> <span class="n">evals</span><span class="p">:</span>
<span class="k">if</span> <span class="n">ev</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;Covariance matrix is not positive-semidefinite!&#39;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_set_grad</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">grad</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot; Set the gradient of the covobs</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> grad : list or array</span>
<span class="sd"> Has to be either of:</span>
<span class="sd"> 0 dimensional number: gradient w.r.t. a single covobs,</span>
<span class="sd"> 1 dimensional list or array of lenght N: gradient w.r.t. multiple covobs</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_grad</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="n">grad</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad</span><span class="o">.</span><span class="n">ndim</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">0</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">_grad</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_grad</span><span class="p">,</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
@ -219,6 +248,16 @@
<span class="k">return</span> <span class="nb">float</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">grad</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cov</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad</span><span class="p">)))</span>
<span class="k">def</span> <span class="nf">_set_cov</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">cov</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot; Set the covariance matrix of the covobs</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> cov : list or array</span>
<span class="sd"> Has to be either of:</span>
<span class="sd"> 0 dimensional number: variance of a single covobs,</span>
<span class="sd"> 1 dimensional list or array of lenght N: variances of multiple covobs</span>
<span class="sd"> 2 dimensional list or array (N x N): Symmetric, positive-semidefinite covariance matrix</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_cov</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="n">cov</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cov</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">N</span> <span class="o">=</span> <span class="mi">1</span>
@ -233,7 +272,26 @@
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;Covariance matrix has to be a 2 dimensional square matrix!&#39;</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</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">N</span><span class="p">):</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">i</span><span class="p">):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cov</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cov</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">i</span><span class="p">]:</span>
<span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;Covariance matrix is non-symmetric for (</span><span class="si">%d</span><span class="s1">, </span><span class="si">%d</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">))</span>
<span class="n">evals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">eigvalsh</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_cov</span><span class="p">)</span>
<span class="k">for</span> <span class="n">ev</span> <span class="ow">in</span> <span class="n">evals</span><span class="p">:</span>
<span class="k">if</span> <span class="n">ev</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;Covariance matrix is not positive-semidefinite!&#39;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_set_grad</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">grad</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot; Set the gradient of the covobs</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> grad : list or array</span>
<span class="sd"> Has to be either of:</span>
<span class="sd"> 0 dimensional number: gradient w.r.t. a single covobs,</span>
<span class="sd"> 1 dimensional list or array of lenght N: gradient w.r.t. multiple covobs</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_grad</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="n">grad</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad</span><span class="o">.</span><span class="n">ndim</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">0</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">_grad</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_grad</span><span class="p">,</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>