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fjosw 2021-11-07 21:45:25 +00:00
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docs/pyerrors/linalg.html
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@ -121,9 +121,6 @@
<span class="sd"> automatic differentiation to work, all numpy functions have to have</span>
<span class="sd"> the autograd wrapper (use &#39;import autograd.numpy as anp&#39;).</span>
<span class="sd"> data -- list of Obs, e.g. [obs1, obs2, obs3].</span>
<span class="sd"> Keyword arguments</span>
<span class="sd"> -----------------</span>
<span class="sd"> man_grad -- manually supply a list or an array which contains the jacobian</span>
<span class="sd"> of func. Use cautiously, supplying the wrong derivative will</span>
<span class="sd"> not be intercepted.</span>
@ -667,9 +664,6 @@
<span class="sd"> automatic differentiation to work, all numpy functions have to have</span>
<span class="sd"> the autograd wrapper (use &#39;import autograd.numpy as anp&#39;).</span>
<span class="sd"> data -- list of Obs, e.g. [obs1, obs2, obs3].</span>
<span class="sd"> Keyword arguments</span>
<span class="sd"> -----------------</span>
<span class="sd"> man_grad -- manually supply a list or an array which contains the jacobian</span>
<span class="sd"> of func. Use cautiously, supplying the wrong derivative will</span>
<span class="sd"> not be intercepted.</span>
@ -769,13 +763,9 @@ using automatic differentiation.</p>
<li><strong>func -- arbitrary function of the form func(data, **kwargs). For the</strong>: automatic differentiation to work, all numpy functions have to have
the autograd wrapper (use 'import autograd.numpy as anp').</li>
<li><strong>data -- list of Obs, e.g. [obs1, obs2, obs3].</strong></li>
<li><strong>man_grad -- manually supply a list or an array which contains the jacobian</strong>: of func. Use cautiously, supplying the wrong derivative will
not be intercepted.</li>
</ul>
<h6 id="keyword-arguments">Keyword arguments</h6>
<p>man_grad -- manually supply a list or an array which contains the jacobian
of func. Use cautiously, supplying the wrong derivative will
not be intercepted.</p>
</div>