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<h1>Module <a href="type_Gpr_interfaces.Sigs.Deriv.Deriv.Optim.Gsl.html">Gpr_interfaces.Sigs.Deriv.Deriv.Optim.Gsl</a></h1>
<pre><span class="keyword">module</span> Gsl: <code class="code">sig</code> <a href="Gpr_interfaces.Sigs.Deriv.Deriv.Optim.Gsl.html">..</a> <code class="code">end</code></pre><div class="info module top">
Optimization with the GNU Scientific library (GSL)<br>
</div>
<hr width="100%">
<pre><span id="EXCEPTIONOptim_exception"><span class="keyword">exception</span> Optim_exception</span> <span class="keyword">of</span> <code class="type">exn</code></pre>
<div class="info ">
<code class="code">Optim_exception exn</code> is raised when an internal exception occurs,
e.g. because GSL fails, or because a callback raised it.<br>
</div>
<pre><span id="VALtrain"><span class="keyword">val</span> train</span> : <code class="type">?step:float -><br> ?tol:float -><br> ?epsabs:float -><br> ?report_trained_model:(iter:int -> <a href="Gpr_interfaces.Sigs.Eval.Trained.html#TYPEt">Gpr_interfaces.Sigs.Eval.Trained.t</a> -> unit) -><br> ?report_gradient_norm:(iter:int -> float -> unit) -><br> ?kernel:Eval.Spec.Kernel.t -><br> ?sigma2:float -><br> ?inducing:Eval.Spec.Inducing.t -><br> ?n_rand_inducing:int -><br> ?learn_sigma2:bool -><br> ?hypers:Gpr_interfaces.Sigs.Deriv.Deriv.Spec.Hyper.t array -><br> inputs:Eval.Spec.Inputs.t -><br> targets:Lacaml.D.vec -> unit -> <a href="Gpr_interfaces.Sigs.Eval.Trained.html#TYPEt">Gpr_interfaces.Sigs.Eval.Trained.t</a></code></pre><div class="info ">
<code class="code">train ?step ?tol ?epsabs ?report_trained_model
?report_gradient_norm ?kernel ?sigma2 ?inducing ?n_rand_inducing
?learn_sigma2 ?hypers ~inputs ~targets ()</code> takes the optional
initial optimizer step size <code class="code">step</code>, the optimizer line search
tolerance <code class="code">tol</code>, the minimum gradient norm <code class="code">epsabs</code> to achieve by
the optimizer, callbacks for reporting intermediate results
<code class="code">report_trained_model</code> and <code class="code">report_gradient_norm</code>, an optional
<code class="code">kernel</code>, noise level <code class="code">sigma2</code>, inducing inputs <code class="code">inducing</code>, number
of randomly chosen inducing inputs <code class="code">n_rand_inducing</code>, a flag for
whether the noise level should be learnt <code class="code">learn_sigma2</code>, an array
of optional hyper parameters <code class="code">hypers</code> which should be optimized,
and the <code class="code">inputs</code> and <code class="code">targets</code>.<br>
<b>Returns</b> the trained model obtained by evidence maximization (=
type II maximum likelihood).<br>
</div>
<div class="param_info"><code class="code">step</code> : default = <code class="code">1e-1</code></div>
<div class="param_info"><code class="code">tol</code> : default = <code class="code">1e-1</code></div>
<div class="param_info"><code class="code">epsabs</code> : default = <code class="code">1e-1</code></div>
<div class="param_info"><code class="code">report_trained_model</code> : default = ignored</div>
<div class="param_info"><code class="code">report_gradient_norm</code> : default = ignored</div>
<div class="param_info"><code class="code">kernel</code> : default = default kernel computed from specification</div>
<div class="param_info"><code class="code">sigma2</code> : default = target variance</div>
<div class="param_info"><code class="code">inducing</code> : default = randomly selected subset of inputs</div>
<div class="param_info"><code class="code">n_rand_inducing</code> : default = 10% of inputs, at most 1000</div>
<div class="param_info"><code class="code">learn_sigma2</code> : default = <code class="code">true</code></div>
<div class="param_info"><code class="code">hypers</code> : default = all hyper parameters</div>
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