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<h1>Module <a href="type_Gpr.Interfaces.html">Gpr.Interfaces</a></h1>
<pre><span class="keyword">module</span> Interfaces: <code class="type"><a href="Gpr_interfaces.html">Gpr_interfaces</a></code></pre><hr width="100%">
<pre><span class="keyword">module</span> <a href="Gpr_interfaces.Sparse_indices.html">Sparse_indices</a>: <code class="type"><a href="Gpr_utils.Int_vec.html">Gpr_utils.Int_vec</a></code></pre><div class="info">
Representation of indices into sparse matrices
</div>
<pre><span id="TYPEcommon_mat_deriv"><span class="keyword">type</span> <code class="type"></code>common_mat_deriv</span> = <code class="type">[ `Const of float<br> | `Dense of Lacaml.D.mat<br> | `Factor of float<br> | `Sparse_rows of Lacaml.D.mat * Sparse_indices.t ]</code> </pre>
<div class="info ">
Derivative representations for both symmetric and unsymmetric matrices.
<p>
<ul>
<li>Dense: matrix is dense.</li>
<li>Sparse_rows: matrix is zero everywhere except for rows whose
index is stored in the sparse index argument. The rows in the
matrix correspond to the given indices.</li>
<li>Const: matrix is constant everywhere.</li>
<li>Factor: matrix is the non-derived matrix times the given factor
(useful with exponential functions).</li>
</ul>
<br>
</div>
<pre><span id="TYPEmat_deriv"><span class="keyword">type</span> <code class="type"></code>mat_deriv</span> = <code class="type">[ `Const of float<br> | `Dense of Lacaml.D.mat<br> | `Factor of float<br> | `Sparse_cols of Lacaml.D.mat * Sparse_indices.t<br> | `Sparse_rows of Lacaml.D.mat * Sparse_indices.t ]</code> </pre>
<div class="info ">
Only general matrices support sparse column representations.
<p>
<ul>
<li>Sparse_cols: matrix is zero everywhere except for columns whose
index is stored in the sparse index argument. The columns in
the matrix correspond to the given indices.</li>
</ul>
<br>
</div>
<pre><span id="TYPEsymm_mat_deriv"><span class="keyword">type</span> <code class="type"></code>symm_mat_deriv</span> = <code class="type">[ `Const of float<br> | `Dense of Lacaml.D.mat<br> | `Diag_const of float<br> | `Diag_vec of Lacaml.D.vec<br> | `Factor of float<br> | `Sparse_rows of Lacaml.D.mat * Sparse_indices.t ]</code> </pre>
<div class="info ">
Only symmetric (square) matrices support diagonal vectors and
diagonal constants as derivatives.
<p>
<ul>
<li>Diag_vec: matrix is zero everywhere except for the diagonal
whose values are given in the argument.</li>
<li>Diag_const: matrix is zero everywhere except for the diagonal
whose values are set to the given constant.</li>
</ul>
Note that sparse rows do not need to compute or store all elements
for symmetric matrices. Entries that have already appeared in
previous rows by symmetry can be left uninitialized.<br>
</div>
<pre><span id="TYPEdiag_deriv"><span class="keyword">type</span> <code class="type"></code>diag_deriv</span> = <code class="type">[ `Const of float<br> | `Factor of float<br> | `Sparse_vec of Lacaml.D.vec * Sparse_indices.t<br> | `Vec of Lacaml.D.vec ]</code> </pre>
<div class="info ">
Derivatives of diagonal matrices.
<p>
<ul>
<li>Vec: the derivatives of the diagonal given in a dense vector.</li>
<li>Sparse_vec: matrix is zero everywhere except at those indices
along the diagonal that are mentioned in the sparse indices
argument. The element associated with such an index is stored
in the vector argument.</li>
<li>Const: the derivative of the diagonal matrix is a constant.</li>
<li>Factor: the derivative of the diagonal is the the non-derived
diagonal matrix times the given factor (useful with exponential
functions).</li>
</ul>
<br>
</div>
<pre><span class="keyword">module</span> <a href="Gpr_interfaces.Specs.html">Specs</a>: <code class="code">sig</code> <a href="Gpr_interfaces.Specs.html">..</a> <code class="code">end</code></pre><div class="info">
Specifications of covariance functions (= kernels) and their derivatives
</div>
<pre><span class="keyword">module</span> <a href="Gpr_interfaces.Sigs.html">Sigs</a>: <code class="code">sig</code> <a href="Gpr_interfaces.Sigs.html">..</a> <code class="code">end</code></pre><div class="info">
Signatures for learning sparse Gaussian processes with inducing inputs
</div>
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