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<div class="section" id="gpy-core-package">
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<h1>GPy.core package<a class="headerlink" href="#gpy-core-package" title="Permalink to this headline">¶</a></h1>
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<div class="section" id="subpackages">
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<h2>Subpackages<a class="headerlink" href="#subpackages" title="Permalink to this headline">¶</a></h2>
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<div class="toctree-wrapper compound">
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<ul>
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<li class="toctree-l1"><a class="reference internal" href="GPy.core.parameterization.html">GPy.core.parameterization package</a><ul>
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<li class="toctree-l2"><a class="reference internal" href="GPy.core.parameterization.html#submodules">Submodules</a></li>
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<li class="toctree-l2"><a class="reference internal" href="GPy.core.parameterization.html#module-GPy.core.parameterization.domains">GPy.core.parameterization.domains module</a></li>
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||||
<li class="toctree-l2"><a class="reference internal" href="GPy.core.parameterization.html#module-GPy.core.parameterization.index_operations">GPy.core.parameterization.index_operations module</a></li>
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||||
<li class="toctree-l2"><a class="reference internal" href="GPy.core.parameterization.html#module-GPy.core.parameterization.lists_and_dicts">GPy.core.parameterization.lists_and_dicts module</a></li>
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<li class="toctree-l2"><a class="reference internal" href="GPy.core.parameterization.html#module-GPy.core.parameterization.observable_array">GPy.core.parameterization.observable_array module</a></li>
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<li class="toctree-l2"><a class="reference internal" href="GPy.core.parameterization.html#module-GPy.core.parameterization.param">GPy.core.parameterization.param module</a></li>
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<li class="toctree-l2"><a class="reference internal" href="GPy.core.parameterization.html#module-GPy.core.parameterization.parameter_core">GPy.core.parameterization.parameter_core module</a></li>
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<li class="toctree-l2"><a class="reference internal" href="GPy.core.parameterization.html#module-GPy.core.parameterization.parameterized">GPy.core.parameterization.parameterized module</a></li>
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<li class="toctree-l2"><a class="reference internal" href="GPy.core.parameterization.html#module-GPy.core.parameterization.priors">GPy.core.parameterization.priors module</a></li>
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<li class="toctree-l2"><a class="reference internal" href="GPy.core.parameterization.html#module-GPy.core.parameterization.ties_and_remappings">GPy.core.parameterization.ties_and_remappings module</a></li>
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<li class="toctree-l2"><a class="reference internal" href="GPy.core.parameterization.html#module-GPy.core.parameterization.transformations">GPy.core.parameterization.transformations module</a></li>
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<li class="toctree-l2"><a class="reference internal" href="GPy.core.parameterization.html#module-GPy.core.parameterization.variational">GPy.core.parameterization.variational module</a></li>
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<li class="toctree-l2"><a class="reference internal" href="GPy.core.parameterization.html#module-GPy.core.parameterization">Module contents</a></li>
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</ul>
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</li>
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</ul>
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</div>
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</div>
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<div class="section" id="submodules">
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<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
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</div>
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<div class="section" id="module-GPy.core.gp">
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<span id="gpy-core-gp-module"></span><h2>GPy.core.gp module<a class="headerlink" href="#module-GPy.core.gp" title="Permalink to this headline">¶</a></h2>
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<dl class="class">
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<dt id="GPy.core.gp.GP">
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<em class="property">class </em><code class="descclassname">GPy.core.gp.</code><code class="descname">GP</code><span class="sig-paren">(</span><em>X</em>, <em>Y</em>, <em>kernel</em>, <em>likelihood</em>, <em>inference_method=None</em>, <em>name='gp'</em>, <em>Y_metadata=None</em>, <em>normalizer=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/gp.html#GP"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.gp.GP" title="Permalink to this definition">¶</a></dt>
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<dd><p>Bases: <a class="reference internal" href="#GPy.core.model.Model" title="GPy.core.model.Model"><code class="xref py py-class docutils literal"><span class="pre">GPy.core.model.Model</span></code></a></p>
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<p>General purpose Gaussian process model</p>
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<table class="docutils field-list" frame="void" rules="none">
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<col class="field-name" />
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<col class="field-body" />
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<tbody valign="top">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
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<li><strong>X</strong> – input observations</li>
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<li><strong>Y</strong> – output observations</li>
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<li><strong>kernel</strong> – a GPy kernel, defaults to rbf+white</li>
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<li><strong>likelihood</strong> – a GPy likelihood</li>
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<li><strong>inference_method</strong> – The <a class="reference internal" href="GPy.inference.latent_function_inference.html#GPy.inference.latent_function_inference.LatentFunctionInference" title="GPy.inference.latent_function_inference.LatentFunctionInference"><code class="xref py py-class docutils literal"><span class="pre">LatentFunctionInference</span></code></a> inference method to use for this GP</li>
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<li><strong>normalizer</strong> (<a class="reference internal" href="GPy.util.html#GPy.util.normalizer.Norm" title="GPy.util.normalizer.Norm"><em>Norm</em></a>) – normalize the outputs Y.
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Prediction will be un-normalized using this normalizer.
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If normalizer is None, we will normalize using MeanNorm.
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If normalizer is False, no normalization will be done.</li>
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</ul>
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</td>
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</tr>
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<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">model object</p>
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</td>
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</tr>
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</tbody>
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</table>
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<div class="admonition note">
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<p class="first admonition-title">Note</p>
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<p class="last">Multiple independent outputs are allowed using columns of Y</p>
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</div>
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<dl class="method">
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<dt id="GPy.core.gp.GP.infer_newX">
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<code class="descname">infer_newX</code><span class="sig-paren">(</span><em>Y_new</em>, <em>optimize=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/gp.html#GP.infer_newX"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.gp.GP.infer_newX" title="Permalink to this definition">¶</a></dt>
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<dd><p>Infer the distribution of X for the new observed data <em>Y_new</em>.</p>
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<table class="docutils field-list" frame="void" rules="none">
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<col class="field-name" />
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<col class="field-body" />
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<tbody valign="top">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
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<li><strong>Y_new</strong> (<em>numpy.ndarray</em>) – the new observed data for inference</li>
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<li><strong>optimize</strong> (<em>boolean</em>) – whether to optimize the location of new X (True by default)</li>
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</ul>
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</td>
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</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">a tuple containing the posterior estimation of X and the model that optimize X</p>
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</td>
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</tr>
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<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">(<a class="reference internal" href="GPy.core.parameterization.html#GPy.core.parameterization.variational.VariationalPosterior" title="GPy.core.parameterization.variational.VariationalPosterior"><code class="xref py py-class docutils literal"><span class="pre">VariationalPosterior</span></code></a> or numpy.ndarray, <a class="reference internal" href="#GPy.core.model.Model" title="GPy.core.model.Model"><code class="xref py py-class docutils literal"><span class="pre">Model</span></code></a>)</p>
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</td>
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</tr>
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</tbody>
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</table>
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</dd></dl>
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<dl class="method">
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<dt id="GPy.core.gp.GP.input_sensitivity">
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<code class="descname">input_sensitivity</code><span class="sig-paren">(</span><em>summarize=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/gp.html#GP.input_sensitivity"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.gp.GP.input_sensitivity" title="Permalink to this definition">¶</a></dt>
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<dd><p>Returns the sensitivity for each dimension of this model</p>
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</dd></dl>
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<dl class="method">
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<dt id="GPy.core.gp.GP.log_likelihood">
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<code class="descname">log_likelihood</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/gp.html#GP.log_likelihood"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.gp.GP.log_likelihood" title="Permalink to this definition">¶</a></dt>
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<dd><p>The log marginal likelihood of the model, <img class="math" src="_images/math/836d46125c08b336c780001fd9b6cfa2ecd6f6d6.png" alt="p(\mathbf{y})"/>, this is the objective function of the model being optimised</p>
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</dd></dl>
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<dl class="method">
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<dt id="GPy.core.gp.GP.optimize">
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<code class="descname">optimize</code><span class="sig-paren">(</span><em>optimizer=None</em>, <em>start=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/gp.html#GP.optimize"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.gp.GP.optimize" title="Permalink to this definition">¶</a></dt>
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<dd><p>Optimize the model using self.log_likelihood and self.log_likelihood_gradient, as well as self.priors.
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kwargs are passed to the optimizer. They can be:</p>
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<table class="docutils field-list" frame="void" rules="none">
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<col class="field-name" />
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<col class="field-body" />
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<tbody valign="top">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
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<li><strong>max_f_eval</strong> (<em>int</em>) – maximum number of function evaluations</li>
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<li><strong>optimizer</strong> (<em>string</em>) – which optimizer to use (defaults to self.preferred optimizer), a range of optimisers can be found in <a href="#id1"><span class="problematic" id="id2">:module:`~GPy.inference.optimization`</span></a>, they include ‘scg’, ‘lbfgs’, ‘tnc’.</li>
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</ul>
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</td>
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</tr>
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<tr class="field-even field"><th class="field-name">Messages:</th><td class="field-body"><p class="first last">whether to display during optimisation</p>
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</td>
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</tr>
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</tbody>
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</table>
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</dd></dl>
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<dl class="method">
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<dt id="GPy.core.gp.GP.parameters_changed">
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<code class="descname">parameters_changed</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/gp.html#GP.parameters_changed"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.gp.GP.parameters_changed" title="Permalink to this definition">¶</a></dt>
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<dd><p>Method that is called upon any changes to <a class="reference internal" href="GPy.core.parameterization.html#GPy.core.parameterization.param.Param" title="GPy.core.parameterization.param.Param"><code class="xref py py-class docutils literal"><span class="pre">Param</span></code></a> variables within the model.
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In particular in the GP class this method reperforms inference, recalculating the posterior and log marginal likelihood and gradients of the model</p>
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<div class="admonition warning">
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<p class="first admonition-title">Warning</p>
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<p class="last">This method is not designed to be called manually, the framework is set up to automatically call this method upon changes to parameters, if you call
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this method yourself, there may be unexpected consequences.</p>
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</div>
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</dd></dl>
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<dl class="method">
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<dt id="GPy.core.gp.GP.plot">
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<code class="descname">plot</code><span class="sig-paren">(</span><em>plot_limits=None</em>, <em>which_data_rows='all'</em>, <em>which_data_ycols='all'</em>, <em>fixed_inputs=[]</em>, <em>levels=20</em>, <em>samples=0</em>, <em>fignum=None</em>, <em>ax=None</em>, <em>resolution=None</em>, <em>plot_raw=False</em>, <em>linecol=None</em>, <em>fillcol=None</em>, <em>Y_metadata=None</em>, <em>data_symbol='kx'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/gp.html#GP.plot"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.gp.GP.plot" title="Permalink to this definition">¶</a></dt>
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<dd><dl class="docutils">
|
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<dt>Plot the posterior of the GP.</dt>
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<dd><ul class="first last simple">
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<li>In one dimension, the function is plotted with a shaded region identifying two standard deviations.</li>
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<li>In two dimsensions, a contour-plot shows the mean predicted function</li>
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<li>In higher dimensions, use fixed_inputs to plot the GP with some of the inputs fixed.</li>
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</ul>
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</dd>
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</dl>
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<p>Can plot only part of the data and part of the posterior functions
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using which_data_rowsm which_data_ycols.</p>
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<table class="docutils field-list" frame="void" rules="none">
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<col class="field-name" />
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<col class="field-body" />
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<tbody valign="top">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
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<li><strong>plot_limits</strong> (<em>np.array</em>) – The limits of the plot. If 1D [xmin,xmax], if 2D [[xmin,ymin],[xmax,ymax]]. Defaluts to data limits</li>
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<li><strong>which_data_rows</strong> (<em>‘all’ or a slice object to slice model.X, model.Y</em>) – which of the training data to plot (default all)</li>
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<li><strong>which_data_ycols</strong> (<em>‘all’ or a list of integers</em>) – when the data has several columns (independant outputs), only plot these</li>
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<li><strong>fixed_inputs</strong> (<em>a list of tuples</em>) – a list of tuple [(i,v), (i,v)...], specifying that input index i should be set to value v.</li>
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<li><strong>resolution</strong> (<em>int</em>) – the number of intervals to sample the GP on. Defaults to 200 in 1D and 50 (a 50x50 grid) in 2D</li>
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<li><strong>levels</strong> (<em>int</em>) – number of levels to plot in a contour plot.</li>
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<li><strong>levels</strong> – for 2D plotting, the number of contour levels to use is ax is None, create a new figure</li>
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<li><strong>samples</strong> (<em>int</em>) – the number of a posteriori samples to plot</li>
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<li><strong>fignum</strong> (<em>figure number</em>) – figure to plot on.</li>
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<li><strong>ax</strong> (<em>axes handle</em>) – axes to plot on.</li>
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<li><strong>linecol</strong> (<em>color either as Tango.colorsHex object or character (‘r’ is red, ‘g’ is green) as is standard in matplotlib</em>) – color of line to plot [Tango.colorsHex[‘darkBlue’]]</li>
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<li><strong>fillcol</strong> (<em>color either as Tango.colorsHex object or character (‘r’ is red, ‘g’ is green) as is standard in matplotlib</em>) – color of fill [Tango.colorsHex[‘lightBlue’]]</li>
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<li><strong>Y_metadata</strong> (<em>dict</em>) – additional data associated with Y which may be needed</li>
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<li><strong>data_symbol</strong> (<em>color either as Tango.colorsHex object or character (‘r’ is red, ‘g’ is green) alongside marker type, as is standard in matplotlib.</em>) – symbol as used matplotlib, by default this is a black cross (‘kx’)</li>
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</ul>
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</td>
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</tr>
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</tbody>
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</table>
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</dd></dl>
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<dl class="method">
|
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<dt id="GPy.core.gp.GP.plot_f">
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<code class="descname">plot_f</code><span class="sig-paren">(</span><em>plot_limits=None</em>, <em>which_data_rows='all'</em>, <em>which_data_ycols='all'</em>, <em>fixed_inputs=[]</em>, <em>levels=20</em>, <em>samples=0</em>, <em>fignum=None</em>, <em>ax=None</em>, <em>resolution=None</em>, <em>plot_raw=True</em>, <em>linecol=None</em>, <em>fillcol=None</em>, <em>Y_metadata=None</em>, <em>data_symbol='kx'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/gp.html#GP.plot_f"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.gp.GP.plot_f" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Plot the GP’s view of the world, where the data is normalized and before applying a likelihood.
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This is a call to plot with plot_raw=True.
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Data will not be plotted in this, as the GP’s view of the world
|
||||
may live in another space, or units then the data.</p>
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<p>Can plot only part of the data and part of the posterior functions
|
||||
using which_data_rowsm which_data_ycols.</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
|
||||
<li><strong>plot_limits</strong> (<em>np.array</em>) – The limits of the plot. If 1D [xmin,xmax], if 2D [[xmin,ymin],[xmax,ymax]]. Defaluts to data limits</li>
|
||||
<li><strong>which_data_rows</strong> (<em>‘all’ or a slice object to slice model.X, model.Y</em>) – which of the training data to plot (default all)</li>
|
||||
<li><strong>which_data_ycols</strong> (<em>‘all’ or a list of integers</em>) – when the data has several columns (independant outputs), only plot these</li>
|
||||
<li><strong>fixed_inputs</strong> (<em>a list of tuples</em>) – a list of tuple [(i,v), (i,v)...], specifying that input index i should be set to value v.</li>
|
||||
<li><strong>resolution</strong> (<em>int</em>) – the number of intervals to sample the GP on. Defaults to 200 in 1D and 50 (a 50x50 grid) in 2D</li>
|
||||
<li><strong>levels</strong> (<em>int</em>) – number of levels to plot in a contour plot.</li>
|
||||
<li><strong>levels</strong> – for 2D plotting, the number of contour levels to use is ax is None, create a new figure</li>
|
||||
<li><strong>samples</strong> (<em>int</em>) – the number of a posteriori samples to plot</li>
|
||||
<li><strong>fignum</strong> (<em>figure number</em>) – figure to plot on.</li>
|
||||
<li><strong>ax</strong> (<em>axes handle</em>) – axes to plot on.</li>
|
||||
<li><strong>linecol</strong> (<em>color either as Tango.colorsHex object or character (‘r’ is red, ‘g’ is green) as is standard in matplotlib</em>) – color of line to plot [Tango.colorsHex[‘darkBlue’]]</li>
|
||||
<li><strong>fillcol</strong> (<em>color either as Tango.colorsHex object or character (‘r’ is red, ‘g’ is green) as is standard in matplotlib</em>) – color of fill [Tango.colorsHex[‘lightBlue’]]</li>
|
||||
<li><strong>Y_metadata</strong> (<em>dict</em>) – additional data associated with Y which may be needed</li>
|
||||
<li><strong>data_symbol</strong> (<em>color either as Tango.colorsHex object or character (‘r’ is red, ‘g’ is green) alongside marker type, as is standard in matplotlib.</em>) – symbol as used matplotlib, by default this is a black cross (‘kx’)</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.core.gp.GP.posterior_samples">
|
||||
<code class="descname">posterior_samples</code><span class="sig-paren">(</span><em>X</em>, <em>size=10</em>, <em>full_cov=False</em>, <em>Y_metadata=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/gp.html#GP.posterior_samples"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.gp.GP.posterior_samples" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Samples the posterior GP at the points X.</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>X</strong> (<em>np.ndarray (Nnew x self.input_dim.)</em>) – the points at which to take the samples.</li>
|
||||
<li><strong>size</strong> (<em>int.</em>) – the number of a posteriori samples.</li>
|
||||
<li><strong>full_cov</strong> (<em>bool.</em>) – whether to return the full covariance matrix, or just the diagonal.</li>
|
||||
<li><strong>noise_model</strong> (<em>integer.</em>) – for mixed noise likelihood, the noise model to use in the samples.</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">Ysim: set of simulations, a Numpy array (N x samples).</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.core.gp.GP.posterior_samples_f">
|
||||
<code class="descname">posterior_samples_f</code><span class="sig-paren">(</span><em>X</em>, <em>size=10</em>, <em>full_cov=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/gp.html#GP.posterior_samples_f"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.gp.GP.posterior_samples_f" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Samples the posterior GP at the points X.</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>X</strong> (<em>np.ndarray (Nnew x self.input_dim)</em>) – The points at which to take the samples.</li>
|
||||
<li><strong>size</strong> (<em>int.</em>) – the number of a posteriori samples.</li>
|
||||
<li><strong>full_cov</strong> (<em>bool.</em>) – whether to return the full covariance matrix, or just the diagonal.</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">Ysim: set of simulations</p>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">np.ndarray (N x samples)</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.core.gp.GP.predict">
|
||||
<code class="descname">predict</code><span class="sig-paren">(</span><em>Xnew</em>, <em>full_cov=False</em>, <em>Y_metadata=None</em>, <em>kern=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/gp.html#GP.predict"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.gp.GP.predict" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Predict the function(s) at the new point(s) Xnew.</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>Xnew</strong> (<em>np.ndarray (Nnew x self.input_dim)</em>) – The points at which to make a prediction</li>
|
||||
<li><strong>full_cov</strong> (<em>bool</em>) – whether to return the full covariance matrix, or just
|
||||
the diagonal</li>
|
||||
<li><strong>Y_metadata</strong> – metadata about the predicting point to pass to the likelihood</li>
|
||||
<li><strong>kern</strong> – The kernel to use for prediction (defaults to the model
|
||||
kern). this is useful for examining e.g. subprocesses.</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><dl class="docutils">
|
||||
<dt>(mean, var, lower_upper):</dt>
|
||||
<dd><p class="first last">mean: posterior mean, a Numpy array, Nnew x self.input_dim
|
||||
var: posterior variance, a Numpy array, Nnew x 1 if full_cov=False, Nnew x Nnew otherwise
|
||||
lower_upper: lower and upper boundaries of the 95% confidence intervals, Numpy arrays, Nnew x self.input_dim</p>
|
||||
</dd>
|
||||
</dl>
|
||||
<p>If full_cov and self.input_dim > 1, the return shape of var is Nnew x Nnew x self.input_dim. If self.input_dim == 1, the return shape is Nnew x Nnew.
|
||||
This is to allow for different normalizations of the output dimensions.</p>
|
||||
</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.core.gp.GP.predict_quantiles">
|
||||
<code class="descname">predict_quantiles</code><span class="sig-paren">(</span><em>X</em>, <em>quantiles=(2.5</em>, <em>97.5)</em>, <em>Y_metadata=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/gp.html#GP.predict_quantiles"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.gp.GP.predict_quantiles" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Get the predictive quantiles around the prediction at X</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>X</strong> (<em>np.ndarray (Xnew x self.input_dim)</em>) – The points at which to make a prediction</li>
|
||||
<li><strong>quantiles</strong> (<em>tuple</em>) – tuple of quantiles, default is (2.5, 97.5) which is the 95% interval</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">list of quantiles for each X and predictive quantiles for interval combination</p>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">[np.ndarray (Xnew x self.input_dim), np.ndarray (Xnew x self.input_dim)]</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.core.gp.GP.predictive_gradients">
|
||||
<code class="descname">predictive_gradients</code><span class="sig-paren">(</span><em>Xnew</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/gp.html#GP.predictive_gradients"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.gp.GP.predictive_gradients" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Compute the derivatives of the latent function with respect to X*</p>
|
||||
<p>Given a set of points at which to predict X* (size [N*,Q]), compute the
|
||||
derivatives of the mean and variance. Resulting arrays are sized:</p>
|
||||
<blockquote>
|
||||
<div><p>dmu_dX* – [N*, Q ,D], where D is the number of output in this GP (usually one).</p>
|
||||
<p>dv_dX* – [N*, Q], (since all outputs have the same variance)</p>
|
||||
</div></blockquote>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>X</strong> (<em>np.ndarray (Xnew x self.input_dim)</em>) – The points at which to get the predictive gradients</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">dmu_dX, dv_dX</td>
|
||||
</tr>
|
||||
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body">[np.ndarray (N*, Q ,D), np.ndarray (N*,Q) ]</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.core.gp.GP.set_X">
|
||||
<code class="descname">set_X</code><span class="sig-paren">(</span><em>X</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/gp.html#GP.set_X"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.gp.GP.set_X" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Set the input data of the model</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>X</strong> (<em>np.ndarray</em>) – input observations</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.core.gp.GP.set_XY">
|
||||
<code class="descname">set_XY</code><span class="sig-paren">(</span><em>X=None</em>, <em>Y=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/gp.html#GP.set_XY"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.gp.GP.set_XY" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Set the input / output data of the model
|
||||
This is useful if we wish to change our existing data but maintain the same model</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
|
||||
<li><strong>X</strong> (<em>np.ndarray</em>) – input observations</li>
|
||||
<li><strong>Y</strong> (<em>np.ndarray</em>) – output observations</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.core.gp.GP.set_Y">
|
||||
<code class="descname">set_Y</code><span class="sig-paren">(</span><em>Y</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/gp.html#GP.set_Y"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.gp.GP.set_Y" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Set the output data of the model</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>X</strong> (<em>np.ndarray</em>) – output observations</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-GPy.core.mapping">
|
||||
<span id="gpy-core-mapping-module"></span><h2>GPy.core.mapping module<a class="headerlink" href="#module-GPy.core.mapping" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="class">
|
||||
<dt id="GPy.core.mapping.Bijective_mapping">
|
||||
<em class="property">class </em><code class="descclassname">GPy.core.mapping.</code><code class="descname">Bijective_mapping</code><span class="sig-paren">(</span><em>input_dim</em>, <em>output_dim</em>, <em>name='bijective_mapping'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/mapping.html#Bijective_mapping"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.mapping.Bijective_mapping" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#GPy.core.mapping.Mapping" title="GPy.core.mapping.Mapping"><code class="xref py py-class docutils literal"><span class="pre">GPy.core.mapping.Mapping</span></code></a></p>
|
||||
<p>This is a mapping that is bijective, i.e. you can go from X to f and
|
||||
also back from f to X. The inverse mapping is called g().</p>
|
||||
<dl class="method">
|
||||
<dt id="GPy.core.mapping.Bijective_mapping.g">
|
||||
<code class="descname">g</code><span class="sig-paren">(</span><em>f</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/mapping.html#Bijective_mapping.g"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.mapping.Bijective_mapping.g" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Inverse mapping from output domain of the function to the inputs.</p>
|
||||
</dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="class">
|
||||
<dt id="GPy.core.mapping.Mapping">
|
||||
<em class="property">class </em><code class="descclassname">GPy.core.mapping.</code><code class="descname">Mapping</code><span class="sig-paren">(</span><em>input_dim</em>, <em>output_dim</em>, <em>name='mapping'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/mapping.html#Mapping"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.mapping.Mapping" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="GPy.core.parameterization.html#GPy.core.parameterization.parameterized.Parameterized" title="GPy.core.parameterization.parameterized.Parameterized"><code class="xref py py-class docutils literal"><span class="pre">GPy.core.parameterization.parameterized.Parameterized</span></code></a></p>
|
||||
<p>Base model for shared behavior between models that can act like a mapping.</p>
|
||||
<dl class="method">
|
||||
<dt id="GPy.core.mapping.Mapping.df_dX">
|
||||
<code class="descname">df_dX</code><span class="sig-paren">(</span><em>dL_df</em>, <em>X</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/mapping.html#Mapping.df_dX"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.mapping.Mapping.df_dX" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Evaluate derivatives of mapping outputs with respect to inputs.</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>dL_df</strong> (<em>ndarray (num_data x output_dim)</em>) – gradient of the objective with respect to the function.</li>
|
||||
<li><strong>X</strong> (<em>ndarray (num_data x input_dim)</em>) – the input locations where derivatives are to be evaluated.</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">matrix containing gradients of the function with respect to the inputs.</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.core.mapping.Mapping.df_dtheta">
|
||||
<code class="descname">df_dtheta</code><span class="sig-paren">(</span><em>dL_df</em>, <em>X</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/mapping.html#Mapping.df_dtheta"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.mapping.Mapping.df_dtheta" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>The gradient of the outputs of the mapping with respect to each of the parameters.</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>dL_df</strong> (<em>ndarray (num_data x output_dim)</em>) – gradient of the objective with respect to the function.</li>
|
||||
<li><strong>X</strong> (<em>ndarray (num_data x input_dim)</em>) – input locations where the function is evaluated.</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">Matrix containing gradients with respect to parameters of each output for each input data.</p>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">ndarray (num_params length)</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.core.mapping.Mapping.f">
|
||||
<code class="descname">f</code><span class="sig-paren">(</span><em>X</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/mapping.html#Mapping.f"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.mapping.Mapping.f" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.core.mapping.Mapping.plot">
|
||||
<code class="descname">plot</code><span class="sig-paren">(</span><em>*args</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/mapping.html#Mapping.plot"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.mapping.Mapping.plot" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><dl class="docutils">
|
||||
<dt>Plots the mapping associated with the model.</dt>
|
||||
<dd><ul class="first last simple">
|
||||
<li>In one dimension, the function is plotted.</li>
|
||||
<li>In two dimensions, a contour-plot shows the function</li>
|
||||
<li>In higher dimensions, we’ve not implemented this yet !TODO!</li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
<p>Can plot only part of the data and part of the posterior functions
|
||||
using which_data and which_functions</p>
|
||||
<p>This is a convenience function: arguments are passed to
|
||||
GPy.plotting.matplot_dep.models_plots.plot_mapping</p>
|
||||
</dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="class">
|
||||
<dt id="GPy.core.mapping.Mapping_check_df_dX">
|
||||
<em class="property">class </em><code class="descclassname">GPy.core.mapping.</code><code class="descname">Mapping_check_df_dX</code><span class="sig-paren">(</span><em>mapping=None</em>, <em>dL_df=None</em>, <em>X=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/mapping.html#Mapping_check_df_dX"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.mapping.Mapping_check_df_dX" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#GPy.core.mapping.Mapping_check_model" title="GPy.core.mapping.Mapping_check_model"><code class="xref py py-class docutils literal"><span class="pre">GPy.core.mapping.Mapping_check_model</span></code></a></p>
|
||||
<p>This class allows gradient checks for the gradient of a mapping with respect to X.</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="class">
|
||||
<dt id="GPy.core.mapping.Mapping_check_df_dtheta">
|
||||
<em class="property">class </em><code class="descclassname">GPy.core.mapping.</code><code class="descname">Mapping_check_df_dtheta</code><span class="sig-paren">(</span><em>mapping=None</em>, <em>dL_df=None</em>, <em>X=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/mapping.html#Mapping_check_df_dtheta"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.mapping.Mapping_check_df_dtheta" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#GPy.core.mapping.Mapping_check_model" title="GPy.core.mapping.Mapping_check_model"><code class="xref py py-class docutils literal"><span class="pre">GPy.core.mapping.Mapping_check_model</span></code></a></p>
|
||||
<p>This class allows gradient checks for the gradient of a mapping with respect to parameters.</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="class">
|
||||
<dt id="GPy.core.mapping.Mapping_check_model">
|
||||
<em class="property">class </em><code class="descclassname">GPy.core.mapping.</code><code class="descname">Mapping_check_model</code><span class="sig-paren">(</span><em>mapping=None</em>, <em>dL_df=None</em>, <em>X=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/mapping.html#Mapping_check_model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.mapping.Mapping_check_model" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#GPy.core.model.Model" title="GPy.core.model.Model"><code class="xref py py-class docutils literal"><span class="pre">GPy.core.model.Model</span></code></a></p>
|
||||
<p>This is a dummy model class used as a base class for checking that the
|
||||
gradients of a given mapping are implemented correctly. It enables
|
||||
checkgradient() to be called independently on each mapping.</p>
|
||||
<dl class="method">
|
||||
<dt id="GPy.core.mapping.Mapping_check_model.log_likelihood">
|
||||
<code class="descname">log_likelihood</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/mapping.html#Mapping_check_model.log_likelihood"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.mapping.Mapping_check_model.log_likelihood" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-GPy.core.model">
|
||||
<span id="gpy-core-model-module"></span><h2>GPy.core.model module<a class="headerlink" href="#module-GPy.core.model" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="class">
|
||||
<dt id="GPy.core.model.Model">
|
||||
<em class="property">class </em><code class="descclassname">GPy.core.model.</code><code class="descname">Model</code><span class="sig-paren">(</span><em>name</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/model.html#Model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.model.Model" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="GPy.core.parameterization.html#GPy.core.parameterization.parameterized.Parameterized" title="GPy.core.parameterization.parameterized.Parameterized"><code class="xref py py-class docutils literal"><span class="pre">GPy.core.parameterization.parameterized.Parameterized</span></code></a></p>
|
||||
<dl class="method">
|
||||
<dt id="GPy.core.model.Model.ensure_default_constraints">
|
||||
<code class="descname">ensure_default_constraints</code><span class="sig-paren">(</span><em>warning=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/model.html#Model.ensure_default_constraints"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.model.Model.ensure_default_constraints" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Ensure that any variables which should clearly be positive
|
||||
have been constrained somehow. The method performs a regular
|
||||
expression search on parameter names looking for the terms
|
||||
‘variance’, ‘lengthscale’, ‘precision’ and ‘kappa’. If any of
|
||||
these terms are present in the name the parameter is
|
||||
constrained positive.</p>
|
||||
<p>DEPRECATED.</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.core.model.Model.log_likelihood">
|
||||
<code class="descname">log_likelihood</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/model.html#Model.log_likelihood"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.model.Model.log_likelihood" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.core.model.Model.objective_function">
|
||||
<code class="descname">objective_function</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/model.html#Model.objective_function"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.model.Model.objective_function" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>The objective function for the given algorithm.</p>
|
||||
<p>This function is the true objective, which wants to be minimized.
|
||||
Note that all parameters are already set and in place, so you just need
|
||||
to return the objective function here.</p>
|
||||
<p>For probabilistic models this is the negative log_likelihood
|
||||
(including the MAP prior), so we return it here. If your model is not
|
||||
probabilistic, just return your objective to minimize here!</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.core.model.Model.objective_function_gradients">
|
||||
<code class="descname">objective_function_gradients</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/model.html#Model.objective_function_gradients"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.model.Model.objective_function_gradients" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>The gradients for the objective function for the given algorithm.
|
||||
The gradients are w.r.t. the <em>negative</em> objective function, as
|
||||
this framework works with <em>negative</em> log-likelihoods as a default.</p>
|
||||
<p>You can find the gradient for the parameters in self.gradient at all times.
|
||||
This is the place, where gradients get stored for parameters.</p>
|
||||
<p>This function is the true objective, which wants to be minimized.
|
||||
Note that all parameters are already set and in place, so you just need
|
||||
to return the gradient here.</p>
|
||||
<p>For probabilistic models this is the gradient of the negative log_likelihood
|
||||
(including the MAP prior), so we return it here. If your model is not
|
||||
probabilistic, just return your <em>negative</em> gradient here!</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.core.model.Model.optimize">
|
||||
<code class="descname">optimize</code><span class="sig-paren">(</span><em>optimizer=None</em>, <em>start=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/model.html#Model.optimize"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.model.Model.optimize" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Optimize the model using self.log_likelihood and self.log_likelihood_gradient, as well as self.priors.</p>
|
||||
<p>kwargs are passed to the optimizer. They can be:</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>max_f_eval</strong> (<em>int</em>) – maximum number of function evaluations</li>
|
||||
<li><strong>optimizer</strong> (<em>string</em>) – which optimizer to use (defaults to self.preferred optimizer)</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Messages:</th><td class="field-body"><p class="first last">whether to display during optimisation</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<dl class="docutils">
|
||||
<dt>Valid optimizers are:</dt>
|
||||
<dd><ul class="first last">
|
||||
<li><dl class="first docutils">
|
||||
<dt>‘scg’: scaled conjugate gradient method, recommended for stability.</dt>
|
||||
<dd><p class="first last">See also GPy.inference.optimization.scg</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</li>
|
||||
<li><p class="first">‘fmin_tnc’: truncated Newton method (see scipy.optimize.fmin_tnc)</p>
|
||||
</li>
|
||||
<li><p class="first">‘simplex’: the Nelder-Mead simplex method (see scipy.optimize.fmin),</p>
|
||||
</li>
|
||||
<li><p class="first">‘lbfgsb’: the l-bfgs-b method (see scipy.optimize.fmin_l_bfgs_b),</p>
|
||||
</li>
|
||||
<li><p class="first">‘sgd’: stochastic gradient decsent (see scipy.optimize.sgd). For experts only!</p>
|
||||
</li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.core.model.Model.optimize_SGD">
|
||||
<code class="descname">optimize_SGD</code><span class="sig-paren">(</span><em>momentum=0.1</em>, <em>learning_rate=0.01</em>, <em>iterations=20</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/model.html#Model.optimize_SGD"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.model.Model.optimize_SGD" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.core.model.Model.optimize_restarts">
|
||||
<code class="descname">optimize_restarts</code><span class="sig-paren">(</span><em>num_restarts=10</em>, <em>robust=False</em>, <em>verbose=True</em>, <em>parallel=False</em>, <em>num_processes=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/model.html#Model.optimize_restarts"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.model.Model.optimize_restarts" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Perform random restarts of the model, and set the model to the best
|
||||
seen solution.</p>
|
||||
<p>If the robust flag is set, exceptions raised during optimizations will
|
||||
be handled silently. If _all_ runs fail, the model is reset to the
|
||||
existing parameter values.</p>
|
||||
<p><strong>Notes</strong></p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
|
||||
<li><strong>num_restarts</strong> (<em>int</em>) – number of restarts to use (default 10)</li>
|
||||
<li><strong>robust</strong> (<em>bool</em>) – whether to handle exceptions silently or not (default False)</li>
|
||||
<li><strong>parallel</strong> (<em>bool</em>) – whether to run each restart as a separate process. It relies on the multiprocessing module.</li>
|
||||
<li><strong>num_processes</strong> – number of workers in the multiprocessing pool</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<p>**kwargs are passed to the optimizer. They can be:</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
|
||||
<li><strong>max_f_eval</strong> (<em>int</em>) – maximum number of function evaluations</li>
|
||||
<li><strong>max_iters</strong> (<em>int</em>) – maximum number of iterations</li>
|
||||
<li><strong>messages</strong> (<em>bool</em>) – whether to display during optimisation</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<div class="admonition note">
|
||||
<p class="first admonition-title">Note</p>
|
||||
<p class="last">If num_processes is None, the number of workes in the</p>
|
||||
</div>
|
||||
<p>multiprocessing pool is automatically set to the number of processors
|
||||
on the current machine.</p>
|
||||
</dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-GPy.core.sparse_gp">
|
||||
<span id="gpy-core-sparse-gp-module"></span><h2>GPy.core.sparse_gp module<a class="headerlink" href="#module-GPy.core.sparse_gp" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="class">
|
||||
<dt id="GPy.core.sparse_gp.SparseGP">
|
||||
<em class="property">class </em><code class="descclassname">GPy.core.sparse_gp.</code><code class="descname">SparseGP</code><span class="sig-paren">(</span><em>X</em>, <em>Y</em>, <em>Z</em>, <em>kernel</em>, <em>likelihood</em>, <em>inference_method=None</em>, <em>name='sparse gp'</em>, <em>Y_metadata=None</em>, <em>normalizer=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/sparse_gp.html#SparseGP"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.sparse_gp.SparseGP" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#GPy.core.gp.GP" title="GPy.core.gp.GP"><code class="xref py py-class docutils literal"><span class="pre">GPy.core.gp.GP</span></code></a></p>
|
||||
<p>A general purpose Sparse GP model</p>
|
||||
<p>This model allows (approximate) inference using variational DTC or FITC
|
||||
(Gaussian likelihoods) as well as non-conjugate sparse methods based on
|
||||
these.</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
|
||||
<li><strong>X</strong> (<em>np.ndarray (num_data x input_dim)</em>) – inputs</li>
|
||||
<li><strong>likelihood</strong> (<em>GPy.likelihood.(Gaussian | EP | Laplace)</em>) – a likelihood instance, containing the observed data</li>
|
||||
<li><strong>kernel</strong> (<em>a GPy.kern.kern instance</em>) – the kernel (covariance function). See link kernels</li>
|
||||
<li><strong>X_variance</strong> (<em>np.ndarray (num_data x input_dim) | None</em>) – The uncertainty in the measurements of X (Gaussian variance)</li>
|
||||
<li><strong>Z</strong> (<em>np.ndarray (num_inducing x input_dim)</em>) – inducing inputs</li>
|
||||
<li><strong>num_inducing</strong> (<em>int</em>) – Number of inducing points (optional, default 10. Ignored if Z is not None)</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<dl class="method">
|
||||
<dt id="GPy.core.sparse_gp.SparseGP.has_uncertain_inputs">
|
||||
<code class="descname">has_uncertain_inputs</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/sparse_gp.html#SparseGP.has_uncertain_inputs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.sparse_gp.SparseGP.has_uncertain_inputs" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.core.sparse_gp.SparseGP.parameters_changed">
|
||||
<code class="descname">parameters_changed</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/sparse_gp.html#SparseGP.parameters_changed"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.sparse_gp.SparseGP.parameters_changed" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-GPy.core.sparse_gp_mpi">
|
||||
<span id="gpy-core-sparse-gp-mpi-module"></span><h2>GPy.core.sparse_gp_mpi module<a class="headerlink" href="#module-GPy.core.sparse_gp_mpi" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="class">
|
||||
<dt id="GPy.core.sparse_gp_mpi.SparseGP_MPI">
|
||||
<em class="property">class </em><code class="descclassname">GPy.core.sparse_gp_mpi.</code><code class="descname">SparseGP_MPI</code><span class="sig-paren">(</span><em>X</em>, <em>Y</em>, <em>Z</em>, <em>kernel</em>, <em>likelihood</em>, <em>variational_prior=None</em>, <em>inference_method=None</em>, <em>name='sparse gp mpi'</em>, <em>Y_metadata=None</em>, <em>mpi_comm=None</em>, <em>normalizer=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/sparse_gp_mpi.html#SparseGP_MPI"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.sparse_gp_mpi.SparseGP_MPI" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#GPy.core.sparse_gp.SparseGP" title="GPy.core.sparse_gp.SparseGP"><code class="xref py py-class docutils literal"><span class="pre">GPy.core.sparse_gp.SparseGP</span></code></a></p>
|
||||
<p>A general purpose Sparse GP model with MPI parallelization support</p>
|
||||
<p>This model allows (approximate) inference using variational DTC or FITC
|
||||
(Gaussian likelihoods) as well as non-conjugate sparse methods based on
|
||||
these.</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
|
||||
<li><strong>X</strong> (<em>np.ndarray (num_data x input_dim)</em>) – inputs</li>
|
||||
<li><strong>likelihood</strong> (<em>GPy.likelihood.(Gaussian | EP | Laplace)</em>) – a likelihood instance, containing the observed data</li>
|
||||
<li><strong>kernel</strong> (<em>a GPy.kern.kern instance</em>) – the kernel (covariance function). See link kernels</li>
|
||||
<li><strong>X_variance</strong> (<em>np.ndarray (num_data x input_dim) | None</em>) – The uncertainty in the measurements of X (Gaussian variance)</li>
|
||||
<li><strong>Z</strong> (<em>np.ndarray (num_inducing x input_dim)</em>) – inducing inputs</li>
|
||||
<li><strong>num_inducing</strong> (<em>int</em>) – Number of inducing points (optional, default 10. Ignored if Z is not None)</li>
|
||||
<li><strong>mpi_comm</strong> (<em>mpi4py.MPI.Intracomm</em>) – The communication group of MPI, e.g. mpi4py.MPI.COMM_WORLD</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<dl class="method">
|
||||
<dt id="GPy.core.sparse_gp_mpi.SparseGP_MPI.optimize">
|
||||
<code class="descname">optimize</code><span class="sig-paren">(</span><em>optimizer=None</em>, <em>start=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/sparse_gp_mpi.html#SparseGP_MPI.optimize"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.sparse_gp_mpi.SparseGP_MPI.optimize" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="GPy.core.sparse_gp_mpi.SparseGP_MPI.optimizer_array">
|
||||
<code class="descname">optimizer_array</code><a class="headerlink" href="#GPy.core.sparse_gp_mpi.SparseGP_MPI.optimizer_array" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Array for the optimizer to work on.
|
||||
This array always lives in the space for the optimizer.
|
||||
Thus, it is untransformed, going from Transformations.</p>
|
||||
<p>Setting this array, will make sure the transformed parameters for this model
|
||||
will be set accordingly. It has to be set with an array, retrieved from
|
||||
this method, as e.g. fixing will resize the array.</p>
|
||||
<p>The optimizer should only interfere with this array, such that transformations
|
||||
are secured.</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.core.sparse_gp_mpi.SparseGP_MPI.parameters_changed">
|
||||
<code class="descname">parameters_changed</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/core/sparse_gp_mpi.html#SparseGP_MPI.parameters_changed"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.core.sparse_gp_mpi.SparseGP_MPI.parameters_changed" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="gpy-core-svigp-module">
|
||||
<h2>GPy.core.svigp module<a class="headerlink" href="#gpy-core-svigp-module" title="Permalink to this headline">¶</a></h2>
|
||||
</div>
|
||||
<div class="section" id="gpy-core-symbolic-module">
|
||||
<h2>GPy.core.symbolic module<a class="headerlink" href="#gpy-core-symbolic-module" title="Permalink to this headline">¶</a></h2>
|
||||
</div>
|
||||
<div class="section" id="module-GPy.core">
|
||||
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-GPy.core" title="Permalink to this headline">¶</a></h2>
|
||||
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|
||||
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|
||||
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|
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|
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|
||||
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|
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<li><a class="reference internal" href="#module-GPy.core.gp">GPy.core.gp module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.core.mapping">GPy.core.mapping module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.core.model">GPy.core.model module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.core.sparse_gp">GPy.core.sparse_gp module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.core.sparse_gp_mpi">GPy.core.sparse_gp_mpi module</a></li>
|
||||
<li><a class="reference internal" href="#gpy-core-svigp-module">GPy.core.svigp module</a></li>
|
||||
<li><a class="reference internal" href="#gpy-core-symbolic-module">GPy.core.symbolic module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.core">Module contents</a></li>
|
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<div class="section" id="gpy-examples-package">
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<h1>GPy.examples package<a class="headerlink" href="#gpy-examples-package" title="Permalink to this headline">¶</a></h1>
|
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<div class="section" id="submodules">
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<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
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</div>
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<div class="section" id="module-GPy.examples.classification">
|
||||
<span id="gpy-examples-classification-module"></span><h2>GPy.examples.classification module<a class="headerlink" href="#module-GPy.examples.classification" title="Permalink to this headline">¶</a></h2>
|
||||
<p>Gaussian Processes classification examples</p>
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.classification.crescent_data">
|
||||
<code class="descclassname">GPy.examples.classification.</code><code class="descname">crescent_data</code><span class="sig-paren">(</span><em>model_type='Full'</em>, <em>num_inducing=10</em>, <em>seed=10000</em>, <em>kernel=None</em>, <em>optimize=True</em>, <em>plot=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/classification.html#crescent_data"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.classification.crescent_data" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Run a Gaussian process classification on the crescent data. The demonstration calls the basic GP classification model and uses EP to approximate the likelihood.</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
|
||||
<li><strong>model_type</strong> – type of model to fit [‘Full’, ‘FITC’, ‘DTC’].</li>
|
||||
<li><strong>inducing</strong> (<em>int</em>) – number of inducing variables (only used for ‘FITC’ or ‘DTC’).</li>
|
||||
<li><strong>seed</strong> (<em>int</em>) – seed value for data generation.</li>
|
||||
<li><strong>kernel</strong> (<em>a GPy kernel</em>) – kernel to use in the model</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.classification.oil">
|
||||
<code class="descclassname">GPy.examples.classification.</code><code class="descname">oil</code><span class="sig-paren">(</span><em>num_inducing=50</em>, <em>max_iters=100</em>, <em>kernel=None</em>, <em>optimize=True</em>, <em>plot=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/classification.html#oil"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.classification.oil" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Run a Gaussian process classification on the three phase oil data. The demonstration calls the basic GP classification model and uses EP to approximate the likelihood.</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.classification.sparse_toy_linear_1d_classification">
|
||||
<code class="descclassname">GPy.examples.classification.</code><code class="descname">sparse_toy_linear_1d_classification</code><span class="sig-paren">(</span><em>num_inducing=10</em>, <em>seed=10000</em>, <em>optimize=True</em>, <em>plot=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/classification.html#sparse_toy_linear_1d_classification"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.classification.sparse_toy_linear_1d_classification" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Sparse 1D classification example</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>seed</strong> (<em>int</em>) – seed value for data generation (default is 4).</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.classification.toy_heaviside">
|
||||
<code class="descclassname">GPy.examples.classification.</code><code class="descname">toy_heaviside</code><span class="sig-paren">(</span><em>seed=10000</em>, <em>max_iters=100</em>, <em>optimize=True</em>, <em>plot=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/classification.html#toy_heaviside"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.classification.toy_heaviside" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Simple 1D classification example using a heavy side gp transformation</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>seed</strong> (<em>int</em>) – seed value for data generation (default is 4).</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.classification.toy_linear_1d_classification">
|
||||
<code class="descclassname">GPy.examples.classification.</code><code class="descname">toy_linear_1d_classification</code><span class="sig-paren">(</span><em>seed=10000</em>, <em>optimize=True</em>, <em>plot=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/classification.html#toy_linear_1d_classification"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.classification.toy_linear_1d_classification" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Simple 1D classification example using EP approximation</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>seed</strong> (<em>int</em>) – seed value for data generation (default is 4).</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.classification.toy_linear_1d_classification_laplace">
|
||||
<code class="descclassname">GPy.examples.classification.</code><code class="descname">toy_linear_1d_classification_laplace</code><span class="sig-paren">(</span><em>seed=10000</em>, <em>optimize=True</em>, <em>plot=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/classification.html#toy_linear_1d_classification_laplace"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.classification.toy_linear_1d_classification_laplace" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Simple 1D classification example using Laplace approximation</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>seed</strong> (<em>int</em>) – seed value for data generation (default is 4).</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-GPy.examples.coreg_example">
|
||||
<span id="gpy-examples-coreg-example-module"></span><h2>GPy.examples.coreg_example module<a class="headerlink" href="#module-GPy.examples.coreg_example" title="Permalink to this headline">¶</a></h2>
|
||||
</div>
|
||||
<div class="section" id="module-GPy.examples.dimensionality_reduction">
|
||||
<span id="gpy-examples-dimensionality-reduction-module"></span><h2>GPy.examples.dimensionality_reduction module<a class="headerlink" href="#module-GPy.examples.dimensionality_reduction" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.dimensionality_reduction.bcgplvm_linear_stick">
|
||||
<code class="descclassname">GPy.examples.dimensionality_reduction.</code><code class="descname">bcgplvm_linear_stick</code><span class="sig-paren">(</span><em>kernel=None</em>, <em>optimize=True</em>, <em>verbose=True</em>, <em>plot=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/dimensionality_reduction.html#bcgplvm_linear_stick"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.dimensionality_reduction.bcgplvm_linear_stick" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.dimensionality_reduction.bcgplvm_stick">
|
||||
<code class="descclassname">GPy.examples.dimensionality_reduction.</code><code class="descname">bcgplvm_stick</code><span class="sig-paren">(</span><em>kernel=None</em>, <em>optimize=True</em>, <em>verbose=True</em>, <em>plot=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/dimensionality_reduction.html#bcgplvm_stick"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.dimensionality_reduction.bcgplvm_stick" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.dimensionality_reduction.bgplvm_oil">
|
||||
<code class="descclassname">GPy.examples.dimensionality_reduction.</code><code class="descname">bgplvm_oil</code><span class="sig-paren">(</span><em>optimize=True</em>, <em>verbose=1</em>, <em>plot=True</em>, <em>N=200</em>, <em>Q=7</em>, <em>num_inducing=40</em>, <em>max_iters=1000</em>, <em>**k</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/dimensionality_reduction.html#bgplvm_oil"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.dimensionality_reduction.bgplvm_oil" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.dimensionality_reduction.bgplvm_simulation">
|
||||
<code class="descclassname">GPy.examples.dimensionality_reduction.</code><code class="descname">bgplvm_simulation</code><span class="sig-paren">(</span><em>optimize=True</em>, <em>verbose=1</em>, <em>plot=True</em>, <em>plot_sim=False</em>, <em>max_iters=20000.0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/dimensionality_reduction.html#bgplvm_simulation"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.dimensionality_reduction.bgplvm_simulation" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.dimensionality_reduction.bgplvm_simulation_missing_data">
|
||||
<code class="descclassname">GPy.examples.dimensionality_reduction.</code><code class="descname">bgplvm_simulation_missing_data</code><span class="sig-paren">(</span><em>optimize=True</em>, <em>verbose=1</em>, <em>plot=True</em>, <em>plot_sim=False</em>, <em>max_iters=20000.0</em>, <em>percent_missing=0.1</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/dimensionality_reduction.html#bgplvm_simulation_missing_data"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.dimensionality_reduction.bgplvm_simulation_missing_data" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.dimensionality_reduction.bgplvm_test_model">
|
||||
<code class="descclassname">GPy.examples.dimensionality_reduction.</code><code class="descname">bgplvm_test_model</code><span class="sig-paren">(</span><em>optimize=False</em>, <em>verbose=1</em>, <em>plot=False</em>, <em>output_dim=200</em>, <em>nan=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/dimensionality_reduction.html#bgplvm_test_model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.dimensionality_reduction.bgplvm_test_model" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>model for testing purposes. Samples from a GP with rbf kernel and learns
|
||||
the samples with a new kernel. Normally not for optimization, just model cheking</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.dimensionality_reduction.brendan_faces">
|
||||
<code class="descclassname">GPy.examples.dimensionality_reduction.</code><code class="descname">brendan_faces</code><span class="sig-paren">(</span><em>optimize=True</em>, <em>verbose=True</em>, <em>plot=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/dimensionality_reduction.html#brendan_faces"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.dimensionality_reduction.brendan_faces" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.dimensionality_reduction.cmu_mocap">
|
||||
<code class="descclassname">GPy.examples.dimensionality_reduction.</code><code class="descname">cmu_mocap</code><span class="sig-paren">(</span><em>subject='35', motion=['01'], in_place=True, optimize=True, verbose=True, plot=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/dimensionality_reduction.html#cmu_mocap"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.dimensionality_reduction.cmu_mocap" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.dimensionality_reduction.gplvm_oil_100">
|
||||
<code class="descclassname">GPy.examples.dimensionality_reduction.</code><code class="descname">gplvm_oil_100</code><span class="sig-paren">(</span><em>optimize=True</em>, <em>verbose=1</em>, <em>plot=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/dimensionality_reduction.html#gplvm_oil_100"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.dimensionality_reduction.gplvm_oil_100" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.dimensionality_reduction.mrd_simulation">
|
||||
<code class="descclassname">GPy.examples.dimensionality_reduction.</code><code class="descname">mrd_simulation</code><span class="sig-paren">(</span><em>optimize=True</em>, <em>verbose=True</em>, <em>plot=True</em>, <em>plot_sim=True</em>, <em>**kw</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/dimensionality_reduction.html#mrd_simulation"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.dimensionality_reduction.mrd_simulation" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.dimensionality_reduction.mrd_simulation_missing_data">
|
||||
<code class="descclassname">GPy.examples.dimensionality_reduction.</code><code class="descname">mrd_simulation_missing_data</code><span class="sig-paren">(</span><em>optimize=True</em>, <em>verbose=True</em>, <em>plot=True</em>, <em>plot_sim=True</em>, <em>**kw</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/dimensionality_reduction.html#mrd_simulation_missing_data"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.dimensionality_reduction.mrd_simulation_missing_data" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.dimensionality_reduction.olivetti_faces">
|
||||
<code class="descclassname">GPy.examples.dimensionality_reduction.</code><code class="descname">olivetti_faces</code><span class="sig-paren">(</span><em>optimize=True</em>, <em>verbose=True</em>, <em>plot=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/dimensionality_reduction.html#olivetti_faces"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.dimensionality_reduction.olivetti_faces" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.dimensionality_reduction.robot_wireless">
|
||||
<code class="descclassname">GPy.examples.dimensionality_reduction.</code><code class="descname">robot_wireless</code><span class="sig-paren">(</span><em>optimize=True</em>, <em>verbose=True</em>, <em>plot=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/dimensionality_reduction.html#robot_wireless"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.dimensionality_reduction.robot_wireless" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.dimensionality_reduction.sparse_gplvm_oil">
|
||||
<code class="descclassname">GPy.examples.dimensionality_reduction.</code><code class="descname">sparse_gplvm_oil</code><span class="sig-paren">(</span><em>optimize=True</em>, <em>verbose=0</em>, <em>plot=True</em>, <em>N=100</em>, <em>Q=6</em>, <em>num_inducing=15</em>, <em>max_iters=50</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/dimensionality_reduction.html#sparse_gplvm_oil"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.dimensionality_reduction.sparse_gplvm_oil" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.dimensionality_reduction.ssgplvm_oil">
|
||||
<code class="descclassname">GPy.examples.dimensionality_reduction.</code><code class="descname">ssgplvm_oil</code><span class="sig-paren">(</span><em>optimize=True</em>, <em>verbose=1</em>, <em>plot=True</em>, <em>N=200</em>, <em>Q=7</em>, <em>num_inducing=40</em>, <em>max_iters=1000</em>, <em>**k</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/dimensionality_reduction.html#ssgplvm_oil"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.dimensionality_reduction.ssgplvm_oil" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.dimensionality_reduction.ssgplvm_simulation">
|
||||
<code class="descclassname">GPy.examples.dimensionality_reduction.</code><code class="descname">ssgplvm_simulation</code><span class="sig-paren">(</span><em>optimize=True</em>, <em>verbose=1</em>, <em>plot=True</em>, <em>plot_sim=False</em>, <em>max_iters=20000.0</em>, <em>useGPU=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/dimensionality_reduction.html#ssgplvm_simulation"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.dimensionality_reduction.ssgplvm_simulation" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.dimensionality_reduction.ssgplvm_simulation_linear">
|
||||
<code class="descclassname">GPy.examples.dimensionality_reduction.</code><code class="descname">ssgplvm_simulation_linear</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/dimensionality_reduction.html#ssgplvm_simulation_linear"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.dimensionality_reduction.ssgplvm_simulation_linear" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.dimensionality_reduction.stick">
|
||||
<code class="descclassname">GPy.examples.dimensionality_reduction.</code><code class="descname">stick</code><span class="sig-paren">(</span><em>kernel=None</em>, <em>optimize=True</em>, <em>verbose=True</em>, <em>plot=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/dimensionality_reduction.html#stick"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.dimensionality_reduction.stick" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.dimensionality_reduction.stick_bgplvm">
|
||||
<code class="descclassname">GPy.examples.dimensionality_reduction.</code><code class="descname">stick_bgplvm</code><span class="sig-paren">(</span><em>model=None</em>, <em>optimize=True</em>, <em>verbose=True</em>, <em>plot=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/dimensionality_reduction.html#stick_bgplvm"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.dimensionality_reduction.stick_bgplvm" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.dimensionality_reduction.stick_play">
|
||||
<code class="descclassname">GPy.examples.dimensionality_reduction.</code><code class="descname">stick_play</code><span class="sig-paren">(</span><em>range=None</em>, <em>frame_rate=15</em>, <em>optimize=False</em>, <em>verbose=True</em>, <em>plot=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/dimensionality_reduction.html#stick_play"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.dimensionality_reduction.stick_play" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.dimensionality_reduction.swiss_roll">
|
||||
<code class="descclassname">GPy.examples.dimensionality_reduction.</code><code class="descname">swiss_roll</code><span class="sig-paren">(</span><em>optimize=True</em>, <em>verbose=1</em>, <em>plot=True</em>, <em>N=1000</em>, <em>num_inducing=25</em>, <em>Q=4</em>, <em>sigma=0.2</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/dimensionality_reduction.html#swiss_roll"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.dimensionality_reduction.swiss_roll" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-GPy.examples.non_gaussian">
|
||||
<span id="gpy-examples-non-gaussian-module"></span><h2>GPy.examples.non_gaussian module<a class="headerlink" href="#module-GPy.examples.non_gaussian" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.non_gaussian.boston_example">
|
||||
<code class="descclassname">GPy.examples.non_gaussian.</code><code class="descname">boston_example</code><span class="sig-paren">(</span><em>optimize=True</em>, <em>plot=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/non_gaussian.html#boston_example"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.non_gaussian.boston_example" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.non_gaussian.student_t_approx">
|
||||
<code class="descclassname">GPy.examples.non_gaussian.</code><code class="descname">student_t_approx</code><span class="sig-paren">(</span><em>optimize=True</em>, <em>plot=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/non_gaussian.html#student_t_approx"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.non_gaussian.student_t_approx" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Example of regressing with a student t likelihood using Laplace</p>
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-GPy.examples.regression">
|
||||
<span id="gpy-examples-regression-module"></span><h2>GPy.examples.regression module<a class="headerlink" href="#module-GPy.examples.regression" title="Permalink to this headline">¶</a></h2>
|
||||
<p>Gaussian Processes regression examples</p>
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.regression.coregionalization_sparse">
|
||||
<code class="descclassname">GPy.examples.regression.</code><code class="descname">coregionalization_sparse</code><span class="sig-paren">(</span><em>optimize=True</em>, <em>plot=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/regression.html#coregionalization_sparse"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.regression.coregionalization_sparse" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>A simple demonstration of coregionalization on two sinusoidal functions using sparse approximations.</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.regression.coregionalization_toy">
|
||||
<code class="descclassname">GPy.examples.regression.</code><code class="descname">coregionalization_toy</code><span class="sig-paren">(</span><em>optimize=True</em>, <em>plot=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/regression.html#coregionalization_toy"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.regression.coregionalization_toy" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>A simple demonstration of coregionalization on two sinusoidal functions.</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.regression.epomeo_gpx">
|
||||
<code class="descclassname">GPy.examples.regression.</code><code class="descname">epomeo_gpx</code><span class="sig-paren">(</span><em>max_iters=200</em>, <em>optimize=True</em>, <em>plot=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/regression.html#epomeo_gpx"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.regression.epomeo_gpx" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Perform Gaussian process regression on the latitude and longitude data
|
||||
from the Mount Epomeo runs. Requires gpxpy to be installed on your system
|
||||
to load in the data.</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.regression.multiple_optima">
|
||||
<code class="descclassname">GPy.examples.regression.</code><code class="descname">multiple_optima</code><span class="sig-paren">(</span><em>gene_number=937</em>, <em>resolution=80</em>, <em>model_restarts=10</em>, <em>seed=10000</em>, <em>max_iters=300</em>, <em>optimize=True</em>, <em>plot=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/regression.html#multiple_optima"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.regression.multiple_optima" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Show an example of a multimodal error surface for Gaussian process
|
||||
regression. Gene 939 has bimodal behaviour where the noisy mode is
|
||||
higher.</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.regression.olympic_100m_men">
|
||||
<code class="descclassname">GPy.examples.regression.</code><code class="descname">olympic_100m_men</code><span class="sig-paren">(</span><em>optimize=True</em>, <em>plot=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/regression.html#olympic_100m_men"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.regression.olympic_100m_men" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Run a standard Gaussian process regression on the Rogers and Girolami olympics data.</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.regression.olympic_marathon_men">
|
||||
<code class="descclassname">GPy.examples.regression.</code><code class="descname">olympic_marathon_men</code><span class="sig-paren">(</span><em>optimize=True</em>, <em>plot=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/regression.html#olympic_marathon_men"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.regression.olympic_marathon_men" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Run a standard Gaussian process regression on the Olympic marathon data.</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.regression.robot_wireless">
|
||||
<code class="descclassname">GPy.examples.regression.</code><code class="descname">robot_wireless</code><span class="sig-paren">(</span><em>max_iters=100</em>, <em>kernel=None</em>, <em>optimize=True</em>, <em>plot=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/regression.html#robot_wireless"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.regression.robot_wireless" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Predict the location of a robot given wirelss signal strength readings.</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.regression.silhouette">
|
||||
<code class="descclassname">GPy.examples.regression.</code><code class="descname">silhouette</code><span class="sig-paren">(</span><em>max_iters=100</em>, <em>optimize=True</em>, <em>plot=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/regression.html#silhouette"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.regression.silhouette" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Predict the pose of a figure given a silhouette. This is a task from Agarwal and Triggs 2004 ICML paper.</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.regression.sparse_GP_regression_1D">
|
||||
<code class="descclassname">GPy.examples.regression.</code><code class="descname">sparse_GP_regression_1D</code><span class="sig-paren">(</span><em>num_samples=400</em>, <em>num_inducing=5</em>, <em>max_iters=100</em>, <em>optimize=True</em>, <em>plot=True</em>, <em>checkgrad=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/regression.html#sparse_GP_regression_1D"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.regression.sparse_GP_regression_1D" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Run a 1D example of a sparse GP regression.</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.regression.sparse_GP_regression_2D">
|
||||
<code class="descclassname">GPy.examples.regression.</code><code class="descname">sparse_GP_regression_2D</code><span class="sig-paren">(</span><em>num_samples=400</em>, <em>num_inducing=50</em>, <em>max_iters=100</em>, <em>optimize=True</em>, <em>plot=True</em>, <em>nan=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/regression.html#sparse_GP_regression_2D"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.regression.sparse_GP_regression_2D" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Run a 2D example of a sparse GP regression.</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.regression.toy_ARD">
|
||||
<code class="descclassname">GPy.examples.regression.</code><code class="descname">toy_ARD</code><span class="sig-paren">(</span><em>max_iters=1000</em>, <em>kernel_type='linear'</em>, <em>num_samples=300</em>, <em>D=4</em>, <em>optimize=True</em>, <em>plot=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/regression.html#toy_ARD"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.regression.toy_ARD" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.regression.toy_ARD_sparse">
|
||||
<code class="descclassname">GPy.examples.regression.</code><code class="descname">toy_ARD_sparse</code><span class="sig-paren">(</span><em>max_iters=1000</em>, <em>kernel_type='linear'</em>, <em>num_samples=300</em>, <em>D=4</em>, <em>optimize=True</em>, <em>plot=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/regression.html#toy_ARD_sparse"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.regression.toy_ARD_sparse" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.regression.toy_poisson_rbf_1d_laplace">
|
||||
<code class="descclassname">GPy.examples.regression.</code><code class="descname">toy_poisson_rbf_1d_laplace</code><span class="sig-paren">(</span><em>optimize=True</em>, <em>plot=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/regression.html#toy_poisson_rbf_1d_laplace"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.regression.toy_poisson_rbf_1d_laplace" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance.</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.regression.toy_rbf_1d">
|
||||
<code class="descclassname">GPy.examples.regression.</code><code class="descname">toy_rbf_1d</code><span class="sig-paren">(</span><em>optimize=True</em>, <em>plot=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/regression.html#toy_rbf_1d"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.regression.toy_rbf_1d" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance.</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.regression.toy_rbf_1d_50">
|
||||
<code class="descclassname">GPy.examples.regression.</code><code class="descname">toy_rbf_1d_50</code><span class="sig-paren">(</span><em>optimize=True</em>, <em>plot=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/regression.html#toy_rbf_1d_50"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.regression.toy_rbf_1d_50" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance.</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.examples.regression.uncertain_inputs_sparse_regression">
|
||||
<code class="descclassname">GPy.examples.regression.</code><code class="descname">uncertain_inputs_sparse_regression</code><span class="sig-paren">(</span><em>max_iters=200</em>, <em>optimize=True</em>, <em>plot=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/examples/regression.html#uncertain_inputs_sparse_regression"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.examples.regression.uncertain_inputs_sparse_regression" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Run a 1D example of a sparse GP regression with uncertain inputs.</p>
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="gpy-examples-stochastic-module">
|
||||
<h2>GPy.examples.stochastic module<a class="headerlink" href="#gpy-examples-stochastic-module" title="Permalink to this headline">¶</a></h2>
|
||||
</div>
|
||||
<div class="section" id="gpy-examples-tutorials-module">
|
||||
<h2>GPy.examples.tutorials module<a class="headerlink" href="#gpy-examples-tutorials-module" title="Permalink to this headline">¶</a></h2>
|
||||
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|
||||
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|
||||
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-GPy.examples" title="Permalink to this headline">¶</a></h2>
|
||||
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|
||||
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|
||||
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<h3><a href="index.html">Table Of Contents</a></h3>
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<li><a class="reference internal" href="#">GPy.examples package</a><ul>
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<li><a class="reference internal" href="#submodules">Submodules</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.examples.classification">GPy.examples.classification module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.examples.coreg_example">GPy.examples.coreg_example module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.examples.dimensionality_reduction">GPy.examples.dimensionality_reduction module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.examples.non_gaussian">GPy.examples.non_gaussian module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.examples.regression">GPy.examples.regression module</a></li>
|
||||
<li><a class="reference internal" href="#gpy-examples-stochastic-module">GPy.examples.stochastic module</a></li>
|
||||
<li><a class="reference internal" href="#gpy-examples-tutorials-module">GPy.examples.tutorials module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.examples">Module contents</a></li>
|
||||
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|
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|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.examples.html#module-GPy.examples.dimensionality_reduction">GPy.examples.dimensionality_reduction module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.examples.html#module-GPy.examples.non_gaussian">GPy.examples.non_gaussian module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.examples.html#module-GPy.examples.regression">GPy.examples.regression module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.examples.html#gpy-examples-stochastic-module">GPy.examples.stochastic module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.examples.html#gpy-examples-tutorials-module">GPy.examples.tutorials module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.examples.html#module-GPy.examples">Module contents</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="GPy.inference.html">GPy.inference package</a><ul>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.inference.html#subpackages">Subpackages</a><ul>
|
||||
<li class="toctree-l3"><a class="reference internal" href="GPy.inference.latent_function_inference.html">GPy.inference.latent_function_inference package</a><ul>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.inference.latent_function_inference.html#submodules">Submodules</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.inference.latent_function_inference.html#module-GPy.inference.latent_function_inference.dtc">GPy.inference.latent_function_inference.dtc module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.inference.latent_function_inference.html#module-GPy.inference.latent_function_inference.exact_gaussian_inference">GPy.inference.latent_function_inference.exact_gaussian_inference module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.inference.latent_function_inference.html#module-GPy.inference.latent_function_inference.expectation_propagation">GPy.inference.latent_function_inference.expectation_propagation module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.inference.latent_function_inference.html#module-GPy.inference.latent_function_inference.expectation_propagation_dtc">GPy.inference.latent_function_inference.expectation_propagation_dtc module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.inference.latent_function_inference.html#module-GPy.inference.latent_function_inference.fitc">GPy.inference.latent_function_inference.fitc module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.inference.latent_function_inference.html#module-GPy.inference.latent_function_inference.inferenceX">GPy.inference.latent_function_inference.inferenceX module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.inference.latent_function_inference.html#module-GPy.inference.latent_function_inference.laplace">GPy.inference.latent_function_inference.laplace module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.inference.latent_function_inference.html#module-GPy.inference.latent_function_inference.posterior">GPy.inference.latent_function_inference.posterior module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.inference.latent_function_inference.html#module-GPy.inference.latent_function_inference.var_dtc">GPy.inference.latent_function_inference.var_dtc module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.inference.latent_function_inference.html#gpy-inference-latent-function-inference-var-dtc-gpu-module">GPy.inference.latent_function_inference.var_dtc_gpu module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.inference.latent_function_inference.html#module-GPy.inference.latent_function_inference.var_dtc_parallel">GPy.inference.latent_function_inference.var_dtc_parallel module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.inference.latent_function_inference.html#module-GPy.inference.latent_function_inference">Module contents</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="GPy.inference.mcmc.html">GPy.inference.mcmc package</a><ul>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.inference.mcmc.html#submodules">Submodules</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.inference.mcmc.html#module-GPy.inference.mcmc.hmc">GPy.inference.mcmc.hmc module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.inference.mcmc.html#module-GPy.inference.mcmc.samplers">GPy.inference.mcmc.samplers module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.inference.mcmc.html#module-GPy.inference.mcmc">Module contents</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="GPy.inference.optimization.html">GPy.inference.optimization package</a><ul>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.inference.optimization.html#submodules">Submodules</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.inference.optimization.html#module-GPy.inference.optimization.conjugate_gradient_descent">GPy.inference.optimization.conjugate_gradient_descent module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.inference.optimization.html#module-GPy.inference.optimization.gradient_descent_update_rules">GPy.inference.optimization.gradient_descent_update_rules module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.inference.optimization.html#module-GPy.inference.optimization.optimization">GPy.inference.optimization.optimization module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.inference.optimization.html#module-GPy.inference.optimization.scg">GPy.inference.optimization.scg module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.inference.optimization.html#gpy-inference-optimization-sgd-module">GPy.inference.optimization.sgd module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.inference.optimization.html#module-GPy.inference.optimization.stochastics">GPy.inference.optimization.stochastics module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.inference.optimization.html#module-GPy.inference.optimization">Module contents</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.inference.html#module-GPy.inference">Module contents</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="GPy.kern.html">GPy.kern package</a><ul>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.kern.html#subpackages">Subpackages</a><ul>
|
||||
<li class="toctree-l3"><a class="reference internal" href="GPy.kern._src.html">GPy.kern._src package</a><ul>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.kern._src.html#subpackages">Subpackages</a><ul>
|
||||
<li class="toctree-l5"><a class="reference internal" href="GPy.kern._src.psi_comp.html">GPy.kern._src.psi_comp package</a><ul>
|
||||
<li class="toctree-l6"><a class="reference internal" href="GPy.kern._src.psi_comp.html#submodules">Submodules</a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="GPy.kern._src.psi_comp.html#module-GPy.kern._src.psi_comp.linear_psi_comp">GPy.kern._src.psi_comp.linear_psi_comp module</a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="GPy.kern._src.psi_comp.html#module-GPy.kern._src.psi_comp.rbf_psi_comp">GPy.kern._src.psi_comp.rbf_psi_comp module</a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="GPy.kern._src.psi_comp.html#module-GPy.kern._src.psi_comp.rbf_psi_gpucomp">GPy.kern._src.psi_comp.rbf_psi_gpucomp module</a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="GPy.kern._src.psi_comp.html#module-GPy.kern._src.psi_comp.sslinear_psi_comp">GPy.kern._src.psi_comp.sslinear_psi_comp module</a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="GPy.kern._src.psi_comp.html#module-GPy.kern._src.psi_comp.ssrbf_psi_comp">GPy.kern._src.psi_comp.ssrbf_psi_comp module</a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="GPy.kern._src.psi_comp.html#module-GPy.kern._src.psi_comp.ssrbf_psi_gpucomp">GPy.kern._src.psi_comp.ssrbf_psi_gpucomp module</a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="GPy.kern._src.psi_comp.html#module-GPy.kern._src.psi_comp">Module contents</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.kern._src.html#submodules">Submodules</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.kern._src.html#module-GPy.kern._src.ODE_UY">GPy.kern._src.ODE_UY module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.kern._src.html#module-GPy.kern._src.ODE_UYC">GPy.kern._src.ODE_UYC module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.kern._src.html#module-GPy.kern._src.ODE_st">GPy.kern._src.ODE_st module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.kern._src.html#module-GPy.kern._src.ODE_t">GPy.kern._src.ODE_t module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.kern._src.html#module-GPy.kern._src.add">GPy.kern._src.add module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.kern._src.html#module-GPy.kern._src.brownian">GPy.kern._src.brownian module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.kern._src.html#module-GPy.kern._src.coregionalize">GPy.kern._src.coregionalize module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.kern._src.html#gpy-kern-src-hierarchical-module">GPy.kern._src.hierarchical module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.kern._src.html#module-GPy.kern._src.independent_outputs">GPy.kern._src.independent_outputs module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.kern._src.html#module-GPy.kern._src.kern">GPy.kern._src.kern module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.kern._src.html#module-GPy.kern._src.kernel_slice_operations">GPy.kern._src.kernel_slice_operations module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.kern._src.html#module-GPy.kern._src.linear">GPy.kern._src.linear module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.kern._src.html#module-GPy.kern._src.mlp">GPy.kern._src.mlp module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.kern._src.html#module-GPy.kern._src.periodic">GPy.kern._src.periodic module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.kern._src.html#module-GPy.kern._src.poly">GPy.kern._src.poly module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.kern._src.html#module-GPy.kern._src.prod">GPy.kern._src.prod module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.kern._src.html#module-GPy.kern._src.rbf">GPy.kern._src.rbf module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.kern._src.html#module-GPy.kern._src.splitKern">GPy.kern._src.splitKern module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.kern._src.html#module-GPy.kern._src.static">GPy.kern._src.static module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.kern._src.html#module-GPy.kern._src.stationary">GPy.kern._src.stationary module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.kern._src.html#gpy-kern-src-symbolic-module">GPy.kern._src.symbolic module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.kern._src.html#module-GPy.kern._src.trunclinear">GPy.kern._src.trunclinear module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.kern._src.html#module-GPy.kern._src">Module contents</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.kern.html#module-GPy.kern">Module contents</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="GPy.likelihoods.html">GPy.likelihoods package</a><ul>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.likelihoods.html#submodules">Submodules</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.likelihoods.html#module-GPy.likelihoods.bernoulli">GPy.likelihoods.bernoulli module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.likelihoods.html#module-GPy.likelihoods.exponential">GPy.likelihoods.exponential module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.likelihoods.html#module-GPy.likelihoods.gamma">GPy.likelihoods.gamma module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.likelihoods.html#module-GPy.likelihoods.gaussian">GPy.likelihoods.gaussian module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.likelihoods.html#module-GPy.likelihoods.likelihood">GPy.likelihoods.likelihood module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.likelihoods.html#module-GPy.likelihoods.link_functions">GPy.likelihoods.link_functions module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.likelihoods.html#module-GPy.likelihoods.mixed_noise">GPy.likelihoods.mixed_noise module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.likelihoods.html#module-GPy.likelihoods.poisson">GPy.likelihoods.poisson module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.likelihoods.html#module-GPy.likelihoods.student_t">GPy.likelihoods.student_t module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.likelihoods.html#module-GPy.likelihoods">Module contents</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="GPy.mappings.html">GPy.mappings package</a><ul>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.mappings.html#submodules">Submodules</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.mappings.html#module-GPy.mappings.additive">GPy.mappings.additive module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.mappings.html#module-GPy.mappings.kernel">GPy.mappings.kernel module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.mappings.html#module-GPy.mappings.linear">GPy.mappings.linear module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.mappings.html#module-GPy.mappings.mlp">GPy.mappings.mlp module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.mappings.html#module-GPy.mappings">Module contents</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="GPy.models.html">GPy.models package</a><ul>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.models.html#submodules">Submodules</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.models.html#module-GPy.models.bayesian_gplvm">GPy.models.bayesian_gplvm module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.models.html#module-GPy.models.bayesian_gplvm_minibatch">GPy.models.bayesian_gplvm_minibatch module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.models.html#module-GPy.models.bcgplvm">GPy.models.bcgplvm module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.models.html#module-GPy.models.gp_classification">GPy.models.gp_classification module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.models.html#module-GPy.models.gp_coregionalized_regression">GPy.models.gp_coregionalized_regression module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.models.html#module-GPy.models.gp_heteroscedastic_regression">GPy.models.gp_heteroscedastic_regression module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.models.html#module-GPy.models.gp_kronecker_gaussian_regression">GPy.models.gp_kronecker_gaussian_regression module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.models.html#gpy-models-gp-multioutput-regression-module">GPy.models.gp_multioutput_regression module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.models.html#module-GPy.models.gp_regression">GPy.models.gp_regression module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.models.html#module-GPy.models.gp_var_gauss">GPy.models.gp_var_gauss module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.models.html#module-GPy.models.gplvm">GPy.models.gplvm module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.models.html#module-GPy.models.gradient_checker">GPy.models.gradient_checker module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.models.html#module-GPy.models.mrd">GPy.models.mrd module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.models.html#module-GPy.models.sparse_gp_classification">GPy.models.sparse_gp_classification module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.models.html#module-GPy.models.sparse_gp_coregionalized_regression">GPy.models.sparse_gp_coregionalized_regression module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.models.html#module-GPy.models.sparse_gp_minibatch">GPy.models.sparse_gp_minibatch module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.models.html#gpy-models-sparse-gp-multioutput-regression-module">GPy.models.sparse_gp_multioutput_regression module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.models.html#module-GPy.models.sparse_gp_regression">GPy.models.sparse_gp_regression module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.models.html#module-GPy.models.sparse_gplvm">GPy.models.sparse_gplvm module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.models.html#module-GPy.models.ss_gplvm">GPy.models.ss_gplvm module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.models.html#module-GPy.models.ss_mrd">GPy.models.ss_mrd module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.models.html#gpy-models-svigp-regression-module">GPy.models.svigp_regression module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.models.html#module-GPy.models.warped_gp">GPy.models.warped_gp module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.models.html#module-GPy.models">Module contents</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="GPy.plotting.html">GPy.plotting package</a><ul>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.plotting.html#subpackages">Subpackages</a><ul>
|
||||
<li class="toctree-l3"><a class="reference internal" href="GPy.plotting.matplot_dep.html">GPy.plotting.matplot_dep package</a><ul>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.plotting.matplot_dep.html#subpackages">Subpackages</a><ul>
|
||||
<li class="toctree-l5"><a class="reference internal" href="GPy.plotting.matplot_dep.latent_space_visualizations.html">GPy.plotting.matplot_dep.latent_space_visualizations package</a><ul>
|
||||
<li class="toctree-l6"><a class="reference internal" href="GPy.plotting.matplot_dep.latent_space_visualizations.html#subpackages">Subpackages</a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="GPy.plotting.matplot_dep.latent_space_visualizations.controllers.html">GPy.plotting.matplot_dep.latent_space_visualizations.controllers package</a><ul>
|
||||
<li class="toctree-l8"><a class="reference internal" href="GPy.plotting.matplot_dep.latent_space_visualizations.controllers.html#submodules">Submodules</a></li>
|
||||
<li class="toctree-l8"><a class="reference internal" href="GPy.plotting.matplot_dep.latent_space_visualizations.controllers.html#module-GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller">GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller module</a></li>
|
||||
<li class="toctree-l8"><a class="reference internal" href="GPy.plotting.matplot_dep.latent_space_visualizations.controllers.html#module-GPy.plotting.matplot_dep.latent_space_visualizations.controllers.imshow_controller">GPy.plotting.matplot_dep.latent_space_visualizations.controllers.imshow_controller module</a></li>
|
||||
<li class="toctree-l8"><a class="reference internal" href="GPy.plotting.matplot_dep.latent_space_visualizations.controllers.html#module-GPy.plotting.matplot_dep.latent_space_visualizations.controllers">Module contents</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="GPy.plotting.matplot_dep.latent_space_visualizations.html#module-GPy.plotting.matplot_dep.latent_space_visualizations">Module contents</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.plotting.matplot_dep.html#submodules">Submodules</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.plotting.matplot_dep.html#module-GPy.plotting.matplot_dep.Tango">GPy.plotting.matplot_dep.Tango module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.plotting.matplot_dep.html#module-GPy.plotting.matplot_dep.base_plots">GPy.plotting.matplot_dep.base_plots module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.plotting.matplot_dep.html#module-GPy.plotting.matplot_dep.dim_reduction_plots">GPy.plotting.matplot_dep.dim_reduction_plots module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.plotting.matplot_dep.html#module-GPy.plotting.matplot_dep.img_plots">GPy.plotting.matplot_dep.img_plots module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.plotting.matplot_dep.html#module-GPy.plotting.matplot_dep.inference_plots">GPy.plotting.matplot_dep.inference_plots module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.plotting.matplot_dep.html#module-GPy.plotting.matplot_dep.kernel_plots">GPy.plotting.matplot_dep.kernel_plots module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.plotting.matplot_dep.html#module-GPy.plotting.matplot_dep.mapping_plots">GPy.plotting.matplot_dep.mapping_plots module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.plotting.matplot_dep.html#module-GPy.plotting.matplot_dep.maps">GPy.plotting.matplot_dep.maps module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.plotting.matplot_dep.html#module-GPy.plotting.matplot_dep.models_plots">GPy.plotting.matplot_dep.models_plots module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.plotting.matplot_dep.html#module-GPy.plotting.matplot_dep.netpbmfile">GPy.plotting.matplot_dep.netpbmfile module</a><ul>
|
||||
<li class="toctree-l5"><a class="reference internal" href="GPy.plotting.matplot_dep.html#requirements">Requirements</a></li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="GPy.plotting.matplot_dep.html#examples">Examples</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.plotting.matplot_dep.html#module-GPy.plotting.matplot_dep.priors_plots">GPy.plotting.matplot_dep.priors_plots module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.plotting.matplot_dep.html#module-GPy.plotting.matplot_dep.ssgplvm">GPy.plotting.matplot_dep.ssgplvm module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.plotting.matplot_dep.html#module-GPy.plotting.matplot_dep.svig_plots">GPy.plotting.matplot_dep.svig_plots module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.plotting.matplot_dep.html#module-GPy.plotting.matplot_dep.variational_plots">GPy.plotting.matplot_dep.variational_plots module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.plotting.matplot_dep.html#module-GPy.plotting.matplot_dep.visualize">GPy.plotting.matplot_dep.visualize module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="GPy.plotting.matplot_dep.html#module-GPy.plotting.matplot_dep">Module contents</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.plotting.html#module-GPy.plotting">Module contents</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="GPy.testing.html">GPy.testing package</a><ul>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.testing.html#submodules">Submodules</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.testing.html#module-GPy.testing.examples_tests">GPy.testing.examples_tests module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.testing.html#module-GPy.testing.fitc">GPy.testing.fitc module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.testing.html#module-GPy.testing.index_operations_tests">GPy.testing.index_operations_tests module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.testing.html#module-GPy.testing.inference_tests">GPy.testing.inference_tests module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.testing.html#module-GPy.testing.kernel_tests">GPy.testing.kernel_tests module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.testing.html#module-GPy.testing.likelihood_tests">GPy.testing.likelihood_tests module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.testing.html#module-GPy.testing.model_tests">GPy.testing.model_tests module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.testing.html#module-GPy.testing.observable_tests">GPy.testing.observable_tests module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.testing.html#module-GPy.testing.parameterized_tests">GPy.testing.parameterized_tests module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.testing.html#module-GPy.testing.pickle_tests">GPy.testing.pickle_tests module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.testing.html#module-GPy.testing.prior_tests">GPy.testing.prior_tests module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.testing.html#module-GPy.testing">Module contents</a></li>
|
||||
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|
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|
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|
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|
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<li class="toctree-l2"><a class="reference internal" href="GPy.util.html#module-GPy.util.block_matrices">GPy.util.block_matrices module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.util.html#module-GPy.util.caching">GPy.util.caching module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.util.html#module-GPy.util.classification">GPy.util.classification module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.util.html#module-GPy.util.config">GPy.util.config module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.util.html#module-GPy.util.datasets">GPy.util.datasets module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.util.html#module-GPy.util.debug">GPy.util.debug module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.util.html#module-GPy.util.decorators">GPy.util.decorators module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.util.html#module-GPy.util.diag">GPy.util.diag module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.util.html#module-GPy.util.erfcx">GPy.util.erfcx module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.util.html#module-GPy.util.functions">GPy.util.functions module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.util.html#module-GPy.util.gpu_init">GPy.util.gpu_init module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.util.html#module-GPy.util.initialization">GPy.util.initialization module</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="GPy.util.html#module-GPy.util.linalg">GPy.util.linalg module</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="GPy.util.html#module-GPy.util.linalg_gpu">GPy.util.linalg_gpu module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.util.html#module-GPy.util.ln_diff_erfs">GPy.util.ln_diff_erfs module</a></li>
|
||||
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|
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|
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<li class="toctree-l2"><a class="reference internal" href="GPy.util.html#gpy-util-mpi-module">GPy.util.mpi module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="GPy.util.html#module-GPy.util.multioutput">GPy.util.multioutput module</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="GPy.util.html#module-GPy.util.netpbmfile">GPy.util.netpbmfile module</a><ul>
|
||||
<li class="toctree-l3"><a class="reference internal" href="GPy.util.html#requirements">Requirements</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="GPy.util.html#examples">Examples</a></li>
|
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|
||||
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|
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<li class="toctree-l2"><a class="reference internal" href="GPy.util.html#module-GPy.util.normalizer">GPy.util.normalizer module</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="GPy.util.html#module-GPy.util.parallel">GPy.util.parallel module</a></li>
|
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|
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<li class="toctree-l2"><a class="reference internal" href="GPy.util.html#module-GPy.util.squashers">GPy.util.squashers module</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="GPy.util.html#module-GPy.util.subarray_and_sorting">GPy.util.subarray_and_sorting module</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="GPy.util.html#module-GPy.util.univariate_Gaussian">GPy.util.univariate_Gaussian module</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="GPy.util.html#module-GPy.util.warping_functions">GPy.util.warping_functions module</a></li>
|
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<div class="section" id="module-GPy">
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<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-GPy" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="function">
|
||||
<dt id="GPy.load">
|
||||
<code class="descclassname">GPy.</code><code class="descname">load</code><span class="sig-paren">(</span><em>file_path</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy.html#load"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.load" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Load a previously pickled model, using <a href="#id1"><span class="problematic" id="id2">`</span></a>m.pickle(‘path/to/file.pickle)’</p>
|
||||
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|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>file_name</strong> – path/to/file.pickle</td>
|
||||
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|
||||
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|
||||
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|
||||
</dd></dl>
|
||||
|
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<dl class="function">
|
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<dt id="GPy.tests">
|
||||
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|
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<div class="section" id="gpy-inference-latent-function-inference-package">
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<h1>GPy.inference.latent_function_inference package<a class="headerlink" href="#gpy-inference-latent-function-inference-package" title="Permalink to this headline">¶</a></h1>
|
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<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
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<div class="section" id="module-GPy.inference.latent_function_inference.dtc">
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<span id="gpy-inference-latent-function-inference-dtc-module"></span><h2>GPy.inference.latent_function_inference.dtc module<a class="headerlink" href="#module-GPy.inference.latent_function_inference.dtc" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="class">
|
||||
<dt id="GPy.inference.latent_function_inference.dtc.DTC">
|
||||
<em class="property">class </em><code class="descclassname">GPy.inference.latent_function_inference.dtc.</code><code class="descname">DTC</code><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/dtc.html#DTC"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.dtc.DTC" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#GPy.inference.latent_function_inference.LatentFunctionInference" title="GPy.inference.latent_function_inference.LatentFunctionInference"><code class="xref py py-class docutils literal"><span class="pre">GPy.inference.latent_function_inference.LatentFunctionInference</span></code></a></p>
|
||||
<p>An object for inference when the likelihood is Gaussian, but we want to do sparse inference.</p>
|
||||
<p>The function self.inference returns a Posterior object, which summarizes
|
||||
the posterior.</p>
|
||||
<p>NB. It’s not recommended to use this function! It’s here for historical purposes.</p>
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.latent_function_inference.dtc.DTC.inference">
|
||||
<code class="descname">inference</code><span class="sig-paren">(</span><em>kern</em>, <em>X</em>, <em>Z</em>, <em>likelihood</em>, <em>Y</em>, <em>Y_metadata=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/dtc.html#DTC.inference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.dtc.DTC.inference" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="class">
|
||||
<dt id="GPy.inference.latent_function_inference.dtc.vDTC">
|
||||
<em class="property">class </em><code class="descclassname">GPy.inference.latent_function_inference.dtc.</code><code class="descname">vDTC</code><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/dtc.html#vDTC"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.dtc.vDTC" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <code class="xref py py-class docutils literal"><span class="pre">object</span></code></p>
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.latent_function_inference.dtc.vDTC.inference">
|
||||
<code class="descname">inference</code><span class="sig-paren">(</span><em>kern</em>, <em>X</em>, <em>X_variance</em>, <em>Z</em>, <em>likelihood</em>, <em>Y</em>, <em>Y_metadata</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/dtc.html#vDTC.inference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.dtc.vDTC.inference" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-GPy.inference.latent_function_inference.exact_gaussian_inference">
|
||||
<span id="gpy-inference-latent-function-inference-exact-gaussian-inference-module"></span><h2>GPy.inference.latent_function_inference.exact_gaussian_inference module<a class="headerlink" href="#module-GPy.inference.latent_function_inference.exact_gaussian_inference" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="class">
|
||||
<dt id="GPy.inference.latent_function_inference.exact_gaussian_inference.ExactGaussianInference">
|
||||
<em class="property">class </em><code class="descclassname">GPy.inference.latent_function_inference.exact_gaussian_inference.</code><code class="descname">ExactGaussianInference</code><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/exact_gaussian_inference.html#ExactGaussianInference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.exact_gaussian_inference.ExactGaussianInference" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#GPy.inference.latent_function_inference.LatentFunctionInference" title="GPy.inference.latent_function_inference.LatentFunctionInference"><code class="xref py py-class docutils literal"><span class="pre">GPy.inference.latent_function_inference.LatentFunctionInference</span></code></a></p>
|
||||
<p>An object for inference when the likelihood is Gaussian.</p>
|
||||
<p>The function self.inference returns a Posterior object, which summarizes
|
||||
the posterior.</p>
|
||||
<p>For efficiency, we sometimes work with the cholesky of Y*Y.T. To save repeatedly recomputing this, we cache it.</p>
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.latent_function_inference.exact_gaussian_inference.ExactGaussianInference.get_YYTfactor">
|
||||
<code class="descname">get_YYTfactor</code><span class="sig-paren">(</span><em>Y</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/exact_gaussian_inference.html#ExactGaussianInference.get_YYTfactor"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.exact_gaussian_inference.ExactGaussianInference.get_YYTfactor" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>find a matrix L which satisfies LL^T = YY^T.</p>
|
||||
<p>Note that L may have fewer columns than Y, else L=Y.</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.latent_function_inference.exact_gaussian_inference.ExactGaussianInference.inference">
|
||||
<code class="descname">inference</code><span class="sig-paren">(</span><em>kern</em>, <em>X</em>, <em>likelihood</em>, <em>Y</em>, <em>Y_metadata=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/exact_gaussian_inference.html#ExactGaussianInference.inference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.exact_gaussian_inference.ExactGaussianInference.inference" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Returns a Posterior class containing essential quantities of the posterior</p>
|
||||
</dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-GPy.inference.latent_function_inference.expectation_propagation">
|
||||
<span id="gpy-inference-latent-function-inference-expectation-propagation-module"></span><h2>GPy.inference.latent_function_inference.expectation_propagation module<a class="headerlink" href="#module-GPy.inference.latent_function_inference.expectation_propagation" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="class">
|
||||
<dt id="GPy.inference.latent_function_inference.expectation_propagation.EP">
|
||||
<em class="property">class </em><code class="descclassname">GPy.inference.latent_function_inference.expectation_propagation.</code><code class="descname">EP</code><span class="sig-paren">(</span><em>epsilon=1e-06</em>, <em>eta=1.0</em>, <em>delta=1.0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/expectation_propagation.html#EP"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.expectation_propagation.EP" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#GPy.inference.latent_function_inference.LatentFunctionInference" title="GPy.inference.latent_function_inference.LatentFunctionInference"><code class="xref py py-class docutils literal"><span class="pre">GPy.inference.latent_function_inference.LatentFunctionInference</span></code></a></p>
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.latent_function_inference.expectation_propagation.EP.expectation_propagation">
|
||||
<code class="descname">expectation_propagation</code><span class="sig-paren">(</span><em>K</em>, <em>Y</em>, <em>likelihood</em>, <em>Y_metadata</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/expectation_propagation.html#EP.expectation_propagation"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.expectation_propagation.EP.expectation_propagation" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.latent_function_inference.expectation_propagation.EP.inference">
|
||||
<code class="descname">inference</code><span class="sig-paren">(</span><em>kern</em>, <em>X</em>, <em>likelihood</em>, <em>Y</em>, <em>Y_metadata=None</em>, <em>Z=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/expectation_propagation.html#EP.inference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.expectation_propagation.EP.inference" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.latent_function_inference.expectation_propagation.EP.on_optimization_end">
|
||||
<code class="descname">on_optimization_end</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/expectation_propagation.html#EP.on_optimization_end"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.expectation_propagation.EP.on_optimization_end" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.latent_function_inference.expectation_propagation.EP.on_optimization_start">
|
||||
<code class="descname">on_optimization_start</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/expectation_propagation.html#EP.on_optimization_start"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.expectation_propagation.EP.on_optimization_start" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.latent_function_inference.expectation_propagation.EP.reset">
|
||||
<code class="descname">reset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/expectation_propagation.html#EP.reset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.expectation_propagation.EP.reset" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-GPy.inference.latent_function_inference.expectation_propagation_dtc">
|
||||
<span id="gpy-inference-latent-function-inference-expectation-propagation-dtc-module"></span><h2>GPy.inference.latent_function_inference.expectation_propagation_dtc module<a class="headerlink" href="#module-GPy.inference.latent_function_inference.expectation_propagation_dtc" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="class">
|
||||
<dt id="GPy.inference.latent_function_inference.expectation_propagation_dtc.EPDTC">
|
||||
<em class="property">class </em><code class="descclassname">GPy.inference.latent_function_inference.expectation_propagation_dtc.</code><code class="descname">EPDTC</code><span class="sig-paren">(</span><em>epsilon=1e-06</em>, <em>eta=1.0</em>, <em>delta=1.0</em>, <em>limit=1</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/expectation_propagation_dtc.html#EPDTC"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.expectation_propagation_dtc.EPDTC" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#GPy.inference.latent_function_inference.LatentFunctionInference" title="GPy.inference.latent_function_inference.LatentFunctionInference"><code class="xref py py-class docutils literal"><span class="pre">GPy.inference.latent_function_inference.LatentFunctionInference</span></code></a></p>
|
||||
<dl class="attribute">
|
||||
<dt id="GPy.inference.latent_function_inference.expectation_propagation_dtc.EPDTC.const_jitter">
|
||||
<code class="descname">const_jitter</code><em class="property"> = 1e-06</em><a class="headerlink" href="#GPy.inference.latent_function_inference.expectation_propagation_dtc.EPDTC.const_jitter" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.latent_function_inference.expectation_propagation_dtc.EPDTC.expectation_propagation">
|
||||
<code class="descname">expectation_propagation</code><span class="sig-paren">(</span><em>Kmm</em>, <em>Kmn</em>, <em>Y</em>, <em>likelihood</em>, <em>Y_metadata</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/expectation_propagation_dtc.html#EPDTC.expectation_propagation"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.expectation_propagation_dtc.EPDTC.expectation_propagation" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.latent_function_inference.expectation_propagation_dtc.EPDTC.get_VVTfactor">
|
||||
<code class="descname">get_VVTfactor</code><span class="sig-paren">(</span><em>Y</em>, <em>prec</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/expectation_propagation_dtc.html#EPDTC.get_VVTfactor"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.expectation_propagation_dtc.EPDTC.get_VVTfactor" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.latent_function_inference.expectation_propagation_dtc.EPDTC.inference">
|
||||
<code class="descname">inference</code><span class="sig-paren">(</span><em>kern</em>, <em>X</em>, <em>Z</em>, <em>likelihood</em>, <em>Y</em>, <em>Y_metadata=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/expectation_propagation_dtc.html#EPDTC.inference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.expectation_propagation_dtc.EPDTC.inference" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.latent_function_inference.expectation_propagation_dtc.EPDTC.on_optimization_end">
|
||||
<code class="descname">on_optimization_end</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/expectation_propagation_dtc.html#EPDTC.on_optimization_end"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.expectation_propagation_dtc.EPDTC.on_optimization_end" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.latent_function_inference.expectation_propagation_dtc.EPDTC.on_optimization_start">
|
||||
<code class="descname">on_optimization_start</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/expectation_propagation_dtc.html#EPDTC.on_optimization_start"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.expectation_propagation_dtc.EPDTC.on_optimization_start" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.latent_function_inference.expectation_propagation_dtc.EPDTC.reset">
|
||||
<code class="descname">reset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/expectation_propagation_dtc.html#EPDTC.reset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.expectation_propagation_dtc.EPDTC.reset" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.latent_function_inference.expectation_propagation_dtc.EPDTC.set_limit">
|
||||
<code class="descname">set_limit</code><span class="sig-paren">(</span><em>limit</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/expectation_propagation_dtc.html#EPDTC.set_limit"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.expectation_propagation_dtc.EPDTC.set_limit" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-GPy.inference.latent_function_inference.fitc">
|
||||
<span id="gpy-inference-latent-function-inference-fitc-module"></span><h2>GPy.inference.latent_function_inference.fitc module<a class="headerlink" href="#module-GPy.inference.latent_function_inference.fitc" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="class">
|
||||
<dt id="GPy.inference.latent_function_inference.fitc.FITC">
|
||||
<em class="property">class </em><code class="descclassname">GPy.inference.latent_function_inference.fitc.</code><code class="descname">FITC</code><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/fitc.html#FITC"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.fitc.FITC" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#GPy.inference.latent_function_inference.LatentFunctionInference" title="GPy.inference.latent_function_inference.LatentFunctionInference"><code class="xref py py-class docutils literal"><span class="pre">GPy.inference.latent_function_inference.LatentFunctionInference</span></code></a></p>
|
||||
<p>An object for inference when the likelihood is Gaussian, but we want to do sparse inference.</p>
|
||||
<p>The function self.inference returns a Posterior object, which summarizes
|
||||
the posterior.</p>
|
||||
<dl class="attribute">
|
||||
<dt id="GPy.inference.latent_function_inference.fitc.FITC.const_jitter">
|
||||
<code class="descname">const_jitter</code><em class="property"> = 1e-06</em><a class="headerlink" href="#GPy.inference.latent_function_inference.fitc.FITC.const_jitter" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.latent_function_inference.fitc.FITC.inference">
|
||||
<code class="descname">inference</code><span class="sig-paren">(</span><em>kern</em>, <em>X</em>, <em>Z</em>, <em>likelihood</em>, <em>Y</em>, <em>Y_metadata=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/fitc.html#FITC.inference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.fitc.FITC.inference" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-GPy.inference.latent_function_inference.inferenceX">
|
||||
<span id="gpy-inference-latent-function-inference-inferencex-module"></span><h2>GPy.inference.latent_function_inference.inferenceX module<a class="headerlink" href="#module-GPy.inference.latent_function_inference.inferenceX" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="class">
|
||||
<dt id="GPy.inference.latent_function_inference.inferenceX.InferenceX">
|
||||
<em class="property">class </em><code class="descclassname">GPy.inference.latent_function_inference.inferenceX.</code><code class="descname">InferenceX</code><span class="sig-paren">(</span><em>model</em>, <em>Y</em>, <em>name='inferenceX'</em>, <em>init='L2'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/inferenceX.html#InferenceX"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.inferenceX.InferenceX" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="GPy.core.html#GPy.core.model.Model" title="GPy.core.model.Model"><code class="xref py py-class docutils literal"><span class="pre">GPy.core.model.Model</span></code></a></p>
|
||||
<p>The class for inference of new X with given new Y. (do_test_latent)</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
|
||||
<li><strong>model</strong> (<em>GPy.core.Model</em>) – the GPy model used in inference</li>
|
||||
<li><strong>Y</strong> (<em>numpy.ndarray</em>) – the new observed data for inference</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.latent_function_inference.inferenceX.InferenceX.compute_dL">
|
||||
<code class="descname">compute_dL</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/inferenceX.html#InferenceX.compute_dL"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.inferenceX.InferenceX.compute_dL" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.latent_function_inference.inferenceX.InferenceX.log_likelihood">
|
||||
<code class="descname">log_likelihood</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/inferenceX.html#InferenceX.log_likelihood"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.inferenceX.InferenceX.log_likelihood" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.latent_function_inference.inferenceX.InferenceX.parameters_changed">
|
||||
<code class="descname">parameters_changed</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/inferenceX.html#InferenceX.parameters_changed"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.inferenceX.InferenceX.parameters_changed" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.inference.latent_function_inference.inferenceX.infer_newX">
|
||||
<code class="descclassname">GPy.inference.latent_function_inference.inferenceX.</code><code class="descname">infer_newX</code><span class="sig-paren">(</span><em>model</em>, <em>Y_new</em>, <em>optimize=True</em>, <em>init='L2'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/inferenceX.html#infer_newX"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.inferenceX.infer_newX" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Infer the distribution of X for the new observed data <em>Y_new</em>.</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>model</strong> (<em>GPy.core.Model</em>) – the GPy model used in inference</li>
|
||||
<li><strong>Y_new</strong> (<em>numpy.ndarray</em>) – the new observed data for inference</li>
|
||||
<li><strong>optimize</strong> (<em>boolean</em>) – whether to optimize the location of new X (True by default)</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">a tuple containing the estimated posterior distribution of X and the model that optimize X</p>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">(GPy.core.parameterization.variational.VariationalPosterior, GPy.core.Model)</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-GPy.inference.latent_function_inference.laplace">
|
||||
<span id="gpy-inference-latent-function-inference-laplace-module"></span><h2>GPy.inference.latent_function_inference.laplace module<a class="headerlink" href="#module-GPy.inference.latent_function_inference.laplace" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="class">
|
||||
<dt id="GPy.inference.latent_function_inference.laplace.Laplace">
|
||||
<em class="property">class </em><code class="descclassname">GPy.inference.latent_function_inference.laplace.</code><code class="descname">Laplace</code><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/laplace.html#Laplace"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.laplace.Laplace" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#GPy.inference.latent_function_inference.LatentFunctionInference" title="GPy.inference.latent_function_inference.LatentFunctionInference"><code class="xref py py-class docutils literal"><span class="pre">GPy.inference.latent_function_inference.LatentFunctionInference</span></code></a></p>
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.latent_function_inference.laplace.Laplace.inference">
|
||||
<code class="descname">inference</code><span class="sig-paren">(</span><em>kern</em>, <em>X</em>, <em>likelihood</em>, <em>Y</em>, <em>Y_metadata=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/laplace.html#Laplace.inference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.laplace.Laplace.inference" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Returns a Posterior class containing essential quantities of the posterior</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.latent_function_inference.laplace.Laplace.mode_computations">
|
||||
<code class="descname">mode_computations</code><span class="sig-paren">(</span><em>f_hat</em>, <em>Ki_f</em>, <em>K</em>, <em>Y</em>, <em>likelihood</em>, <em>kern</em>, <em>Y_metadata</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/laplace.html#Laplace.mode_computations"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.laplace.Laplace.mode_computations" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>At the mode, compute the hessian and effective covariance matrix.</p>
|
||||
<dl class="docutils">
|
||||
<dt>returns: logZ <span class="classifier-delimiter">:</span> <span class="classifier">approximation to the marginal likelihood</span></dt>
|
||||
<dd>woodbury_inv : variable required for calculating the approximation to the covariance matrix
|
||||
dL_dthetaL : array of derivatives (1 x num_kernel_params)
|
||||
dL_dthetaL : array of derivatives (1 x num_likelihood_params)</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.latent_function_inference.laplace.Laplace.rasm_mode">
|
||||
<code class="descname">rasm_mode</code><span class="sig-paren">(</span><em>K</em>, <em>Y</em>, <em>likelihood</em>, <em>Ki_f_init</em>, <em>Y_metadata=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/laplace.html#Laplace.rasm_mode"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.laplace.Laplace.rasm_mode" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Rasmussen’s numerically stable mode finding
|
||||
For nomenclature see Rasmussen & Williams 2006
|
||||
Influenced by GPML (BSD) code, all errors are our own</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>K</strong> (<em>NxD matrix</em>) – Covariance matrix evaluated at locations X</li>
|
||||
<li><strong>Y</strong> (<em>np.ndarray</em>) – The data</li>
|
||||
<li><strong>likelihood</strong> (<em>a GPy.likelihood object</em>) – the likelihood of the latent function value for the given data</li>
|
||||
<li><strong>Ki_f_init</strong> (<em>np.ndarray</em>) – the initial guess at the mode</li>
|
||||
<li><strong>Y_metadata</strong> (<em>np.ndarray | None</em>) – information about the data, e.g. which likelihood to take from a multi-likelihood object</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">f_hat, mode on which to make laplace approxmiation</p>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">np.ndarray</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.inference.latent_function_inference.laplace.warning_on_one_line">
|
||||
<code class="descclassname">GPy.inference.latent_function_inference.laplace.</code><code class="descname">warning_on_one_line</code><span class="sig-paren">(</span><em>message</em>, <em>category</em>, <em>filename</em>, <em>lineno</em>, <em>file=None</em>, <em>line=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/laplace.html#warning_on_one_line"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.laplace.warning_on_one_line" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-GPy.inference.latent_function_inference.posterior">
|
||||
<span id="gpy-inference-latent-function-inference-posterior-module"></span><h2>GPy.inference.latent_function_inference.posterior module<a class="headerlink" href="#module-GPy.inference.latent_function_inference.posterior" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="class">
|
||||
<dt id="GPy.inference.latent_function_inference.posterior.Posterior">
|
||||
<em class="property">class </em><code class="descclassname">GPy.inference.latent_function_inference.posterior.</code><code class="descname">Posterior</code><span class="sig-paren">(</span><em>woodbury_chol=None</em>, <em>woodbury_vector=None</em>, <em>K=None</em>, <em>mean=None</em>, <em>cov=None</em>, <em>K_chol=None</em>, <em>woodbury_inv=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/posterior.html#Posterior"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.posterior.Posterior" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <code class="xref py py-class docutils literal"><span class="pre">object</span></code></p>
|
||||
<p>An object to represent a Gaussian posterior over latent function values, p(f|D).
|
||||
This may be computed exactly for Gaussian likelihoods, or approximated for
|
||||
non-Gaussian likelihoods.</p>
|
||||
<p>The purpose of this class is to serve as an interface between the inference
|
||||
schemes and the model classes. the model class can make predictions for
|
||||
the function at any new point x_* by integrating over this posterior.</p>
|
||||
<dl class="attribute">
|
||||
<dt id="GPy.inference.latent_function_inference.posterior.Posterior.K_chol">
|
||||
<code class="descname">K_chol</code><a class="headerlink" href="#GPy.inference.latent_function_inference.posterior.Posterior.K_chol" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Cholesky of the prior covariance K</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="GPy.inference.latent_function_inference.posterior.Posterior.covariance">
|
||||
<code class="descname">covariance</code><a class="headerlink" href="#GPy.inference.latent_function_inference.posterior.Posterior.covariance" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Posterior covariance
|
||||
$$
|
||||
K_{xx} - K_{xx}W_{xx}^{-1}K_{xx}
|
||||
W_{xx} := exttt{Woodbury inv}
|
||||
$$</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="GPy.inference.latent_function_inference.posterior.Posterior.mean">
|
||||
<code class="descname">mean</code><a class="headerlink" href="#GPy.inference.latent_function_inference.posterior.Posterior.mean" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Posterior mean
|
||||
$$
|
||||
K_{xx}v
|
||||
v := exttt{Woodbury vector}
|
||||
$$</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="GPy.inference.latent_function_inference.posterior.Posterior.precision">
|
||||
<code class="descname">precision</code><a class="headerlink" href="#GPy.inference.latent_function_inference.posterior.Posterior.precision" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Inverse of posterior covariance</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="GPy.inference.latent_function_inference.posterior.Posterior.woodbury_chol">
|
||||
<code class="descname">woodbury_chol</code><a class="headerlink" href="#GPy.inference.latent_function_inference.posterior.Posterior.woodbury_chol" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>return $L_{W}$ where L is the lower triangular Cholesky decomposition of the Woodbury matrix
|
||||
$$
|
||||
L_{W}L_{W}^{ op} = W^{-1}
|
||||
W^{-1} := exttt{Woodbury inv}
|
||||
$$</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="GPy.inference.latent_function_inference.posterior.Posterior.woodbury_inv">
|
||||
<code class="descname">woodbury_inv</code><a class="headerlink" href="#GPy.inference.latent_function_inference.posterior.Posterior.woodbury_inv" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>The inverse of the woodbury matrix, in the gaussian likelihood case it is defined as
|
||||
$$
|
||||
(K_{xx} + Sigma_{xx})^{-1}
|
||||
Sigma_{xx} := exttt{Likelihood.variance / Approximate likelihood covariance}
|
||||
$$</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="GPy.inference.latent_function_inference.posterior.Posterior.woodbury_vector">
|
||||
<code class="descname">woodbury_vector</code><a class="headerlink" href="#GPy.inference.latent_function_inference.posterior.Posterior.woodbury_vector" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Woodbury vector in the gaussian likelihood case only is defined as
|
||||
$$
|
||||
(K_{xx} + Sigma)^{-1}Y
|
||||
Sigma := exttt{Likelihood.variance / Approximate likelihood covariance}
|
||||
$$</p>
|
||||
</dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-GPy.inference.latent_function_inference.var_dtc">
|
||||
<span id="gpy-inference-latent-function-inference-var-dtc-module"></span><h2>GPy.inference.latent_function_inference.var_dtc module<a class="headerlink" href="#module-GPy.inference.latent_function_inference.var_dtc" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="class">
|
||||
<dt id="GPy.inference.latent_function_inference.var_dtc.VarDTC">
|
||||
<em class="property">class </em><code class="descclassname">GPy.inference.latent_function_inference.var_dtc.</code><code class="descname">VarDTC</code><span class="sig-paren">(</span><em>limit=1</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/var_dtc.html#VarDTC"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.var_dtc.VarDTC" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#GPy.inference.latent_function_inference.LatentFunctionInference" title="GPy.inference.latent_function_inference.LatentFunctionInference"><code class="xref py py-class docutils literal"><span class="pre">GPy.inference.latent_function_inference.LatentFunctionInference</span></code></a></p>
|
||||
<p>An object for inference when the likelihood is Gaussian, but we want to do sparse inference.</p>
|
||||
<p>The function self.inference returns a Posterior object, which summarizes
|
||||
the posterior.</p>
|
||||
<p>For efficiency, we sometimes work with the cholesky of Y*Y.T. To save repeatedly recomputing this, we cache it.</p>
|
||||
<dl class="attribute">
|
||||
<dt id="GPy.inference.latent_function_inference.var_dtc.VarDTC.const_jitter">
|
||||
<code class="descname">const_jitter</code><em class="property"> = 1e-06</em><a class="headerlink" href="#GPy.inference.latent_function_inference.var_dtc.VarDTC.const_jitter" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.latent_function_inference.var_dtc.VarDTC.get_VVTfactor">
|
||||
<code class="descname">get_VVTfactor</code><span class="sig-paren">(</span><em>Y</em>, <em>prec</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/var_dtc.html#VarDTC.get_VVTfactor"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.var_dtc.VarDTC.get_VVTfactor" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.latent_function_inference.var_dtc.VarDTC.inference">
|
||||
<code class="descname">inference</code><span class="sig-paren">(</span><em>kern</em>, <em>X</em>, <em>Z</em>, <em>likelihood</em>, <em>Y</em>, <em>Y_metadata=None</em>, <em>Lm=None</em>, <em>dL_dKmm=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/var_dtc.html#VarDTC.inference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.var_dtc.VarDTC.inference" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.latent_function_inference.var_dtc.VarDTC.set_limit">
|
||||
<code class="descname">set_limit</code><span class="sig-paren">(</span><em>limit</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/var_dtc.html#VarDTC.set_limit"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.var_dtc.VarDTC.set_limit" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="gpy-inference-latent-function-inference-var-dtc-gpu-module">
|
||||
<h2>GPy.inference.latent_function_inference.var_dtc_gpu module<a class="headerlink" href="#gpy-inference-latent-function-inference-var-dtc-gpu-module" title="Permalink to this headline">¶</a></h2>
|
||||
</div>
|
||||
<div class="section" id="module-GPy.inference.latent_function_inference.var_dtc_parallel">
|
||||
<span id="gpy-inference-latent-function-inference-var-dtc-parallel-module"></span><h2>GPy.inference.latent_function_inference.var_dtc_parallel module<a class="headerlink" href="#module-GPy.inference.latent_function_inference.var_dtc_parallel" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="class">
|
||||
<dt id="GPy.inference.latent_function_inference.var_dtc_parallel.VarDTC_minibatch">
|
||||
<em class="property">class </em><code class="descclassname">GPy.inference.latent_function_inference.var_dtc_parallel.</code><code class="descname">VarDTC_minibatch</code><span class="sig-paren">(</span><em>batchsize=None</em>, <em>limit=1</em>, <em>mpi_comm=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/var_dtc_parallel.html#VarDTC_minibatch"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.var_dtc_parallel.VarDTC_minibatch" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#GPy.inference.latent_function_inference.LatentFunctionInference" title="GPy.inference.latent_function_inference.LatentFunctionInference"><code class="xref py py-class docutils literal"><span class="pre">GPy.inference.latent_function_inference.LatentFunctionInference</span></code></a></p>
|
||||
<p>An object for inference when the likelihood is Gaussian, but we want to do sparse inference.</p>
|
||||
<p>The function self.inference returns a Posterior object, which summarizes
|
||||
the posterior.</p>
|
||||
<p>For efficiency, we sometimes work with the cholesky of Y*Y.T. To save repeatedly recomputing this, we cache it.</p>
|
||||
<dl class="attribute">
|
||||
<dt id="GPy.inference.latent_function_inference.var_dtc_parallel.VarDTC_minibatch.const_jitter">
|
||||
<code class="descname">const_jitter</code><em class="property"> = 1e-06</em><a class="headerlink" href="#GPy.inference.latent_function_inference.var_dtc_parallel.VarDTC_minibatch.const_jitter" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.latent_function_inference.var_dtc_parallel.VarDTC_minibatch.gatherPsiStat">
|
||||
<code class="descname">gatherPsiStat</code><span class="sig-paren">(</span><em>kern</em>, <em>X</em>, <em>Z</em>, <em>Y</em>, <em>beta</em>, <em>uncertain_inputs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/var_dtc_parallel.html#VarDTC_minibatch.gatherPsiStat"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.var_dtc_parallel.VarDTC_minibatch.gatherPsiStat" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.latent_function_inference.var_dtc_parallel.VarDTC_minibatch.inference_likelihood">
|
||||
<code class="descname">inference_likelihood</code><span class="sig-paren">(</span><em>kern</em>, <em>X</em>, <em>Z</em>, <em>likelihood</em>, <em>Y</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/var_dtc_parallel.html#VarDTC_minibatch.inference_likelihood"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.var_dtc_parallel.VarDTC_minibatch.inference_likelihood" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>The first phase of inference:
|
||||
Compute: log-likelihood, dL_dKmm</p>
|
||||
<p>Cached intermediate results: Kmm, KmmInv,</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.latent_function_inference.var_dtc_parallel.VarDTC_minibatch.inference_minibatch">
|
||||
<code class="descname">inference_minibatch</code><span class="sig-paren">(</span><em>kern</em>, <em>X</em>, <em>Z</em>, <em>likelihood</em>, <em>Y</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/var_dtc_parallel.html#VarDTC_minibatch.inference_minibatch"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.var_dtc_parallel.VarDTC_minibatch.inference_minibatch" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>The second phase of inference: Computing the derivatives over a minibatch of Y
|
||||
Compute: dL_dpsi0, dL_dpsi1, dL_dpsi2, dL_dthetaL
|
||||
return a flag showing whether it reached the end of Y (isEnd)</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.latent_function_inference.var_dtc_parallel.VarDTC_minibatch.set_limit">
|
||||
<code class="descname">set_limit</code><span class="sig-paren">(</span><em>limit</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/var_dtc_parallel.html#VarDTC_minibatch.set_limit"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.var_dtc_parallel.VarDTC_minibatch.set_limit" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.inference.latent_function_inference.var_dtc_parallel.update_gradients">
|
||||
<code class="descclassname">GPy.inference.latent_function_inference.var_dtc_parallel.</code><code class="descname">update_gradients</code><span class="sig-paren">(</span><em>model</em>, <em>mpi_comm=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/var_dtc_parallel.html#update_gradients"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.var_dtc_parallel.update_gradients" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.inference.latent_function_inference.var_dtc_parallel.update_gradients_sparsegp">
|
||||
<code class="descclassname">GPy.inference.latent_function_inference.var_dtc_parallel.</code><code class="descname">update_gradients_sparsegp</code><span class="sig-paren">(</span><em>model</em>, <em>mpi_comm=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference/var_dtc_parallel.html#update_gradients_sparsegp"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.var_dtc_parallel.update_gradients_sparsegp" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-GPy.inference.latent_function_inference">
|
||||
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-GPy.inference.latent_function_inference" title="Permalink to this headline">¶</a></h2>
|
||||
<p>Inference over Gaussian process latent functions</p>
|
||||
<p>In all our GP models, the consistency propery means that we have a Gaussian
|
||||
prior over a finite set of points f. This prior is</p>
|
||||
<blockquote>
|
||||
<div>math:: N(f | 0, K)</div></blockquote>
|
||||
<p>where K is the kernel matrix.</p>
|
||||
<p>We also have a likelihood (see GPy.likelihoods) which defines how the data are
|
||||
related to the latent function: p(y | f). If the likelihood is also a Gaussian,
|
||||
the inference over f is tractable (see exact_gaussian_inference.py).</p>
|
||||
<p>If the likelihood object is something other than Gaussian, then exact inference
|
||||
is not tractable. We then resort to a Laplace approximation (laplace.py) or
|
||||
expectation propagation (ep.py).</p>
|
||||
<p>The inference methods return a
|
||||
<a class="reference internal" href="#GPy.inference.latent_function_inference.posterior.Posterior" title="GPy.inference.latent_function_inference.posterior.Posterior"><code class="xref py py-class docutils literal"><span class="pre">Posterior</span></code></a>
|
||||
instance, which is a simple
|
||||
structure which contains a summary of the posterior. The model classes can then
|
||||
use this posterior object for making predictions, optimizing hyper-parameters,
|
||||
etc.</p>
|
||||
<dl class="class">
|
||||
<dt id="GPy.inference.latent_function_inference.InferenceMethodList">
|
||||
<em class="property">class </em><code class="descclassname">GPy.inference.latent_function_inference.</code><code class="descname">InferenceMethodList</code><a class="reference internal" href="_modules/GPy/inference/latent_function_inference.html#InferenceMethodList"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.InferenceMethodList" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#GPy.inference.latent_function_inference.LatentFunctionInference" title="GPy.inference.latent_function_inference.LatentFunctionInference"><code class="xref py py-class docutils literal"><span class="pre">GPy.inference.latent_function_inference.LatentFunctionInference</span></code></a>, <code class="xref py py-class docutils literal"><span class="pre">list</span></code></p>
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.latent_function_inference.InferenceMethodList.on_optimization_end">
|
||||
<code class="descname">on_optimization_end</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference.html#InferenceMethodList.on_optimization_end"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.InferenceMethodList.on_optimization_end" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.latent_function_inference.InferenceMethodList.on_optimization_start">
|
||||
<code class="descname">on_optimization_start</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference.html#InferenceMethodList.on_optimization_start"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.InferenceMethodList.on_optimization_start" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="class">
|
||||
<dt id="GPy.inference.latent_function_inference.LatentFunctionInference">
|
||||
<em class="property">class </em><code class="descclassname">GPy.inference.latent_function_inference.</code><code class="descname">LatentFunctionInference</code><a class="reference internal" href="_modules/GPy/inference/latent_function_inference.html#LatentFunctionInference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.LatentFunctionInference" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <code class="xref py py-class docutils literal"><span class="pre">object</span></code></p>
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.latent_function_inference.LatentFunctionInference.on_optimization_end">
|
||||
<code class="descname">on_optimization_end</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference.html#LatentFunctionInference.on_optimization_end"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.LatentFunctionInference.on_optimization_end" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>This function gets called, just after the optimization loop ended.</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.latent_function_inference.LatentFunctionInference.on_optimization_start">
|
||||
<code class="descname">on_optimization_start</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/latent_function_inference.html#LatentFunctionInference.on_optimization_start"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.latent_function_inference.LatentFunctionInference.on_optimization_start" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>This function gets called, just before the optimization loop to start.</p>
|
||||
</dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
</div>
|
||||
|
||||
|
||||
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|
||||
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|
||||
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||||
<div class="sphinxsidebar" role="navigation" aria-label="main navigation">
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<div class="sphinxsidebarwrapper">
|
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<h3><a href="index.html">Table Of Contents</a></h3>
|
||||
<ul>
|
||||
<li><a class="reference internal" href="#">GPy.inference.latent_function_inference package</a><ul>
|
||||
<li><a class="reference internal" href="#submodules">Submodules</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.inference.latent_function_inference.dtc">GPy.inference.latent_function_inference.dtc module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.inference.latent_function_inference.exact_gaussian_inference">GPy.inference.latent_function_inference.exact_gaussian_inference module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.inference.latent_function_inference.expectation_propagation">GPy.inference.latent_function_inference.expectation_propagation module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.inference.latent_function_inference.expectation_propagation_dtc">GPy.inference.latent_function_inference.expectation_propagation_dtc module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.inference.latent_function_inference.fitc">GPy.inference.latent_function_inference.fitc module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.inference.latent_function_inference.inferenceX">GPy.inference.latent_function_inference.inferenceX module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.inference.latent_function_inference.laplace">GPy.inference.latent_function_inference.laplace module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.inference.latent_function_inference.posterior">GPy.inference.latent_function_inference.posterior module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.inference.latent_function_inference.var_dtc">GPy.inference.latent_function_inference.var_dtc module</a></li>
|
||||
<li><a class="reference internal" href="#gpy-inference-latent-function-inference-var-dtc-gpu-module">GPy.inference.latent_function_inference.var_dtc_gpu module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.inference.latent_function_inference.var_dtc_parallel">GPy.inference.latent_function_inference.var_dtc_parallel module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.inference.latent_function_inference">Module contents</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
|
||||
<h4>Previous topic</h4>
|
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|
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title="previous chapter">GPy.inference package</a></p>
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<div class="section" id="gpy-inference-mcmc-package">
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<h1>GPy.inference.mcmc package<a class="headerlink" href="#gpy-inference-mcmc-package" title="Permalink to this headline">¶</a></h1>
|
||||
<div class="section" id="submodules">
|
||||
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
|
||||
</div>
|
||||
<div class="section" id="module-GPy.inference.mcmc.hmc">
|
||||
<span id="gpy-inference-mcmc-hmc-module"></span><h2>GPy.inference.mcmc.hmc module<a class="headerlink" href="#module-GPy.inference.mcmc.hmc" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="class">
|
||||
<dt id="GPy.inference.mcmc.hmc.HMC">
|
||||
<em class="property">class </em><code class="descclassname">GPy.inference.mcmc.hmc.</code><code class="descname">HMC</code><span class="sig-paren">(</span><em>model</em>, <em>M=None</em>, <em>stepsize=0.1</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/mcmc/hmc.html#HMC"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.mcmc.hmc.HMC" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>An implementation of Hybrid Monte Carlo (HMC) for GPy models</p>
|
||||
<p>Initialize an object for HMC sampling. Note that the status of the model (model parameters) will be changed during sampling.</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
|
||||
<li><strong>model</strong> (<em>GPy.core.Model</em>) – the GPy model that will be sampled</li>
|
||||
<li><strong>M</strong> (<em>numpy.ndarray</em>) – the mass matrix (an identity matrix by default)</li>
|
||||
<li><strong>stepsize</strong> (<em>float</em>) – the step size for HMC sampling</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.mcmc.hmc.HMC.sample">
|
||||
<code class="descname">sample</code><span class="sig-paren">(</span><em>num_samples=1000</em>, <em>hmc_iters=20</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/mcmc/hmc.html#HMC.sample"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.mcmc.hmc.HMC.sample" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Sample the (unfixed) model parameters.</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>num_samples</strong> (<em>int</em>) – the number of samples to draw (1000 by default)</li>
|
||||
<li><strong>hmc_iters</strong> (<em>int</em>) – the number of leap-frog iterations (20 by default)</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">the list of parameters samples with the size N x P (N - the number of samples, P - the number of parameters to sample)</p>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">numpy.ndarray</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="class">
|
||||
<dt id="GPy.inference.mcmc.hmc.HMC_shortcut">
|
||||
<em class="property">class </em><code class="descclassname">GPy.inference.mcmc.hmc.</code><code class="descname">HMC_shortcut</code><span class="sig-paren">(</span><em>model, M=None, stepsize_range=[1e-06, 0.1], groupsize=5, Hstd_th=[1e-05, 3.0]</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/mcmc/hmc.html#HMC_shortcut"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.mcmc.hmc.HMC_shortcut" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><dl class="method">
|
||||
<dt id="GPy.inference.mcmc.hmc.HMC_shortcut.sample">
|
||||
<code class="descname">sample</code><span class="sig-paren">(</span><em>m_iters=1000</em>, <em>hmc_iters=20</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/mcmc/hmc.html#HMC_shortcut.sample"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.mcmc.hmc.HMC_shortcut.sample" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-GPy.inference.mcmc.samplers">
|
||||
<span id="gpy-inference-mcmc-samplers-module"></span><h2>GPy.inference.mcmc.samplers module<a class="headerlink" href="#module-GPy.inference.mcmc.samplers" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="class">
|
||||
<dt id="GPy.inference.mcmc.samplers.Metropolis_Hastings">
|
||||
<em class="property">class </em><code class="descclassname">GPy.inference.mcmc.samplers.</code><code class="descname">Metropolis_Hastings</code><span class="sig-paren">(</span><em>model</em>, <em>cov=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/mcmc/samplers.html#Metropolis_Hastings"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.mcmc.samplers.Metropolis_Hastings" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><dl class="method">
|
||||
<dt id="GPy.inference.mcmc.samplers.Metropolis_Hastings.new_chain">
|
||||
<code class="descname">new_chain</code><span class="sig-paren">(</span><em>start=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/mcmc/samplers.html#Metropolis_Hastings.new_chain"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.mcmc.samplers.Metropolis_Hastings.new_chain" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.mcmc.samplers.Metropolis_Hastings.predict">
|
||||
<code class="descname">predict</code><span class="sig-paren">(</span><em>function</em>, <em>args</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/mcmc/samplers.html#Metropolis_Hastings.predict"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.mcmc.samplers.Metropolis_Hastings.predict" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Make a prediction for the function, to which we will pass the additional arguments</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.mcmc.samplers.Metropolis_Hastings.sample">
|
||||
<code class="descname">sample</code><span class="sig-paren">(</span><em>Ntotal</em>, <em>Nburn</em>, <em>Nthin</em>, <em>tune=True</em>, <em>tune_throughout=False</em>, <em>tune_interval=400</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/mcmc/samplers.html#Metropolis_Hastings.sample"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.mcmc.samplers.Metropolis_Hastings.sample" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-GPy.inference.mcmc">
|
||||
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-GPy.inference.mcmc" title="Permalink to this headline">¶</a></h2>
|
||||
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|
||||
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|
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<li><a class="reference internal" href="#">GPy.inference.mcmc package</a><ul>
|
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<li><a class="reference internal" href="#submodules">Submodules</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.inference.mcmc.hmc">GPy.inference.mcmc.hmc module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.inference.mcmc.samplers">GPy.inference.mcmc.samplers module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.inference.mcmc">Module contents</a></li>
|
||||
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|
||||
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|
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|
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<h1>GPy.inference.optimization package<a class="headerlink" href="#gpy-inference-optimization-package" title="Permalink to this headline">¶</a></h1>
|
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<div class="section" id="submodules">
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<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
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<div class="section" id="module-GPy.inference.optimization.conjugate_gradient_descent">
|
||||
<span id="gpy-inference-optimization-conjugate-gradient-descent-module"></span><h2>GPy.inference.optimization.conjugate_gradient_descent module<a class="headerlink" href="#module-GPy.inference.optimization.conjugate_gradient_descent" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="class">
|
||||
<dt id="GPy.inference.optimization.conjugate_gradient_descent.Async_Optimize">
|
||||
<em class="property">class </em><code class="descclassname">GPy.inference.optimization.conjugate_gradient_descent.</code><code class="descname">Async_Optimize</code><a class="reference internal" href="_modules/GPy/inference/optimization/conjugate_gradient_descent.html#Async_Optimize"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.conjugate_gradient_descent.Async_Optimize" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <code class="xref py py-class docutils literal"><span class="pre">object</span></code></p>
|
||||
<dl class="attribute">
|
||||
<dt id="GPy.inference.optimization.conjugate_gradient_descent.Async_Optimize.SENTINEL">
|
||||
<code class="descname">SENTINEL</code><em class="property"> = 'SENTINEL'</em><a class="headerlink" href="#GPy.inference.optimization.conjugate_gradient_descent.Async_Optimize.SENTINEL" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.optimization.conjugate_gradient_descent.Async_Optimize.async_callback_collect">
|
||||
<code class="descname">async_callback_collect</code><span class="sig-paren">(</span><em>q</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/optimization/conjugate_gradient_descent.html#Async_Optimize.async_callback_collect"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.conjugate_gradient_descent.Async_Optimize.async_callback_collect" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.optimization.conjugate_gradient_descent.Async_Optimize.callback">
|
||||
<code class="descname">callback</code><span class="sig-paren">(</span><em>*x</em><span class="sig-paren">)</span><a class="headerlink" href="#GPy.inference.optimization.conjugate_gradient_descent.Async_Optimize.callback" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.optimization.conjugate_gradient_descent.Async_Optimize.opt">
|
||||
<code class="descname">opt</code><span class="sig-paren">(</span><em>f</em>, <em>df</em>, <em>x0</em>, <em>callback=None</em>, <em>update_rule=<class GPy.inference.optimization.gradient_descent_update_rules.FletcherReeves></em>, <em>messages=0</em>, <em>maxiter=5000.0</em>, <em>max_f_eval=15000.0</em>, <em>gtol=1e-06</em>, <em>report_every=10</em>, <em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/optimization/conjugate_gradient_descent.html#Async_Optimize.opt"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.conjugate_gradient_descent.Async_Optimize.opt" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.optimization.conjugate_gradient_descent.Async_Optimize.opt_async">
|
||||
<code class="descname">opt_async</code><span class="sig-paren">(</span><em>f</em>, <em>df</em>, <em>x0</em>, <em>callback</em>, <em>update_rule=<class GPy.inference.optimization.gradient_descent_update_rules.PolakRibiere></em>, <em>messages=0</em>, <em>maxiter=5000.0</em>, <em>max_f_eval=15000.0</em>, <em>gtol=1e-06</em>, <em>report_every=10</em>, <em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/optimization/conjugate_gradient_descent.html#Async_Optimize.opt_async"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.conjugate_gradient_descent.Async_Optimize.opt_async" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="GPy.inference.optimization.conjugate_gradient_descent.Async_Optimize.runsignal">
|
||||
<code class="descname">runsignal</code><em class="property"> = <multiprocessing.synchronize.Event object></em><a class="headerlink" href="#GPy.inference.optimization.conjugate_gradient_descent.Async_Optimize.runsignal" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="class">
|
||||
<dt id="GPy.inference.optimization.conjugate_gradient_descent.CGD">
|
||||
<em class="property">class </em><code class="descclassname">GPy.inference.optimization.conjugate_gradient_descent.</code><code class="descname">CGD</code><a class="reference internal" href="_modules/GPy/inference/optimization/conjugate_gradient_descent.html#CGD"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.conjugate_gradient_descent.CGD" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#GPy.inference.optimization.conjugate_gradient_descent.Async_Optimize" title="GPy.inference.optimization.conjugate_gradient_descent.Async_Optimize"><code class="xref py py-class docutils literal"><span class="pre">GPy.inference.optimization.conjugate_gradient_descent.Async_Optimize</span></code></a></p>
|
||||
<p>Conjugate gradient descent algorithm to minimize
|
||||
function f with gradients df, starting at x0
|
||||
with update rule update_rule</p>
|
||||
<p>if df returns tuple (grad, natgrad) it will optimize according
|
||||
to natural gradient rules</p>
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.optimization.conjugate_gradient_descent.CGD.opt">
|
||||
<code class="descname">opt</code><span class="sig-paren">(</span><em>*a</em>, <em>**kw</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/optimization/conjugate_gradient_descent.html#CGD.opt"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.conjugate_gradient_descent.CGD.opt" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><dl class="docutils">
|
||||
<dt>opt(self, f, df, x0, callback=None, update_rule=FletcherReeves,</dt>
|
||||
<dd>messages=0, maxiter=5e3, max_f_eval=15e3, gtol=1e-6,
|
||||
report_every=10, *args, **kwargs)</dd>
|
||||
</dl>
|
||||
<p>Minimize f, calling callback every <cite>report_every</cite> iterations with following syntax:</p>
|
||||
<blockquote>
|
||||
<div>callback(xi, fi, gi, iteration, function_calls, gradient_calls, status_message)</div></blockquote>
|
||||
<p>if df returns tuple (grad, natgrad) it will optimize according
|
||||
to natural gradient rules</p>
|
||||
<p>f, and df will be called with</p>
|
||||
<blockquote>
|
||||
<div>f(xi, *args, **kwargs)
|
||||
df(xi, *args, **kwargs)</div></blockquote>
|
||||
<p><strong>returns</strong></p>
|
||||
<blockquote>
|
||||
<div>x_opt, f_opt, g_opt, iteration, function_calls, gradient_calls, status_message</div></blockquote>
|
||||
<p>at end of optimization</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.optimization.conjugate_gradient_descent.CGD.opt_async">
|
||||
<code class="descname">opt_async</code><span class="sig-paren">(</span><em>*a</em>, <em>**kw</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/optimization/conjugate_gradient_descent.html#CGD.opt_async"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.conjugate_gradient_descent.CGD.opt_async" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><dl class="docutils">
|
||||
<dt>opt_async(self, f, df, x0, callback, update_rule=FletcherReeves,</dt>
|
||||
<dd>messages=0, maxiter=5e3, max_f_eval=15e3, gtol=1e-6,
|
||||
report_every=10, *args, **kwargs)</dd>
|
||||
</dl>
|
||||
<p>callback gets called every <cite>report_every</cite> iterations</p>
|
||||
<blockquote>
|
||||
<div>callback(xi, fi, gi, iteration, function_calls, gradient_calls, status_message)</div></blockquote>
|
||||
<p>if df returns tuple (grad, natgrad) it will optimize according
|
||||
to natural gradient rules</p>
|
||||
<p>f, and df will be called with</p>
|
||||
<blockquote>
|
||||
<div>f(xi, *args, **kwargs)
|
||||
df(xi, *args, **kwargs)</div></blockquote>
|
||||
<p><strong>Returns:</strong></p>
|
||||
<blockquote>
|
||||
<div>Started <cite>Process</cite> object, optimizing asynchronously</div></blockquote>
|
||||
<p><strong>Calls:</strong></p>
|
||||
<blockquote>
|
||||
<div>callback(x_opt, f_opt, g_opt, iteration, function_calls, gradient_calls, status_message)</div></blockquote>
|
||||
<p>at end of optimization!</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="GPy.inference.optimization.conjugate_gradient_descent.CGD.opt_name">
|
||||
<code class="descname">opt_name</code><em class="property"> = 'Conjugate Gradient Descent'</em><a class="headerlink" href="#GPy.inference.optimization.conjugate_gradient_descent.CGD.opt_name" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-GPy.inference.optimization.gradient_descent_update_rules">
|
||||
<span id="gpy-inference-optimization-gradient-descent-update-rules-module"></span><h2>GPy.inference.optimization.gradient_descent_update_rules module<a class="headerlink" href="#module-GPy.inference.optimization.gradient_descent_update_rules" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="class">
|
||||
<dt id="GPy.inference.optimization.gradient_descent_update_rules.FletcherReeves">
|
||||
<em class="property">class </em><code class="descclassname">GPy.inference.optimization.gradient_descent_update_rules.</code><code class="descname">FletcherReeves</code><span class="sig-paren">(</span><em>initgrad</em>, <em>initgradnat=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/optimization/gradient_descent_update_rules.html#FletcherReeves"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.gradient_descent_update_rules.FletcherReeves" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#GPy.inference.optimization.gradient_descent_update_rules.GDUpdateRule" title="GPy.inference.optimization.gradient_descent_update_rules.GDUpdateRule"><code class="xref py py-class docutils literal"><span class="pre">GPy.inference.optimization.gradient_descent_update_rules.GDUpdateRule</span></code></a></p>
|
||||
<p>Fletcher Reeves update rule for gamma</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="class">
|
||||
<dt id="GPy.inference.optimization.gradient_descent_update_rules.GDUpdateRule">
|
||||
<em class="property">class </em><code class="descclassname">GPy.inference.optimization.gradient_descent_update_rules.</code><code class="descname">GDUpdateRule</code><span class="sig-paren">(</span><em>initgrad</em>, <em>initgradnat=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/optimization/gradient_descent_update_rules.html#GDUpdateRule"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.gradient_descent_update_rules.GDUpdateRule" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="class">
|
||||
<dt id="GPy.inference.optimization.gradient_descent_update_rules.PolakRibiere">
|
||||
<em class="property">class </em><code class="descclassname">GPy.inference.optimization.gradient_descent_update_rules.</code><code class="descname">PolakRibiere</code><span class="sig-paren">(</span><em>initgrad</em>, <em>initgradnat=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/optimization/gradient_descent_update_rules.html#PolakRibiere"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.gradient_descent_update_rules.PolakRibiere" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#GPy.inference.optimization.gradient_descent_update_rules.GDUpdateRule" title="GPy.inference.optimization.gradient_descent_update_rules.GDUpdateRule"><code class="xref py py-class docutils literal"><span class="pre">GPy.inference.optimization.gradient_descent_update_rules.GDUpdateRule</span></code></a></p>
|
||||
<p>Fletcher Reeves update rule for gamma</p>
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-GPy.inference.optimization.optimization">
|
||||
<span id="gpy-inference-optimization-optimization-module"></span><h2>GPy.inference.optimization.optimization module<a class="headerlink" href="#module-GPy.inference.optimization.optimization" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="class">
|
||||
<dt id="GPy.inference.optimization.optimization.Optimizer">
|
||||
<em class="property">class </em><code class="descclassname">GPy.inference.optimization.optimization.</code><code class="descname">Optimizer</code><span class="sig-paren">(</span><em>x_init</em>, <em>messages=False</em>, <em>model=None</em>, <em>max_f_eval=10000.0</em>, <em>max_iters=1000.0</em>, <em>ftol=None</em>, <em>gtol=None</em>, <em>xtol=None</em>, <em>bfgs_factor=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/optimization/optimization.html#Optimizer"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.optimization.Optimizer" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Superclass for all the optimizers.</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>x_init</strong> – initial set of parameters</li>
|
||||
<li><strong>f_fp</strong> – function that returns the function AND the gradients at the same time</li>
|
||||
<li><strong>f</strong> – function to optimize</li>
|
||||
<li><strong>fp</strong> – gradients</li>
|
||||
<li><strong>messages</strong> (<em>(True | False)</em>) – print messages from the optimizer?</li>
|
||||
<li><strong>max_f_eval</strong> – maximum number of function evaluations</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">optimizer object.</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.optimization.optimization.Optimizer.opt">
|
||||
<code class="descname">opt</code><span class="sig-paren">(</span><em>f_fp=None</em>, <em>f=None</em>, <em>fp=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/optimization/optimization.html#Optimizer.opt"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.optimization.Optimizer.opt" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.optimization.optimization.Optimizer.plot">
|
||||
<code class="descname">plot</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/optimization/optimization.html#Optimizer.plot"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.optimization.Optimizer.plot" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>See GPy.plotting.matplot_dep.inference_plots</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.optimization.optimization.Optimizer.run">
|
||||
<code class="descname">run</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/optimization/optimization.html#Optimizer.run"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.optimization.Optimizer.run" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.inference.optimization.optimization.get_optimizer">
|
||||
<code class="descclassname">GPy.inference.optimization.optimization.</code><code class="descname">get_optimizer</code><span class="sig-paren">(</span><em>f_min</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/optimization/optimization.html#get_optimizer"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.optimization.get_optimizer" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="class">
|
||||
<dt id="GPy.inference.optimization.optimization.opt_SCG">
|
||||
<em class="property">class </em><code class="descclassname">GPy.inference.optimization.optimization.</code><code class="descname">opt_SCG</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/optimization/optimization.html#opt_SCG"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.optimization.opt_SCG" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#GPy.inference.optimization.optimization.Optimizer" title="GPy.inference.optimization.optimization.Optimizer"><code class="xref py py-class docutils literal"><span class="pre">GPy.inference.optimization.optimization.Optimizer</span></code></a></p>
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.optimization.optimization.opt_SCG.opt">
|
||||
<code class="descname">opt</code><span class="sig-paren">(</span><em>f_fp=None</em>, <em>f=None</em>, <em>fp=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/optimization/optimization.html#opt_SCG.opt"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.optimization.opt_SCG.opt" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="class">
|
||||
<dt id="GPy.inference.optimization.optimization.opt_lbfgsb">
|
||||
<em class="property">class </em><code class="descclassname">GPy.inference.optimization.optimization.</code><code class="descname">opt_lbfgsb</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/optimization/optimization.html#opt_lbfgsb"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.optimization.opt_lbfgsb" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#GPy.inference.optimization.optimization.Optimizer" title="GPy.inference.optimization.optimization.Optimizer"><code class="xref py py-class docutils literal"><span class="pre">GPy.inference.optimization.optimization.Optimizer</span></code></a></p>
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.optimization.optimization.opt_lbfgsb.opt">
|
||||
<code class="descname">opt</code><span class="sig-paren">(</span><em>f_fp=None</em>, <em>f=None</em>, <em>fp=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/optimization/optimization.html#opt_lbfgsb.opt"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.optimization.opt_lbfgsb.opt" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Run the optimizer</p>
|
||||
</dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="class">
|
||||
<dt id="GPy.inference.optimization.optimization.opt_rasm">
|
||||
<em class="property">class </em><code class="descclassname">GPy.inference.optimization.optimization.</code><code class="descname">opt_rasm</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/optimization/optimization.html#opt_rasm"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.optimization.opt_rasm" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#GPy.inference.optimization.optimization.Optimizer" title="GPy.inference.optimization.optimization.Optimizer"><code class="xref py py-class docutils literal"><span class="pre">GPy.inference.optimization.optimization.Optimizer</span></code></a></p>
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.optimization.optimization.opt_rasm.opt">
|
||||
<code class="descname">opt</code><span class="sig-paren">(</span><em>f_fp=None</em>, <em>f=None</em>, <em>fp=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/optimization/optimization.html#opt_rasm.opt"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.optimization.opt_rasm.opt" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Run Rasmussen’s Conjugate Gradient optimizer</p>
|
||||
</dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="class">
|
||||
<dt id="GPy.inference.optimization.optimization.opt_simplex">
|
||||
<em class="property">class </em><code class="descclassname">GPy.inference.optimization.optimization.</code><code class="descname">opt_simplex</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/optimization/optimization.html#opt_simplex"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.optimization.opt_simplex" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#GPy.inference.optimization.optimization.Optimizer" title="GPy.inference.optimization.optimization.Optimizer"><code class="xref py py-class docutils literal"><span class="pre">GPy.inference.optimization.optimization.Optimizer</span></code></a></p>
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.optimization.optimization.opt_simplex.opt">
|
||||
<code class="descname">opt</code><span class="sig-paren">(</span><em>f_fp=None</em>, <em>f=None</em>, <em>fp=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/optimization/optimization.html#opt_simplex.opt"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.optimization.opt_simplex.opt" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>The simplex optimizer does not require gradients.</p>
|
||||
</dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="class">
|
||||
<dt id="GPy.inference.optimization.optimization.opt_tnc">
|
||||
<em class="property">class </em><code class="descclassname">GPy.inference.optimization.optimization.</code><code class="descname">opt_tnc</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/optimization/optimization.html#opt_tnc"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.optimization.opt_tnc" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#GPy.inference.optimization.optimization.Optimizer" title="GPy.inference.optimization.optimization.Optimizer"><code class="xref py py-class docutils literal"><span class="pre">GPy.inference.optimization.optimization.Optimizer</span></code></a></p>
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.optimization.optimization.opt_tnc.opt">
|
||||
<code class="descname">opt</code><span class="sig-paren">(</span><em>f_fp=None</em>, <em>f=None</em>, <em>fp=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/optimization/optimization.html#opt_tnc.opt"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.optimization.opt_tnc.opt" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Run the TNC optimizer</p>
|
||||
</dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-GPy.inference.optimization.scg">
|
||||
<span id="gpy-inference-optimization-scg-module"></span><h2>GPy.inference.optimization.scg module<a class="headerlink" href="#module-GPy.inference.optimization.scg" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="function">
|
||||
<dt id="GPy.inference.optimization.scg.SCG">
|
||||
<code class="descclassname">GPy.inference.optimization.scg.</code><code class="descname">SCG</code><span class="sig-paren">(</span><em>f</em>, <em>gradf</em>, <em>x</em>, <em>optargs=()</em>, <em>maxiters=500</em>, <em>max_f_eval=inf</em>, <em>display=True</em>, <em>xtol=None</em>, <em>ftol=None</em>, <em>gtol=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/optimization/scg.html#SCG"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.scg.SCG" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Optimisation through Scaled Conjugate Gradients (SCG)</p>
|
||||
<p>f: the objective function
|
||||
gradf : the gradient function (should return a 1D np.ndarray)
|
||||
x : the initial condition</p>
|
||||
<p>Returns
|
||||
x the optimal value for x
|
||||
flog : a list of all the objective values
|
||||
function_eval number of fn evaluations
|
||||
status: string describing convergence status</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.inference.optimization.scg.exponents">
|
||||
<code class="descclassname">GPy.inference.optimization.scg.</code><code class="descname">exponents</code><span class="sig-paren">(</span><em>fnow</em>, <em>current_grad</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/optimization/scg.html#exponents"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.scg.exponents" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.inference.optimization.scg.print_out">
|
||||
<code class="descclassname">GPy.inference.optimization.scg.</code><code class="descname">print_out</code><span class="sig-paren">(</span><em>len_maxiters</em>, <em>fnow</em>, <em>current_grad</em>, <em>beta</em>, <em>iteration</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/optimization/scg.html#print_out"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.scg.print_out" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="gpy-inference-optimization-sgd-module">
|
||||
<h2>GPy.inference.optimization.sgd module<a class="headerlink" href="#gpy-inference-optimization-sgd-module" title="Permalink to this headline">¶</a></h2>
|
||||
</div>
|
||||
<div class="section" id="module-GPy.inference.optimization.stochastics">
|
||||
<span id="gpy-inference-optimization-stochastics-module"></span><h2>GPy.inference.optimization.stochastics module<a class="headerlink" href="#module-GPy.inference.optimization.stochastics" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="class">
|
||||
<dt id="GPy.inference.optimization.stochastics.SparseGPMissing">
|
||||
<em class="property">class </em><code class="descclassname">GPy.inference.optimization.stochastics.</code><code class="descname">SparseGPMissing</code><span class="sig-paren">(</span><em>model</em>, <em>batchsize=1</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/optimization/stochastics.html#SparseGPMissing"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.stochastics.SparseGPMissing" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#GPy.inference.optimization.stochastics.StochasticStorage" title="GPy.inference.optimization.stochastics.StochasticStorage"><code class="xref py py-class docutils literal"><span class="pre">GPy.inference.optimization.stochastics.StochasticStorage</span></code></a></p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="class">
|
||||
<dt id="GPy.inference.optimization.stochastics.SparseGPStochastics">
|
||||
<em class="property">class </em><code class="descclassname">GPy.inference.optimization.stochastics.</code><code class="descname">SparseGPStochastics</code><span class="sig-paren">(</span><em>model</em>, <em>batchsize=1</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/optimization/stochastics.html#SparseGPStochastics"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.stochastics.SparseGPStochastics" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#GPy.inference.optimization.stochastics.StochasticStorage" title="GPy.inference.optimization.stochastics.StochasticStorage"><code class="xref py py-class docutils literal"><span class="pre">GPy.inference.optimization.stochastics.StochasticStorage</span></code></a></p>
|
||||
<p>For the sparse gp we need to store the dimension we are in,
|
||||
and the indices corresponding to those</p>
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.optimization.stochastics.SparseGPStochastics.do_stochastics">
|
||||
<code class="descname">do_stochastics</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/optimization/stochastics.html#SparseGPStochastics.do_stochastics"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.stochastics.SparseGPStochastics.do_stochastics" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.optimization.stochastics.SparseGPStochastics.reset">
|
||||
<code class="descname">reset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/optimization/stochastics.html#SparseGPStochastics.reset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.stochastics.SparseGPStochastics.reset" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="class">
|
||||
<dt id="GPy.inference.optimization.stochastics.StochasticStorage">
|
||||
<em class="property">class </em><code class="descclassname">GPy.inference.optimization.stochastics.</code><code class="descname">StochasticStorage</code><span class="sig-paren">(</span><em>model</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/optimization/stochastics.html#StochasticStorage"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.stochastics.StochasticStorage" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <code class="xref py py-class docutils literal"><span class="pre">object</span></code></p>
|
||||
<p>This is a container for holding the stochastic parameters,
|
||||
such as subset indices or step length and so on.</p>
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.optimization.stochastics.StochasticStorage.do_stochastics">
|
||||
<code class="descname">do_stochastics</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/optimization/stochastics.html#StochasticStorage.do_stochastics"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.stochastics.StochasticStorage.do_stochastics" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Update the internal state to the next batch of the stochastic
|
||||
descent algorithm.</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.inference.optimization.stochastics.StochasticStorage.reset">
|
||||
<code class="descname">reset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/inference/optimization/stochastics.html#StochasticStorage.reset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.inference.optimization.stochastics.StochasticStorage.reset" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Reset the state of this stochastics generator.</p>
|
||||
</dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-GPy.inference.optimization">
|
||||
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-GPy.inference.optimization" title="Permalink to this headline">¶</a></h2>
|
||||
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|
||||
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|
||||
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<h3><a href="index.html">Table Of Contents</a></h3>
|
||||
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|
||||
<li><a class="reference internal" href="#">GPy.inference.optimization package</a><ul>
|
||||
<li><a class="reference internal" href="#submodules">Submodules</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.inference.optimization.conjugate_gradient_descent">GPy.inference.optimization.conjugate_gradient_descent module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.inference.optimization.gradient_descent_update_rules">GPy.inference.optimization.gradient_descent_update_rules module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.inference.optimization.optimization">GPy.inference.optimization.optimization module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.inference.optimization.scg">GPy.inference.optimization.scg module</a></li>
|
||||
<li><a class="reference internal" href="#gpy-inference-optimization-sgd-module">GPy.inference.optimization.sgd module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.inference.optimization.stochastics">GPy.inference.optimization.stochastics module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.inference.optimization">Module contents</a></li>
|
||||
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|
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<div class="section" id="gpy-kern-src-psi-comp-package">
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<h1>GPy.kern._src.psi_comp package<a class="headerlink" href="#gpy-kern-src-psi-comp-package" title="Permalink to this headline">¶</a></h1>
|
||||
<div class="section" id="submodules">
|
||||
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
|
||||
</div>
|
||||
<div class="section" id="module-GPy.kern._src.psi_comp.linear_psi_comp">
|
||||
<span id="gpy-kern-src-psi-comp-linear-psi-comp-module"></span><h2>GPy.kern._src.psi_comp.linear_psi_comp module<a class="headerlink" href="#module-GPy.kern._src.psi_comp.linear_psi_comp" title="Permalink to this headline">¶</a></h2>
|
||||
<p>The package for the Psi statistics computation of the linear kernel for Bayesian GPLVM</p>
|
||||
<dl class="function">
|
||||
<dt id="GPy.kern._src.psi_comp.linear_psi_comp.psiDerivativecomputations">
|
||||
<code class="descclassname">GPy.kern._src.psi_comp.linear_psi_comp.</code><code class="descname">psiDerivativecomputations</code><span class="sig-paren">(</span><em>dL_dpsi0</em>, <em>dL_dpsi1</em>, <em>dL_dpsi2</em>, <em>variance</em>, <em>Z</em>, <em>variational_posterior</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/kern/_src/psi_comp/linear_psi_comp.html#psiDerivativecomputations"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.kern._src.psi_comp.linear_psi_comp.psiDerivativecomputations" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.kern._src.psi_comp.linear_psi_comp.psicomputations">
|
||||
<code class="descclassname">GPy.kern._src.psi_comp.linear_psi_comp.</code><code class="descname">psicomputations</code><span class="sig-paren">(</span><em>variance</em>, <em>Z</em>, <em>variational_posterior</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/kern/_src/psi_comp/linear_psi_comp.html#psicomputations"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.kern._src.psi_comp.linear_psi_comp.psicomputations" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Compute psi-statistics for ss-linear kernel</p>
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-GPy.kern._src.psi_comp.rbf_psi_comp">
|
||||
<span id="gpy-kern-src-psi-comp-rbf-psi-comp-module"></span><h2>GPy.kern._src.psi_comp.rbf_psi_comp module<a class="headerlink" href="#module-GPy.kern._src.psi_comp.rbf_psi_comp" title="Permalink to this headline">¶</a></h2>
|
||||
<p>The module for psi-statistics for RBF kernel</p>
|
||||
<dl class="function">
|
||||
<dt id="GPy.kern._src.psi_comp.rbf_psi_comp.psiDerivativecomputations">
|
||||
<code class="descclassname">GPy.kern._src.psi_comp.rbf_psi_comp.</code><code class="descname">psiDerivativecomputations</code><span class="sig-paren">(</span><em>dL_dpsi0</em>, <em>dL_dpsi1</em>, <em>dL_dpsi2</em>, <em>variance</em>, <em>lengthscale</em>, <em>Z</em>, <em>variational_posterior</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/kern/_src/psi_comp/rbf_psi_comp.html#psiDerivativecomputations"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.kern._src.psi_comp.rbf_psi_comp.psiDerivativecomputations" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.kern._src.psi_comp.rbf_psi_comp.psicomputations">
|
||||
<code class="descclassname">GPy.kern._src.psi_comp.rbf_psi_comp.</code><code class="descname">psicomputations</code><span class="sig-paren">(</span><em>variance</em>, <em>lengthscale</em>, <em>Z</em>, <em>variational_posterior</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/kern/_src/psi_comp/rbf_psi_comp.html#psicomputations"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.kern._src.psi_comp.rbf_psi_comp.psicomputations" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Z - MxQ
|
||||
mu - NxQ
|
||||
S - NxQ
|
||||
gamma - NxQ</p>
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-GPy.kern._src.psi_comp.rbf_psi_gpucomp">
|
||||
<span id="gpy-kern-src-psi-comp-rbf-psi-gpucomp-module"></span><h2>GPy.kern._src.psi_comp.rbf_psi_gpucomp module<a class="headerlink" href="#module-GPy.kern._src.psi_comp.rbf_psi_gpucomp" title="Permalink to this headline">¶</a></h2>
|
||||
<p>The module for psi-statistics for RBF kernel</p>
|
||||
<dl class="class">
|
||||
<dt id="GPy.kern._src.psi_comp.rbf_psi_gpucomp.PSICOMP_RBF_GPU">
|
||||
<em class="property">class </em><code class="descclassname">GPy.kern._src.psi_comp.rbf_psi_gpucomp.</code><code class="descname">PSICOMP_RBF_GPU</code><span class="sig-paren">(</span><em>threadnum=128</em>, <em>blocknum=15</em>, <em>GPU_direct=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/kern/_src/psi_comp/rbf_psi_gpucomp.html#PSICOMP_RBF_GPU"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.kern._src.psi_comp.rbf_psi_gpucomp.PSICOMP_RBF_GPU" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#GPy.kern._src.psi_comp.PSICOMP_RBF" title="GPy.kern._src.psi_comp.PSICOMP_RBF"><code class="xref py py-class docutils literal"><span class="pre">GPy.kern._src.psi_comp.PSICOMP_RBF</span></code></a></p>
|
||||
<dl class="method">
|
||||
<dt id="GPy.kern._src.psi_comp.rbf_psi_gpucomp.PSICOMP_RBF_GPU.get_dimensions">
|
||||
<code class="descname">get_dimensions</code><span class="sig-paren">(</span><em>Z</em>, <em>variational_posterior</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/kern/_src/psi_comp/rbf_psi_gpucomp.html#PSICOMP_RBF_GPU.get_dimensions"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.kern._src.psi_comp.rbf_psi_gpucomp.PSICOMP_RBF_GPU.get_dimensions" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="GPy.kern._src.psi_comp.rbf_psi_gpucomp.PSICOMP_RBF_GPU.psiDerivativecomputations">
|
||||
<code class="descname">psiDerivativecomputations</code><em class="property"> = <functools.partial object></em><a class="headerlink" href="#GPy.kern._src.psi_comp.rbf_psi_gpucomp.PSICOMP_RBF_GPU.psiDerivativecomputations" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="GPy.kern._src.psi_comp.rbf_psi_gpucomp.PSICOMP_RBF_GPU.psicomputations">
|
||||
<code class="descname">psicomputations</code><em class="property"> = <functools.partial object></em><a class="headerlink" href="#GPy.kern._src.psi_comp.rbf_psi_gpucomp.PSICOMP_RBF_GPU.psicomputations" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.kern._src.psi_comp.rbf_psi_gpucomp.PSICOMP_RBF_GPU.reset_derivative">
|
||||
<code class="descname">reset_derivative</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/kern/_src/psi_comp/rbf_psi_gpucomp.html#PSICOMP_RBF_GPU.reset_derivative"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.kern._src.psi_comp.rbf_psi_gpucomp.PSICOMP_RBF_GPU.reset_derivative" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.kern._src.psi_comp.rbf_psi_gpucomp.PSICOMP_RBF_GPU.sync_params">
|
||||
<code class="descname">sync_params</code><span class="sig-paren">(</span><em>lengthscale</em>, <em>Z</em>, <em>mu</em>, <em>S</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/kern/_src/psi_comp/rbf_psi_gpucomp.html#PSICOMP_RBF_GPU.sync_params"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.kern._src.psi_comp.rbf_psi_gpucomp.PSICOMP_RBF_GPU.sync_params" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-GPy.kern._src.psi_comp.sslinear_psi_comp">
|
||||
<span id="gpy-kern-src-psi-comp-sslinear-psi-comp-module"></span><h2>GPy.kern._src.psi_comp.sslinear_psi_comp module<a class="headerlink" href="#module-GPy.kern._src.psi_comp.sslinear_psi_comp" title="Permalink to this headline">¶</a></h2>
|
||||
<p>The package for the Psi statistics computation of the linear kernel for SSGPLVM</p>
|
||||
<dl class="function">
|
||||
<dt id="GPy.kern._src.psi_comp.sslinear_psi_comp.psiDerivativecomputations">
|
||||
<code class="descclassname">GPy.kern._src.psi_comp.sslinear_psi_comp.</code><code class="descname">psiDerivativecomputations</code><span class="sig-paren">(</span><em>dL_dpsi0</em>, <em>dL_dpsi1</em>, <em>dL_dpsi2</em>, <em>variance</em>, <em>Z</em>, <em>variational_posterior</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/kern/_src/psi_comp/sslinear_psi_comp.html#psiDerivativecomputations"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.kern._src.psi_comp.sslinear_psi_comp.psiDerivativecomputations" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="GPy.kern._src.psi_comp.sslinear_psi_comp.psicomputations">
|
||||
<code class="descclassname">GPy.kern._src.psi_comp.sslinear_psi_comp.</code><code class="descname">psicomputations</code><span class="sig-paren">(</span><em>variance</em>, <em>Z</em>, <em>variational_posterior</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/kern/_src/psi_comp/sslinear_psi_comp.html#psicomputations"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.kern._src.psi_comp.sslinear_psi_comp.psicomputations" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Compute psi-statistics for ss-linear kernel</p>
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-GPy.kern._src.psi_comp.ssrbf_psi_comp">
|
||||
<span id="gpy-kern-src-psi-comp-ssrbf-psi-comp-module"></span><h2>GPy.kern._src.psi_comp.ssrbf_psi_comp module<a class="headerlink" href="#module-GPy.kern._src.psi_comp.ssrbf_psi_comp" title="Permalink to this headline">¶</a></h2>
|
||||
<p>The package for the psi statistics computation</p>
|
||||
<dl class="function">
|
||||
<dt id="GPy.kern._src.psi_comp.ssrbf_psi_comp.psiDerivativecomputations">
|
||||
<code class="descclassname">GPy.kern._src.psi_comp.ssrbf_psi_comp.</code><code class="descname">psiDerivativecomputations</code><span class="sig-paren">(</span><em>dL_dpsi0</em>, <em>dL_dpsi1</em>, <em>dL_dpsi2</em>, <em>variance</em>, <em>lengthscale</em>, <em>Z</em>, <em>variational_posterior</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/kern/_src/psi_comp/ssrbf_psi_comp.html#psiDerivativecomputations"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.kern._src.psi_comp.ssrbf_psi_comp.psiDerivativecomputations" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-GPy.kern._src.psi_comp.ssrbf_psi_gpucomp">
|
||||
<span id="gpy-kern-src-psi-comp-ssrbf-psi-gpucomp-module"></span><h2>GPy.kern._src.psi_comp.ssrbf_psi_gpucomp module<a class="headerlink" href="#module-GPy.kern._src.psi_comp.ssrbf_psi_gpucomp" title="Permalink to this headline">¶</a></h2>
|
||||
<p>The module for psi-statistics for RBF kernel for Spike-and-Slab GPLVM</p>
|
||||
<dl class="class">
|
||||
<dt id="GPy.kern._src.psi_comp.ssrbf_psi_gpucomp.PSICOMP_SSRBF_GPU">
|
||||
<em class="property">class </em><code class="descclassname">GPy.kern._src.psi_comp.ssrbf_psi_gpucomp.</code><code class="descname">PSICOMP_SSRBF_GPU</code><span class="sig-paren">(</span><em>threadnum=128</em>, <em>blocknum=15</em>, <em>GPU_direct=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/kern/_src/psi_comp/ssrbf_psi_gpucomp.html#PSICOMP_SSRBF_GPU"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.kern._src.psi_comp.ssrbf_psi_gpucomp.PSICOMP_SSRBF_GPU" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#GPy.kern._src.psi_comp.PSICOMP_RBF" title="GPy.kern._src.psi_comp.PSICOMP_RBF"><code class="xref py py-class docutils literal"><span class="pre">GPy.kern._src.psi_comp.PSICOMP_RBF</span></code></a></p>
|
||||
<dl class="method">
|
||||
<dt id="GPy.kern._src.psi_comp.ssrbf_psi_gpucomp.PSICOMP_SSRBF_GPU.get_dimensions">
|
||||
<code class="descname">get_dimensions</code><span class="sig-paren">(</span><em>Z</em>, <em>variational_posterior</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/kern/_src/psi_comp/ssrbf_psi_gpucomp.html#PSICOMP_SSRBF_GPU.get_dimensions"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.kern._src.psi_comp.ssrbf_psi_gpucomp.PSICOMP_SSRBF_GPU.get_dimensions" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="GPy.kern._src.psi_comp.ssrbf_psi_gpucomp.PSICOMP_SSRBF_GPU.psiDerivativecomputations">
|
||||
<code class="descname">psiDerivativecomputations</code><em class="property"> = <functools.partial object></em><a class="headerlink" href="#GPy.kern._src.psi_comp.ssrbf_psi_gpucomp.PSICOMP_SSRBF_GPU.psiDerivativecomputations" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="GPy.kern._src.psi_comp.ssrbf_psi_gpucomp.PSICOMP_SSRBF_GPU.psicomputations">
|
||||
<code class="descname">psicomputations</code><em class="property"> = <functools.partial object></em><a class="headerlink" href="#GPy.kern._src.psi_comp.ssrbf_psi_gpucomp.PSICOMP_SSRBF_GPU.psicomputations" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.kern._src.psi_comp.ssrbf_psi_gpucomp.PSICOMP_SSRBF_GPU.reset_derivative">
|
||||
<code class="descname">reset_derivative</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/kern/_src/psi_comp/ssrbf_psi_gpucomp.html#PSICOMP_SSRBF_GPU.reset_derivative"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.kern._src.psi_comp.ssrbf_psi_gpucomp.PSICOMP_SSRBF_GPU.reset_derivative" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.kern._src.psi_comp.ssrbf_psi_gpucomp.PSICOMP_SSRBF_GPU.sync_params">
|
||||
<code class="descname">sync_params</code><span class="sig-paren">(</span><em>lengthscale</em>, <em>Z</em>, <em>mu</em>, <em>S</em>, <em>gamma</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/kern/_src/psi_comp/ssrbf_psi_gpucomp.html#PSICOMP_SSRBF_GPU.sync_params"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.kern._src.psi_comp.ssrbf_psi_gpucomp.PSICOMP_SSRBF_GPU.sync_params" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-GPy.kern._src.psi_comp">
|
||||
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-GPy.kern._src.psi_comp" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="class">
|
||||
<dt id="GPy.kern._src.psi_comp.PSICOMP_Linear">
|
||||
<em class="property">class </em><code class="descclassname">GPy.kern._src.psi_comp.</code><code class="descname">PSICOMP_Linear</code><span class="sig-paren">(</span><em>*a</em>, <em>**kw</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/kern/_src/psi_comp.html#PSICOMP_Linear"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.kern._src.psi_comp.PSICOMP_Linear" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="GPy.core.parameterization.html#GPy.core.parameterization.parameter_core.Pickleable" title="GPy.core.parameterization.parameter_core.Pickleable"><code class="xref py py-class docutils literal"><span class="pre">GPy.core.parameterization.parameter_core.Pickleable</span></code></a></p>
|
||||
<dl class="attribute">
|
||||
<dt id="GPy.kern._src.psi_comp.PSICOMP_Linear.psiDerivativecomputations">
|
||||
<code class="descname">psiDerivativecomputations</code><em class="property"> = <functools.partial object></em><a class="headerlink" href="#GPy.kern._src.psi_comp.PSICOMP_Linear.psiDerivativecomputations" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="GPy.kern._src.psi_comp.PSICOMP_Linear.psicomputations">
|
||||
<code class="descname">psicomputations</code><em class="property"> = <functools.partial object></em><a class="headerlink" href="#GPy.kern._src.psi_comp.PSICOMP_Linear.psicomputations" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="class">
|
||||
<dt id="GPy.kern._src.psi_comp.PSICOMP_RBF">
|
||||
<em class="property">class </em><code class="descclassname">GPy.kern._src.psi_comp.</code><code class="descname">PSICOMP_RBF</code><span class="sig-paren">(</span><em>*a</em>, <em>**kw</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/kern/_src/psi_comp.html#PSICOMP_RBF"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.kern._src.psi_comp.PSICOMP_RBF" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="GPy.core.parameterization.html#GPy.core.parameterization.parameter_core.Pickleable" title="GPy.core.parameterization.parameter_core.Pickleable"><code class="xref py py-class docutils literal"><span class="pre">GPy.core.parameterization.parameter_core.Pickleable</span></code></a></p>
|
||||
<dl class="attribute">
|
||||
<dt id="GPy.kern._src.psi_comp.PSICOMP_RBF.psiDerivativecomputations">
|
||||
<code class="descname">psiDerivativecomputations</code><em class="property"> = <functools.partial object></em><a class="headerlink" href="#GPy.kern._src.psi_comp.PSICOMP_RBF.psiDerivativecomputations" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="GPy.kern._src.psi_comp.PSICOMP_RBF.psicomputations">
|
||||
<code class="descname">psicomputations</code><em class="property"> = <functools.partial object></em><a class="headerlink" href="#GPy.kern._src.psi_comp.PSICOMP_RBF.psicomputations" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
</div>
|
||||
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<h3><a href="index.html">Table Of Contents</a></h3>
|
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<ul>
|
||||
<li><a class="reference internal" href="#">GPy.kern._src.psi_comp package</a><ul>
|
||||
<li><a class="reference internal" href="#submodules">Submodules</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.kern._src.psi_comp.linear_psi_comp">GPy.kern._src.psi_comp.linear_psi_comp module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.kern._src.psi_comp.rbf_psi_comp">GPy.kern._src.psi_comp.rbf_psi_comp module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.kern._src.psi_comp.rbf_psi_gpucomp">GPy.kern._src.psi_comp.rbf_psi_gpucomp module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.kern._src.psi_comp.sslinear_psi_comp">GPy.kern._src.psi_comp.sslinear_psi_comp module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.kern._src.psi_comp.ssrbf_psi_comp">GPy.kern._src.psi_comp.ssrbf_psi_comp module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.kern._src.psi_comp.ssrbf_psi_gpucomp">GPy.kern._src.psi_comp.ssrbf_psi_gpucomp module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.kern._src.psi_comp">Module contents</a></li>
|
||||
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|
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<h1>GPy.mappings package<a class="headerlink" href="#gpy-mappings-package" title="Permalink to this headline">¶</a></h1>
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<div class="section" id="submodules">
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<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
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<div class="section" id="module-GPy.mappings.additive">
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<span id="gpy-mappings-additive-module"></span><h2>GPy.mappings.additive module<a class="headerlink" href="#module-GPy.mappings.additive" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="class">
|
||||
<dt id="GPy.mappings.additive.Additive">
|
||||
<em class="property">class </em><code class="descclassname">GPy.mappings.additive.</code><code class="descname">Additive</code><span class="sig-paren">(</span><em>mapping1</em>, <em>mapping2</em>, <em>tensor=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/mappings/additive.html#Additive"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.mappings.additive.Additive" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="GPy.core.html#GPy.core.mapping.Mapping" title="GPy.core.mapping.Mapping"><code class="xref py py-class docutils literal"><span class="pre">GPy.core.mapping.Mapping</span></code></a></p>
|
||||
<p>Mapping based on adding two existing mappings together.</p>
|
||||
<div class="math">
|
||||
<p><img src="_images/math/2984fd9b4b48bc7212f5b97e2ffabbc4060cb544.png" alt="f(\mathbf{x}*) = f_1(\mathbf{x}*) + f_2(\mathbf(x)*)"/></p>
|
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||||
<col class="field-body" />
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<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
|
||||
<li><strong>mapping1</strong> (<em>GPy.mappings.Mapping</em>) – first mapping to add together.</li>
|
||||
<li><strong>mapping2</strong> (<em>GPy.mappings.Mapping</em>) – second mapping to add together.</li>
|
||||
<li><strong>tensor</strong> (<em>bool</em>) – whether or not to use the tensor product of input spaces</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<dl class="method">
|
||||
<dt id="GPy.mappings.additive.Additive.df_dX">
|
||||
<code class="descname">df_dX</code><span class="sig-paren">(</span><em>dL_df</em>, <em>X</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/mappings/additive.html#Additive.df_dX"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.mappings.additive.Additive.df_dX" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.mappings.additive.Additive.df_dtheta">
|
||||
<code class="descname">df_dtheta</code><span class="sig-paren">(</span><em>dL_df</em>, <em>X</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/mappings/additive.html#Additive.df_dtheta"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.mappings.additive.Additive.df_dtheta" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.mappings.additive.Additive.f">
|
||||
<code class="descname">f</code><span class="sig-paren">(</span><em>X</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/mappings/additive.html#Additive.f"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.mappings.additive.Additive.f" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.mappings.additive.Additive.randomize">
|
||||
<code class="descname">randomize</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/mappings/additive.html#Additive.randomize"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.mappings.additive.Additive.randomize" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-GPy.mappings.kernel">
|
||||
<span id="gpy-mappings-kernel-module"></span><h2>GPy.mappings.kernel module<a class="headerlink" href="#module-GPy.mappings.kernel" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="class">
|
||||
<dt id="GPy.mappings.kernel.Kernel">
|
||||
<em class="property">class </em><code class="descclassname">GPy.mappings.kernel.</code><code class="descname">Kernel</code><span class="sig-paren">(</span><em>X</em>, <em>output_dim=1</em>, <em>kernel=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/mappings/kernel.html#Kernel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.mappings.kernel.Kernel" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="GPy.core.html#GPy.core.mapping.Mapping" title="GPy.core.mapping.Mapping"><code class="xref py py-class docutils literal"><span class="pre">GPy.core.mapping.Mapping</span></code></a></p>
|
||||
<p>Mapping based on a kernel/covariance function.</p>
|
||||
<div class="math">
|
||||
<p><img src="_images/math/67a3ca8b772a75e4d54fe3e40f9e567f00b66f2f.png" alt="f(\mathbf{x}*) = \mathbf{A}\mathbf{k}(\mathbf{X}, \mathbf{x}^*) + \mathbf{b}"/></p>
|
||||
</div><table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
|
||||
<li><strong>X</strong> (<em>ndarray</em>) – input observations containing <img class="math" src="_images/math/7a9b78bdb36f2fbd5ca087212e58fc74dde40e86.png" alt="\mathbf{X}"/></li>
|
||||
<li><strong>output_dim</strong> (<em>int</em>) – dimension of output.</li>
|
||||
<li><strong>kernel</strong> (<em>GPy.kern.kern</em>) – a GPy kernel, defaults to GPy.kern.RBF</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<dl class="method">
|
||||
<dt id="GPy.mappings.kernel.Kernel.df_dX">
|
||||
<code class="descname">df_dX</code><span class="sig-paren">(</span><em>dL_df</em>, <em>X</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/mappings/kernel.html#Kernel.df_dX"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.mappings.kernel.Kernel.df_dX" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.mappings.kernel.Kernel.df_dtheta">
|
||||
<code class="descname">df_dtheta</code><span class="sig-paren">(</span><em>dL_df</em>, <em>X</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/mappings/kernel.html#Kernel.df_dtheta"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.mappings.kernel.Kernel.df_dtheta" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.mappings.kernel.Kernel.f">
|
||||
<code class="descname">f</code><span class="sig-paren">(</span><em>X</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/mappings/kernel.html#Kernel.f"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.mappings.kernel.Kernel.f" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.mappings.kernel.Kernel.randomize">
|
||||
<code class="descname">randomize</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/mappings/kernel.html#Kernel.randomize"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.mappings.kernel.Kernel.randomize" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-GPy.mappings.linear">
|
||||
<span id="gpy-mappings-linear-module"></span><h2>GPy.mappings.linear module<a class="headerlink" href="#module-GPy.mappings.linear" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="class">
|
||||
<dt id="GPy.mappings.linear.Linear">
|
||||
<em class="property">class </em><code class="descclassname">GPy.mappings.linear.</code><code class="descname">Linear</code><span class="sig-paren">(</span><em>input_dim=1</em>, <em>output_dim=1</em>, <em>name='linear'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/mappings/linear.html#Linear"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.mappings.linear.Linear" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="GPy.core.html#GPy.core.mapping.Bijective_mapping" title="GPy.core.mapping.Bijective_mapping"><code class="xref py py-class docutils literal"><span class="pre">GPy.core.mapping.Bijective_mapping</span></code></a></p>
|
||||
<p>Mapping based on a linear model.</p>
|
||||
<div class="math">
|
||||
<p><img src="_images/math/2520c5bbd4ca1928f464fd3873b10a9b0bde8c18.png" alt="f(\mathbf{x}*) = \mathbf{W}\mathbf{x}^* + \mathbf{b}"/></p>
|
||||
</div><table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
|
||||
<li><strong>X</strong> (<em>ndarray</em>) – input observations</li>
|
||||
<li><strong>output_dim</strong> (<em>int</em>) – dimension of output.</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<dl class="method">
|
||||
<dt id="GPy.mappings.linear.Linear.dL_dX">
|
||||
<code class="descname">dL_dX</code><span class="sig-paren">(</span><em>partial</em>, <em>X</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/mappings/linear.html#Linear.dL_dX"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.mappings.linear.Linear.dL_dX" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>The gradient of L with respect to the inputs to the mapping, where L is a function that is dependent on the output of the mapping, f.</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.mappings.linear.Linear.df_dtheta">
|
||||
<code class="descname">df_dtheta</code><span class="sig-paren">(</span><em>dL_df</em>, <em>X</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/mappings/linear.html#Linear.df_dtheta"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.mappings.linear.Linear.df_dtheta" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.mappings.linear.Linear.f">
|
||||
<code class="descname">f</code><span class="sig-paren">(</span><em>X</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/mappings/linear.html#Linear.f"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.mappings.linear.Linear.f" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.mappings.linear.Linear.g">
|
||||
<code class="descname">g</code><span class="sig-paren">(</span><em>f</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/mappings/linear.html#Linear.g"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.mappings.linear.Linear.g" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-GPy.mappings.mlp">
|
||||
<span id="gpy-mappings-mlp-module"></span><h2>GPy.mappings.mlp module<a class="headerlink" href="#module-GPy.mappings.mlp" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="class">
|
||||
<dt id="GPy.mappings.mlp.MLP">
|
||||
<em class="property">class </em><code class="descclassname">GPy.mappings.mlp.</code><code class="descname">MLP</code><span class="sig-paren">(</span><em>input_dim=1</em>, <em>output_dim=1</em>, <em>hidden_dim=3</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/mappings/mlp.html#MLP"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.mappings.mlp.MLP" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="GPy.core.html#GPy.core.mapping.Mapping" title="GPy.core.mapping.Mapping"><code class="xref py py-class docutils literal"><span class="pre">GPy.core.mapping.Mapping</span></code></a></p>
|
||||
<p>Mapping based on a multi-layer perceptron neural network model.</p>
|
||||
<div class="math">
|
||||
<p><img src="_images/math/5fb1ead7a3ae880faf3e493e050ee55a0e435038.png" alt="f(\mathbf{x}*) = \mathbf{W}^0\boldsymbol{\phi}(\mathbf{W}^1\mathbf{x}+\mathbf{b}^1)^* + \mathbf{b}^0"/></p>
|
||||
</div><p>where</p>
|
||||
<div class="math">
|
||||
<p><img src="_images/math/4b19c5c53c58678e9292c8e646b72103694e99f2.png" alt="\phi(\cdot) = \text{tanh}(\cdot)"/></p>
|
||||
</div><table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
|
||||
<li><strong>X</strong> (<em>ndarray</em>) – input observations</li>
|
||||
<li><strong>output_dim</strong> (<em>int</em>) – dimension of output.</li>
|
||||
<li><strong>hidden_dim</strong> (<em>int or list of ints.</em>) – dimension of hidden layer. If it is an int, there is one hidden layer of the given dimension. If it is a list of ints there are as manny hidden layers as the length of the list, each with the given number of hidden nodes in it.</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<dl class="method">
|
||||
<dt id="GPy.mappings.mlp.MLP.df_dX">
|
||||
<code class="descname">df_dX</code><span class="sig-paren">(</span><em>dL_df</em>, <em>X</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/mappings/mlp.html#MLP.df_dX"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.mappings.mlp.MLP.df_dX" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.mappings.mlp.MLP.df_dtheta">
|
||||
<code class="descname">df_dtheta</code><span class="sig-paren">(</span><em>dL_df</em>, <em>X</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/mappings/mlp.html#MLP.df_dtheta"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.mappings.mlp.MLP.df_dtheta" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.mappings.mlp.MLP.f">
|
||||
<code class="descname">f</code><span class="sig-paren">(</span><em>X</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/mappings/mlp.html#MLP.f"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.mappings.mlp.MLP.f" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.mappings.mlp.MLP.randomize">
|
||||
<code class="descname">randomize</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/mappings/mlp.html#MLP.randomize"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.mappings.mlp.MLP.randomize" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-GPy.mappings">
|
||||
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-GPy.mappings" title="Permalink to this headline">¶</a></h2>
|
||||
</div>
|
||||
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|
||||
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<h3><a href="index.html">Table Of Contents</a></h3>
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|
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<li><a class="reference internal" href="#">GPy.mappings package</a><ul>
|
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<li><a class="reference internal" href="#submodules">Submodules</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.mappings.additive">GPy.mappings.additive module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.mappings.kernel">GPy.mappings.kernel module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.mappings.linear">GPy.mappings.linear module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.mappings.mlp">GPy.mappings.mlp module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.mappings">Module contents</a></li>
|
||||
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|
||||
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<div class="section" id="gpy-plotting-matplot-dep-latent-space-visualizations-controllers-package">
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<h1>GPy.plotting.matplot_dep.latent_space_visualizations.controllers package<a class="headerlink" href="#gpy-plotting-matplot-dep-latent-space-visualizations-controllers-package" title="Permalink to this headline">¶</a></h1>
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<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
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<div class="section" id="module-GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller">
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<span id="gpy-plotting-matplot-dep-latent-space-visualizations-controllers-axis-event-controller-module"></span><h2>GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller module<a class="headerlink" href="#module-GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller" title="Permalink to this headline">¶</a></h2>
|
||||
<p>Created on 24 Jul 2013</p>
|
||||
<p>@author: maxz</p>
|
||||
<dl class="class">
|
||||
<dt id="GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.AxisChangedController">
|
||||
<em class="property">class </em><code class="descclassname">GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.</code><code class="descname">AxisChangedController</code><span class="sig-paren">(</span><em>ax</em>, <em>update_lim=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/plotting/matplot_dep/latent_space_visualizations/controllers/axis_event_controller.html#AxisChangedController"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.AxisChangedController" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.AxisEventController" title="GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.AxisEventController"><code class="xref py py-class docutils literal"><span class="pre">GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.AxisEventController</span></code></a></p>
|
||||
<p>Buffered control of axis limit changes</p>
|
||||
<dl class="method">
|
||||
<dt id="GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.AxisChangedController.extent">
|
||||
<code class="descname">extent</code><span class="sig-paren">(</span><em>lim</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/plotting/matplot_dep/latent_space_visualizations/controllers/axis_event_controller.html#AxisChangedController.extent"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.AxisChangedController.extent" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.AxisChangedController.lim_changed">
|
||||
<code class="descname">lim_changed</code><span class="sig-paren">(</span><em>axlim</em>, <em>savedlim</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/plotting/matplot_dep/latent_space_visualizations/controllers/axis_event_controller.html#AxisChangedController.lim_changed"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.AxisChangedController.lim_changed" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.AxisChangedController.update">
|
||||
<code class="descname">update</code><span class="sig-paren">(</span><em>ax</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/plotting/matplot_dep/latent_space_visualizations/controllers/axis_event_controller.html#AxisChangedController.update"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.AxisChangedController.update" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.AxisChangedController.xlim_changed">
|
||||
<code class="descname">xlim_changed</code><span class="sig-paren">(</span><em>ax</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/plotting/matplot_dep/latent_space_visualizations/controllers/axis_event_controller.html#AxisChangedController.xlim_changed"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.AxisChangedController.xlim_changed" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.AxisChangedController.ylim_changed">
|
||||
<code class="descname">ylim_changed</code><span class="sig-paren">(</span><em>ax</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/plotting/matplot_dep/latent_space_visualizations/controllers/axis_event_controller.html#AxisChangedController.ylim_changed"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.AxisChangedController.ylim_changed" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="class">
|
||||
<dt id="GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.AxisEventController">
|
||||
<em class="property">class </em><code class="descclassname">GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.</code><code class="descname">AxisEventController</code><span class="sig-paren">(</span><em>ax</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/plotting/matplot_dep/latent_space_visualizations/controllers/axis_event_controller.html#AxisEventController"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.AxisEventController" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <code class="xref py py-class docutils literal"><span class="pre">object</span></code></p>
|
||||
<dl class="method">
|
||||
<dt id="GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.AxisEventController.activate">
|
||||
<code class="descname">activate</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/plotting/matplot_dep/latent_space_visualizations/controllers/axis_event_controller.html#AxisEventController.activate"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.AxisEventController.activate" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.AxisEventController.deactivate">
|
||||
<code class="descname">deactivate</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/plotting/matplot_dep/latent_space_visualizations/controllers/axis_event_controller.html#AxisEventController.deactivate"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.AxisEventController.deactivate" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.AxisEventController.xlim_changed">
|
||||
<code class="descname">xlim_changed</code><span class="sig-paren">(</span><em>ax</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/plotting/matplot_dep/latent_space_visualizations/controllers/axis_event_controller.html#AxisEventController.xlim_changed"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.AxisEventController.xlim_changed" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.AxisEventController.ylim_changed">
|
||||
<code class="descname">ylim_changed</code><span class="sig-paren">(</span><em>ax</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/plotting/matplot_dep/latent_space_visualizations/controllers/axis_event_controller.html#AxisEventController.ylim_changed"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.AxisEventController.ylim_changed" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="class">
|
||||
<dt id="GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.BufferedAxisChangedController">
|
||||
<em class="property">class </em><code class="descclassname">GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.</code><code class="descname">BufferedAxisChangedController</code><span class="sig-paren">(</span><em>ax</em>, <em>plot_function</em>, <em>plot_limits</em>, <em>resolution=50</em>, <em>update_lim=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/plotting/matplot_dep/latent_space_visualizations/controllers/axis_event_controller.html#BufferedAxisChangedController"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.BufferedAxisChangedController" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.AxisChangedController" title="GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.AxisChangedController"><code class="xref py py-class docutils literal"><span class="pre">GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.AxisChangedController</span></code></a></p>
|
||||
<dl class="method">
|
||||
<dt id="GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.BufferedAxisChangedController.get_grid">
|
||||
<code class="descname">get_grid</code><span class="sig-paren">(</span><em>buffered=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/plotting/matplot_dep/latent_space_visualizations/controllers/axis_event_controller.html#BufferedAxisChangedController.get_grid"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.BufferedAxisChangedController.get_grid" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.BufferedAxisChangedController.recompute_X">
|
||||
<code class="descname">recompute_X</code><span class="sig-paren">(</span><em>buffered=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/plotting/matplot_dep/latent_space_visualizations/controllers/axis_event_controller.html#BufferedAxisChangedController.recompute_X"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.BufferedAxisChangedController.recompute_X" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.BufferedAxisChangedController.update">
|
||||
<code class="descname">update</code><span class="sig-paren">(</span><em>ax</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/plotting/matplot_dep/latent_space_visualizations/controllers/axis_event_controller.html#BufferedAxisChangedController.update"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.BufferedAxisChangedController.update" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.BufferedAxisChangedController.update_view">
|
||||
<code class="descname">update_view</code><span class="sig-paren">(</span><em>view</em>, <em>X</em>, <em>xmin</em>, <em>xmax</em>, <em>ymin</em>, <em>ymax</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/plotting/matplot_dep/latent_space_visualizations/controllers/axis_event_controller.html#BufferedAxisChangedController.update_view"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.BufferedAxisChangedController.update_view" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-GPy.plotting.matplot_dep.latent_space_visualizations.controllers.imshow_controller">
|
||||
<span id="gpy-plotting-matplot-dep-latent-space-visualizations-controllers-imshow-controller-module"></span><h2>GPy.plotting.matplot_dep.latent_space_visualizations.controllers.imshow_controller module<a class="headerlink" href="#module-GPy.plotting.matplot_dep.latent_space_visualizations.controllers.imshow_controller" title="Permalink to this headline">¶</a></h2>
|
||||
<p>Created on 24 Jul 2013</p>
|
||||
<p>@author: maxz</p>
|
||||
<dl class="class">
|
||||
<dt id="GPy.plotting.matplot_dep.latent_space_visualizations.controllers.imshow_controller.ImAnnotateController">
|
||||
<em class="property">class </em><code class="descclassname">GPy.plotting.matplot_dep.latent_space_visualizations.controllers.imshow_controller.</code><code class="descname">ImAnnotateController</code><span class="sig-paren">(</span><em>ax</em>, <em>plot_function</em>, <em>plot_limits</em>, <em>resolution=20</em>, <em>update_lim=0.99</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/plotting/matplot_dep/latent_space_visualizations/controllers/imshow_controller.html#ImAnnotateController"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.plotting.matplot_dep.latent_space_visualizations.controllers.imshow_controller.ImAnnotateController" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#GPy.plotting.matplot_dep.latent_space_visualizations.controllers.imshow_controller.ImshowController" title="GPy.plotting.matplot_dep.latent_space_visualizations.controllers.imshow_controller.ImshowController"><code class="xref py py-class docutils literal"><span class="pre">GPy.plotting.matplot_dep.latent_space_visualizations.controllers.imshow_controller.ImshowController</span></code></a></p>
|
||||
<dl class="method">
|
||||
<dt id="GPy.plotting.matplot_dep.latent_space_visualizations.controllers.imshow_controller.ImAnnotateController.update_view">
|
||||
<code class="descname">update_view</code><span class="sig-paren">(</span><em>view</em>, <em>X</em>, <em>xmin</em>, <em>xmax</em>, <em>ymin</em>, <em>ymax</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/plotting/matplot_dep/latent_space_visualizations/controllers/imshow_controller.html#ImAnnotateController.update_view"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.plotting.matplot_dep.latent_space_visualizations.controllers.imshow_controller.ImAnnotateController.update_view" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="class">
|
||||
<dt id="GPy.plotting.matplot_dep.latent_space_visualizations.controllers.imshow_controller.ImshowController">
|
||||
<em class="property">class </em><code class="descclassname">GPy.plotting.matplot_dep.latent_space_visualizations.controllers.imshow_controller.</code><code class="descname">ImshowController</code><span class="sig-paren">(</span><em>ax</em>, <em>plot_function</em>, <em>plot_limits</em>, <em>resolution=50</em>, <em>update_lim=0.8</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/plotting/matplot_dep/latent_space_visualizations/controllers/imshow_controller.html#ImshowController"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.plotting.matplot_dep.latent_space_visualizations.controllers.imshow_controller.ImshowController" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.BufferedAxisChangedController" title="GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.BufferedAxisChangedController"><code class="xref py py-class docutils literal"><span class="pre">GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller.BufferedAxisChangedController</span></code></a></p>
|
||||
<dl class="method">
|
||||
<dt id="GPy.plotting.matplot_dep.latent_space_visualizations.controllers.imshow_controller.ImshowController.update_view">
|
||||
<code class="descname">update_view</code><span class="sig-paren">(</span><em>view</em>, <em>X</em>, <em>xmin</em>, <em>xmax</em>, <em>ymin</em>, <em>ymax</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/GPy/plotting/matplot_dep/latent_space_visualizations/controllers/imshow_controller.html#ImshowController.update_view"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#GPy.plotting.matplot_dep.latent_space_visualizations.controllers.imshow_controller.ImshowController.update_view" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
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|
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|
||||
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-GPy.plotting.matplot_dep.latent_space_visualizations.controllers" title="Permalink to this headline">¶</a></h2>
|
||||
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|
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|
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<li><a class="reference internal" href="#">GPy.plotting.matplot_dep.latent_space_visualizations.controllers package</a><ul>
|
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<li><a class="reference internal" href="#submodules">Submodules</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller">GPy.plotting.matplot_dep.latent_space_visualizations.controllers.axis_event_controller module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.plotting.matplot_dep.latent_space_visualizations.controllers.imshow_controller">GPy.plotting.matplot_dep.latent_space_visualizations.controllers.imshow_controller module</a></li>
|
||||
<li><a class="reference internal" href="#module-GPy.plotting.matplot_dep.latent_space_visualizations.controllers">Module contents</a></li>
|
||||
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<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-GPy.plotting.matplot_dep.latent_space_visualizations" title="Permalink to this headline">¶</a></h2>
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<h3><a href="index.html">Table Of Contents</a></h3>
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<ul>
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<li><a class="reference internal" href="#">GPy.plotting.matplot_dep.latent_space_visualizations package</a><ul>
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<li><a class="reference internal" href="#subpackages">Subpackages</a></li>
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