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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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<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#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.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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<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>
|
||
|
|
<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.plot_f">
|
||
|
|
<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.
|
||
|
|
This is a call to plot with plot_raw=True.
|
||
|
|
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>
|
||
|
|
<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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</div>
|
||
|
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</div>
|
||
|
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|
||
|
|
|
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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.core package</a><ul>
|
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<li><a class="reference internal" href="#subpackages">Subpackages</a></li>
|
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<li><a class="reference internal" href="#submodules">Submodules</a></li>
|
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|
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<li><a class="reference internal" href="#module-GPy.core.gp">GPy.core.gp module</a></li>
|
||
|
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<li><a class="reference internal" href="#module-GPy.core.mapping">GPy.core.mapping module</a></li>
|
||
|
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<li><a class="reference internal" href="#module-GPy.core.model">GPy.core.model module</a></li>
|
||
|
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<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>
|
||
|
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<li><a class="reference internal" href="#gpy-core-symbolic-module">GPy.core.symbolic module</a></li>
|
||
|
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<li><a class="reference internal" href="#module-GPy.core">Module contents</a></li>
|
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