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Feb 26, 2023
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Release 0.2.4 #71

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Update version number
yuta-nakahara Feb 26, 2023
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yuta-nakahara Feb 26, 2023
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10 changes: 5 additions & 5 deletions README.md
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Expand Up @@ -137,18 +137,18 @@ Plain text

```
Y. Nakahara, N. Ichijo, K. Shimada, Y. Iikubo,
S. Saito, K. Kazama, T. Matsushima, BayesML Developers, ``BayesML 0.2.3,''
S. Saito, K. Kazama, T. Matsushima, BayesML Developers, ``BayesML 0.2.4,''
[Online] https://github.com/yuta-nakahara/BayesML
```

BibTeX

``` bibtex
@misc{bayesml,
author = {Nakahara Yuta and Ichijo Naoki and Shimada Koshi and
Iikubo Yuji and Saito Shota and Kazama Koki and
Matsushima Toshiyasu and {BayesML Developers}},
title = {BayesML 0.2.3},
author = {Nakahara, Yuta and Ichijo, Naoki and Shimada, Koshi and
Iikubo, Yuji and Saito, Shota and Kazama, Koki and
Matsushima, Toshiyasu and {BayesML Developers}},
title = {BayesML 0.2.4},
howpublished = {\url{https://github.com/yuta-nakahara/BayesML}},
year = {2022}
}
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10 changes: 5 additions & 5 deletions README_jp.md
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Expand Up @@ -134,18 +134,18 @@ BayesMLへのコントリビューションを考えてくださってありが

```
Y. Nakahara, N. Ichijo, K. Shimada, Y. Iikubo,
S. Saito, K. Kazama, T. Matsushima, BayesML Developers, ``BayesML 0.2.3,''
S. Saito, K. Kazama, T. Matsushima, BayesML Developers, ``BayesML 0.2.4,''
[Online] https://github.com/yuta-nakahara/BayesML
```

BibTeX

``` bibtex
@misc{bayesml,
author = {Nakahara Yuta and Ichijo Naoki and Shimada Koshi and
Iikubo Yuji and Saito Shota and Kazama Koki and
Matsushima Toshiyasu and {BayesML Developers}},
title = {BayesML 0.2.3},
author = {Nakahara, Yuta and Ichijo, Naoki and Shimada, Koshi and
Iikubo, Yuji and Saito, Shota and Kazama, Koki and
Matsushima, Toshiyasu and {BayesML Developers}},
title = {BayesML 0.2.4},
howpublished = {\url{https://github.com/yuta-nakahara/BayesML}},
year = {2022}
}
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10 changes: 5 additions & 5 deletions doc/developers.rst
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.. code-block::

Y. Nakahara, N. Ichijo, K. Shimada, Y. Iikubo,
S. Saito, K. Kazama, T. Matsushima, BayesML Developers, ``BayesML 0.2.3,''
S. Saito, K. Kazama, T. Matsushima, BayesML Developers, ``BayesML 0.2.4,''
[Online] https://github.com/yuta-nakahara/BayesML


Expand All @@ -68,10 +68,10 @@ BibTeX
.. code-block:: bibtex

@misc{bayesml,
author = {Nakahara Yuta and Ichijo Naoki and Shimada Koshi and
Iikubo Yuji and Saito Shota and Kazama Koki and
Matsushima Toshiyasu and {BayesML Developers}},
title = {BayesML 0.2.3},
author = {Nakahara, Yuta and Ichijo, Naoki and Shimada, Koshi and
Iikubo, Yuji and Saito, Shota and Kazama, Koki and
Matsushima, Toshiyasu and {BayesML Developers}},
title = {BayesML 0.2.4},
howpublished = {\url{https://github.com/yuta-nakahara/BayesML}},
year = {2022}
}
10 changes: 5 additions & 5 deletions doc/index.rst
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.. code-block::

Y. Nakahara, N. Ichijo, K. Shimada, Y. Iikubo,
S. Saito, K. Kazama, T. Matsushima, BayesML Developers, ``BayesML 0.2.3,''
S. Saito, K. Kazama, T. Matsushima, BayesML Developers, ``BayesML 0.2.4,''
[Online] https://github.com/yuta-nakahara/BayesML


Expand All @@ -159,10 +159,10 @@ BibTeX
.. code-block:: bibtex

@misc{bayesml,
author = {Nakahara Yuta and Ichijo Naoki and Shimada Koshi and
Iikubo Yuji and Saito Shota and Kazama Koki and
Matsushima Toshiyasu and {BayesML Developers}},
title = {BayesML 0.2.3},
author = {Nakahara, Yuta and Ichijo, Naoki and Shimada, Koshi and
Iikubo, Yuji and Saito, Shota and Kazama, Koki and
Matsushima, Toshiyasu and {BayesML Developers}},
title = {BayesML 0.2.4},
howpublished = {\url{https://github.com/yuta-nakahara/BayesML}},
year = {2022}
}
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10 changes: 5 additions & 5 deletions docs/_sources/developers.rst.txt
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Expand Up @@ -59,7 +59,7 @@ Plain text
.. code-block::

Y. Nakahara, N. Ichijo, K. Shimada, Y. Iikubo,
S. Saito, K. Kazama, T. Matsushima, BayesML Developers, ``BayesML 0.2.1,''
S. Saito, K. Kazama, T. Matsushima, BayesML Developers, ``BayesML 0.2.4,''
[Online] https://github.com/yuta-nakahara/BayesML


Expand All @@ -68,10 +68,10 @@ BibTeX
.. code-block:: bibtex

@misc{bayesml,
author = {Nakahara Yuta and Ichijo Naoki and Shimada Koshi and
Iikubo Yuji and Saito Shota and Kazama Koki and
Matsushima Toshiyasu and {BayesML Developers}},
title = {BayesML 0.2.1},
author = {Nakahara, Yuta and Ichijo, Naoki and Shimada, Koshi and
Iikubo, Yuji and Saito, Shota and Kazama, Koki and
Matsushima, Toshiyasu and {BayesML Developers}},
title = {BayesML 0.2.4},
howpublished = {\url{https://github.com/yuta-nakahara/BayesML}},
year = {2022}
}
10 changes: 5 additions & 5 deletions docs/_sources/index.rst.txt
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Expand Up @@ -150,7 +150,7 @@ Plain text
.. code-block::

Y. Nakahara, N. Ichijo, K. Shimada, Y. Iikubo,
S. Saito, K. Kazama, T. Matsushima, BayesML Developers, ``BayesML 0.2.1,''
S. Saito, K. Kazama, T. Matsushima, BayesML Developers, ``BayesML 0.2.4,''
[Online] https://github.com/yuta-nakahara/BayesML


Expand All @@ -159,10 +159,10 @@ BibTeX
.. code-block:: bibtex

@misc{bayesml,
author = {Nakahara Yuta and Ichijo Naoki and Shimada Koshi and
Iikubo Yuji and Saito Shota and Kazama Koki and
Matsushima Toshiyasu and {BayesML Developers}},
title = {BayesML 0.2.1},
author = {Nakahara, Yuta and Ichijo, Naoki and Shimada, Koshi and
Iikubo, Yuji and Saito, Shota and Kazama, Koki and
Matsushima, Toshiyasu and {BayesML Developers}},
title = {BayesML 0.2.4},
howpublished = {\url{https://github.com/yuta-nakahara/BayesML}},
year = {2022}
}
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59 changes: 38 additions & 21 deletions docs/bayesml.bernoulli.html
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Expand Up @@ -596,61 +596,64 @@ <h1>bayesml.bernoulli package<a class="headerlink" href="#bayesml-bernoulli-pack
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#bayesml.bernoulli.LearnModel.calc_pred_dist" title="bayesml.bernoulli.LearnModel.calc_pred_dist"><code class="xref py py-obj docutils literal notranslate"><span class="pre">calc_pred_dist</span></code></a>()</p></td>
<tr class="row-odd"><td><p><a class="reference internal" href="#bayesml.bernoulli.LearnModel.calc_log_marginal_likelihood" title="bayesml.bernoulli.LearnModel.calc_log_marginal_likelihood"><code class="xref py py-obj docutils literal notranslate"><span class="pre">calc_log_marginal_likelihood</span></code></a>()</p></td>
<td><p>Calculate log marginal likelihood</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#bayesml.bernoulli.LearnModel.calc_pred_dist" title="bayesml.bernoulli.LearnModel.calc_pred_dist"><code class="xref py py-obj docutils literal notranslate"><span class="pre">calc_pred_dist</span></code></a>()</p></td>
<td><p>Calculate the parameters of the predictive distribution.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#bayesml.bernoulli.LearnModel.estimate_interval" title="bayesml.bernoulli.LearnModel.estimate_interval"><code class="xref py py-obj docutils literal notranslate"><span class="pre">estimate_interval</span></code></a>([credibility])</p></td>
<tr class="row-odd"><td><p><a class="reference internal" href="#bayesml.bernoulli.LearnModel.estimate_interval" title="bayesml.bernoulli.LearnModel.estimate_interval"><code class="xref py py-obj docutils literal notranslate"><span class="pre">estimate_interval</span></code></a>([credibility])</p></td>
<td><p>Credible interval of the parameter.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#bayesml.bernoulli.LearnModel.estimate_params" title="bayesml.bernoulli.LearnModel.estimate_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">estimate_params</span></code></a>([loss, dict_out])</p></td>
<tr class="row-even"><td><p><a class="reference internal" href="#bayesml.bernoulli.LearnModel.estimate_params" title="bayesml.bernoulli.LearnModel.estimate_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">estimate_params</span></code></a>([loss, dict_out])</p></td>
<td><p>Estimate the parameter of the stochastic data generative model under the given criterion.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#bayesml.bernoulli.LearnModel.get_constants" title="bayesml.bernoulli.LearnModel.get_constants"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_constants</span></code></a>()</p></td>
<tr class="row-odd"><td><p><a class="reference internal" href="#bayesml.bernoulli.LearnModel.get_constants" title="bayesml.bernoulli.LearnModel.get_constants"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_constants</span></code></a>()</p></td>
<td><p>Get constants of LearnModel.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#bayesml.bernoulli.LearnModel.get_h0_params" title="bayesml.bernoulli.LearnModel.get_h0_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_h0_params</span></code></a>()</p></td>
<tr class="row-even"><td><p><a class="reference internal" href="#bayesml.bernoulli.LearnModel.get_h0_params" title="bayesml.bernoulli.LearnModel.get_h0_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_h0_params</span></code></a>()</p></td>
<td><p>Get the initial values of the hyperparameters of the posterior distribution.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#bayesml.bernoulli.LearnModel.get_hn_params" title="bayesml.bernoulli.LearnModel.get_hn_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_hn_params</span></code></a>()</p></td>
<tr class="row-odd"><td><p><a class="reference internal" href="#bayesml.bernoulli.LearnModel.get_hn_params" title="bayesml.bernoulli.LearnModel.get_hn_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_hn_params</span></code></a>()</p></td>
<td><p>Get the hyperparameters of the posterior distribution.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#bayesml.bernoulli.LearnModel.get_p_params" title="bayesml.bernoulli.LearnModel.get_p_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_p_params</span></code></a>()</p></td>
<tr class="row-even"><td><p><a class="reference internal" href="#bayesml.bernoulli.LearnModel.get_p_params" title="bayesml.bernoulli.LearnModel.get_p_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_p_params</span></code></a>()</p></td>
<td><p>Get the parameters of the predictive distribution.</p></td>
</tr>
<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_h0_params</span></code>(filename)</p></td>
<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_h0_params</span></code>(filename)</p></td>
<td><p>Load the hyperparameters to h0_params.</p></td>
</tr>
<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_hn_params</span></code>(filename)</p></td>
<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_hn_params</span></code>(filename)</p></td>
<td><p>Load the hyperparameters to hn_params.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#bayesml.bernoulli.LearnModel.make_prediction" title="bayesml.bernoulli.LearnModel.make_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_prediction</span></code></a>([loss])</p></td>
<tr class="row-odd"><td><p><a class="reference internal" href="#bayesml.bernoulli.LearnModel.make_prediction" title="bayesml.bernoulli.LearnModel.make_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_prediction</span></code></a>([loss])</p></td>
<td><p>Predict a new data point under the given criterion.</p></td>
</tr>
<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">overwrite_h0_params</span></code>()</p></td>
<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">overwrite_h0_params</span></code>()</p></td>
<td><p>Overwrite the initial values of the hyperparameters of the posterior distribution by the learned values.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#bayesml.bernoulli.LearnModel.pred_and_update" title="bayesml.bernoulli.LearnModel.pred_and_update"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pred_and_update</span></code></a>(x[, loss])</p></td>
<tr class="row-odd"><td><p><a class="reference internal" href="#bayesml.bernoulli.LearnModel.pred_and_update" title="bayesml.bernoulli.LearnModel.pred_and_update"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pred_and_update</span></code></a>(x[, loss])</p></td>
<td><p>Predict a new data point and update the posterior sequentially.</p></td>
</tr>
<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_hn_params</span></code>()</p></td>
<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_hn_params</span></code>()</p></td>
<td><p>Reset the hyperparameters of the posterior distribution to their initial values.</p></td>
</tr>
<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_h0_params</span></code>(filename)</p></td>
<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_h0_params</span></code>(filename)</p></td>
<td><p>Save the hyperparameters using python <code class="docutils literal notranslate"><span class="pre">pickle</span></code> module.</p></td>
</tr>
<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_hn_params</span></code>(filename)</p></td>
<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_hn_params</span></code>(filename)</p></td>
<td><p>Save the hyperparameters using python <code class="docutils literal notranslate"><span class="pre">pickle</span></code> module.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#bayesml.bernoulli.LearnModel.set_h0_params" title="bayesml.bernoulli.LearnModel.set_h0_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_h0_params</span></code></a>([h0_alpha, h0_beta])</p></td>
<tr class="row-odd"><td><p><a class="reference internal" href="#bayesml.bernoulli.LearnModel.set_h0_params" title="bayesml.bernoulli.LearnModel.set_h0_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_h0_params</span></code></a>([h0_alpha, h0_beta])</p></td>
<td><p>Set initial values of the hyperparameter of the posterior distribution.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#bayesml.bernoulli.LearnModel.set_hn_params" title="bayesml.bernoulli.LearnModel.set_hn_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_hn_params</span></code></a>([hn_alpha, hn_beta])</p></td>
<tr class="row-even"><td><p><a class="reference internal" href="#bayesml.bernoulli.LearnModel.set_hn_params" title="bayesml.bernoulli.LearnModel.set_hn_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_hn_params</span></code></a>([hn_alpha, hn_beta])</p></td>
<td><p>Set updated values of the hyperparameter of the posterior distribution.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#bayesml.bernoulli.LearnModel.update_posterior" title="bayesml.bernoulli.LearnModel.update_posterior"><code class="xref py py-obj docutils literal notranslate"><span class="pre">update_posterior</span></code></a>(x)</p></td>
<tr class="row-odd"><td><p><a class="reference internal" href="#bayesml.bernoulli.LearnModel.update_posterior" title="bayesml.bernoulli.LearnModel.update_posterior"><code class="xref py py-obj docutils literal notranslate"><span class="pre">update_posterior</span></code></a>(x)</p></td>
<td><p>Update the hyperparameters of the posterior distribution using traning data.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#bayesml.bernoulli.LearnModel.visualize_posterior" title="bayesml.bernoulli.LearnModel.visualize_posterior"><code class="xref py py-obj docutils literal notranslate"><span class="pre">visualize_posterior</span></code></a>()</p></td>
<tr class="row-even"><td><p><a class="reference internal" href="#bayesml.bernoulli.LearnModel.visualize_posterior" title="bayesml.bernoulli.LearnModel.visualize_posterior"><code class="xref py py-obj docutils literal notranslate"><span class="pre">visualize_posterior</span></code></a>()</p></td>
<td><p>Visualize the posterior distribution for the parameter.</p></td>
</tr>
</tbody>
Expand Down Expand Up @@ -776,8 +779,8 @@ <h1>bayesml.bernoulli package<a class="headerlink" href="#bayesml-bernoulli-pack
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.rv_continuous.html#scipy.stats.rv_continuous" title="(in SciPy v1.10.0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.stats.rv_continuous</span></code></a></dt><dd></dd>
<dt><a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.rv_discrete.html#scipy.stats.rv_discrete" title="(in SciPy v1.10.0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.stats.rv_discrete</span></code></a></dt><dd></dd>
<dt><a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.rv_continuous.html#scipy.stats.rv_continuous" title="(in SciPy v1.10.1)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.stats.rv_continuous</span></code></a></dt><dd></dd>
<dt><a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.rv_discrete.html#scipy.stats.rv_discrete" title="(in SciPy v1.10.1)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.stats.rv_discrete</span></code></a></dt><dd></dd>
</dl>
</div>
</dd></dl>
Expand Down Expand Up @@ -890,6 +893,20 @@ <h1>bayesml.bernoulli package<a class="headerlink" href="#bayesml-bernoulli-pack
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="bayesml.bernoulli.LearnModel.calc_log_marginal_likelihood">
<span class="sig-name descname"><span class="pre">calc_log_marginal_likelihood</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#bayesml.bernoulli.LearnModel.calc_log_marginal_likelihood" title="Permalink to this definition">¶</a></dt>
<dd><p>Calculate log marginal likelihood</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>log_marginal_likelihood</strong><span class="classifier">float</span></dt><dd><p>The log marginal likelihood.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

</dd></dl>

</div>
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