Sampler1DNormal

<p>Normal sampling (potentially truncated) for <code>p_dbl()</code>.</p>

Define parameter spaces, constraints and
dependencies for arbitrary algorithms, to program on such spaces. Also
includes statistical designs and random samplers. Objects are
implemented as 'R6' classes.

Martin Binder

paradox

Define and Work with Parameter Spaces for Complex Algorithms

Michel Lang

Bernd Bischl

Jakob Richter

Xudong Sun

Marc Becker

Sampler1DNormal function

<p><code>paradox::Sampler</code> -&gt; <code>paradox::Sampler1D</code> -&gt; <code>paradox::Sampler1DRfun</code> -&gt; <code>Sampler1DNormal</code></p>

Super classes

<p><div class="r6-active-bindings"></div></p><dl>
<dt><code>mean</code></dt>
<dd><p>(<code>numeric(1)</code>)<br>
Mean parameter of the normal distribution.</p></dd><p></p><p>
<dt><code>sd</code></dt>
<dd><p>(<code>numeric(1)</code>)<br>
SD parameter of the normal distribution.</p></dd></p><p>
</p></dl><p></p>

Active bindings


<div class="section" id="public-methods">
<h3>Public methods</h3><p></p><p><ul>
<li><p><a href="/link/Sampler1DNormal%24new()?package=paradox&version=1.0.1" data-mini-rdoc="paradox::Sampler1DNormal$new()"><code>Sampler1DNormal$new()</code></a></p></li>
<li><p><a href="/link/Sampler1DNormal%24clone()?package=paradox&version=1.0.1" data-mini-rdoc="paradox::Sampler1DNormal$clone()"><code>Sampler1DNormal$clone()</code></a></p></li>
</ul></p><p></p></div><p><details open><summary>Inherited methods</summary>
<ul>
<li><span class="pkg-link" data-pkg="paradox" data-topic="Sampler" data-id="format"><a href="/link/paradox%3A%3ASampler%24format()?package=paradox&version=1.0.1" data-mini-rdoc="paradox::paradox::Sampler$format()"><code>paradox::Sampler$format()</code></a></span></li>
<li><span class="pkg-link" data-pkg="paradox" data-topic="Sampler" data-id="print"><a href="/link/paradox%3A%3ASampler%24print()?package=paradox&version=1.0.1" data-mini-rdoc="paradox::paradox::Sampler$print()"><code>paradox::Sampler$print()</code></a></span></li>
<li><span class="pkg-link" data-pkg="paradox" data-topic="Sampler" data-id="sample"><a href="/link/paradox%3A%3ASampler%24sample()?package=paradox&version=1.0.1" data-mini-rdoc="paradox::paradox::Sampler$sample()"><code>paradox::Sampler$sample()</code></a></span></li>
</ul>
</details></p><p><hr>
<a id="method-Sampler1DNormal-new"></a></p><div class="section" id="method-new-">
<h3>Method <code>new()</code></h3>
<p>Creates a new instance of this <a href="https://r6.r-lib.org/reference/R6Class.html">R6</a> class.</p><div class="section" id="usage">
<h4>Usage</h4>
<p><div class="r"></div></p><pre><code>Sampler1DNormal$new(param, mean = NULL, sd = NULL)</code></pre><p></p></div><p></p>
</div><p></p><p><div class="section" id="arguments">
<h4>Arguments</h4>
<p><div class="arguments"></div></p><dl>
<dt><code>param</code></dt>
<dd><p>(<code>ParamSet</code>)<br>
Domain / support of the distribution we want to sample from.
Must be one-dimensional.</p></dd></dl></div></p><p>
<dt><code>mean</code></dt>
<dd><p>(<code>numeric(1)</code>)<br>
Mean parameter of the normal distribution.
Default is <code>mean(c(param$lower, param$upper)</code>.</p></dd></p><p>
<dt><code>sd</code></dt>
<dd><p>(<code>numeric(1)</code>)<br>
SD parameter of the normal distribution.
Default is <code>(param$upper - param$lower)/4</code>.</p></dd></p><p>
</p><p></p>
<p></p><p></p><p><hr>
<a id="method-Sampler1DNormal-clone"></a></p><div class="section" id="method-clone-">
<h3>Method <code>clone()</code></h3>
<p>The objects of this class are cloneable with this method.</p><div class="section" id="usage">
<h4>Usage</h4>
<p><div class="r"></div></p><pre><code>Sampler1DNormal$clone(deep = FALSE)</code></pre><p></p></div><p></p>
</div><p></p><p><div class="section" id="arguments">
<h4>Arguments</h4>
<p><div class="arguments"></div></p><dl>
<dt><code>deep</code></dt>
<dd><p>Whether to make a deep clone.</p></dd></dl></div></p><p>
</p><p></p>
<p></p><p>
</p>

Methods

Sampler1DNormal Class — Sampler1DNormal

<p><div class="r6-active-bindings"></p><dl>
<dt><code>mean</code></dt>
<dd><p>(<code>numeric(1)</code>)<br />
Mean parameter of the normal distribution.</p></dd>


<dt><code>sd</code></dt>
<dd><p>(<code>numeric(1)</code>)<br />
SD parameter of the normal distribution.</p></dd>


</dl><p></div></p>


<div class='section' id='public-methods'>
<h3>Public methods</h3>

<ul>
<li><p><a href='#method-Sampler1DNormal-new'><code>Sampler1DNormal$new()</code></a></p></li>
<li><p><a href='#method-Sampler1DNormal-clone'><code>Sampler1DNormal$clone()</code></a></p></li>
</ul>

</div><p><details open><summary>Inherited methods</summary>
<ul>
<li><span class="pkg-link" data-pkg="paradox" data-topic="Sampler" data-id="format"><a href='../../paradox/html/Sampler.html#method-Sampler-format'><code>paradox::Sampler$format()</code></a></span></li>
<li><span class="pkg-link" data-pkg="paradox" data-topic="Sampler" data-id="print"><a href='../../paradox/html/Sampler.html#method-Sampler-print'><code>paradox::Sampler$print()</code></a></span></li>
<li><span class="pkg-link" data-pkg="paradox" data-topic="Sampler" data-id="sample"><a href='../../paradox/html/Sampler.html#method-Sampler-sample'><code>paradox::Sampler$sample()</code></a></span></li>
</ul>
</details>

<hr>
<a id="method-Sampler1DNormal-new"></a></p><div class='section' id='method-new-'>
<h3>Method <code>new()</code></h3>
<p>Creates a new instance of this <a href='https://r6.r-lib.org/reference/R6Class.html'>R6</a> class.</p><div class='section' id='usage'>
<h4>Usage</h4>
<p><div class="r"></p><pre><code>Sampler1DNormal$new(param, mean = NULL, sd = NULL)</code></pre><p></div></p>
</div>

<div class='section' id='arguments'>
<h4>Arguments</h4>
<p><div class="arguments"></p><dl>
<dt><code>param</code></dt>
<dd><p>(<code>ParamSet</code>)<br />
Domain / support of the distribution we want to sample from.
Must be one-dimensional.</p></dd>


<dt><code>mean</code></dt>
<dd><p>(<code>numeric(1)</code>)<br />
Mean parameter of the normal distribution.
Default is <code>mean(c(param$lower, param$upper)</code>.</p></dd>


<dt><code>sd</code></dt>
<dd><p>(<code>numeric(1)</code>)<br />
SD parameter of the normal distribution.
Default is <code>(param$upper - param$lower)/4</code>.</p></dd>


</dl><p></div></p>
</div>

</div><p><hr>
<a id="method-Sampler1DNormal-clone"></a></p><div class='section' id='method-clone-'>
<h3>Method <code>clone()</code></h3>
<p>The objects of this class are cloneable with this method.</p><div class='section' id='usage'>
<h4>Usage</h4>
<p><div class="r"></p><pre><code>Sampler1DNormal$clone(deep = FALSE)</code></pre><p></div></p>
</div>

<div class='section' id='arguments'>
<h4>Arguments</h4>
<p><div class="arguments"></p><dl>
<dt><code>deep</code></dt>
<dd><p>Whether to make a deep clone.</p></dd>


</dl><p></div></p>
</div>

</div>


Sampler1DNormal: Sampler1DNormal Class

Description

Arguments

Super classes

Active bindings

Methods

Public methods

Method `new()`

Usage

Arguments

Method `clone()`

Usage

Arguments

See Also

Description

Arguments

Super classes

Active bindings

Methods

Public methods

Method new()

Usage

Arguments

Method clone()

Usage

Arguments

See Also

Method `new()`

Method `clone()`