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<link rel="up" href="../dist.html" title="Chapter 5. Statistical Distributions and Functions">
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<link rel="prev" href="dist_ref/dist_algorithms.html" title="Distribution Algorithms">
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<table cellpadding="2" width="100%"><tr>
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<td align="center"><a href="../../../../../index.html">Home</a></td>
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</div>
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<div class="section">
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<div class="titlepage"><div><div><h2 class="title" style="clear: both">
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<a name="math_toolkit.future"></a><a class="link" href="future.html" title="Extras/Future Directions">Extras/Future Directions</a>
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</h2></div></div></div>
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<h5>
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<a name="math_toolkit.future.h0"></a>
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<span class="phrase"><a name="math_toolkit.future.adding_additional_location_and_s"></a></span><a class="link" href="future.html#math_toolkit.future.adding_additional_location_and_s">Adding
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Additional Location and Scale Parameters</a>
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</h5>
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<p>
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In some modelling applications we require a distribution with a specific location
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and scale: often this equates to a specific mean and standard deviation, although
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for many distributions the relationship between these properties and the location
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and scale parameters are non-trivial. See <a href="http://www.itl.nist.gov/div898/handbook/eda/section3/eda364.htm" target="_top">http://www.itl.nist.gov/div898/handbook/eda/section3/eda364.htm</a>
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for more information.
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</p>
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<p>
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The obvious way to handle this is via an adapter template:
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</p>
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<pre class="programlisting"><span class="keyword">template</span> <span class="special"><</span><span class="keyword">class</span> <span class="identifier">Dist</span><span class="special">></span>
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<span class="keyword">class</span> <span class="identifier">scaled_distribution</span>
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<span class="special">{</span>
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<span class="identifier">scaled_distribution</span><span class="special">(</span>
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<span class="keyword">const</span> <span class="identifier">Dist</span> <span class="identifier">dist</span><span class="special">,</span>
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<span class="keyword">typename</span> <span class="identifier">Dist</span><span class="special">::</span><span class="identifier">value_type</span> <span class="identifier">location</span><span class="special">,</span>
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<span class="keyword">typename</span> <span class="identifier">Dist</span><span class="special">::</span><span class="identifier">value_type</span> <span class="identifier">scale</span> <span class="special">=</span> <span class="number">0</span><span class="special">);</span>
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<span class="special">};</span>
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</pre>
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<p>
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Which would then have its own set of overloads for the non-member accessor
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functions.
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</p>
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<h5>
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<a name="math_toolkit.future.h1"></a>
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<span class="phrase"><a name="math_toolkit.future.an_any_distribution_class"></a></span><a class="link" href="future.html#math_toolkit.future.an_any_distribution_class">An
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"any_distribution" class</a>
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</h5>
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<p>
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It is easy to add a distribution object that virtualises the actual type of
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the distribution, and can therefore hold "any" object that conforms
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to the conceptual requirements of a distribution:
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</p>
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<pre class="programlisting"><span class="keyword">template</span> <span class="special"><</span><span class="keyword">class</span> <span class="identifier">RealType</span><span class="special">></span>
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<span class="keyword">class</span> <span class="identifier">any_distribution</span>
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<span class="special">{</span>
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<span class="keyword">public</span><span class="special">:</span>
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<span class="keyword">template</span> <span class="special"><</span><span class="keyword">class</span> <span class="identifier">Distribution</span><span class="special">></span>
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<span class="identifier">any_distribution</span><span class="special">(</span><span class="keyword">const</span> <span class="identifier">Distribution</span><span class="special">&</span> <span class="identifier">d</span><span class="special">);</span>
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<span class="special">};</span>
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<span class="comment">// Get the cdf of the underlying distribution:</span>
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<span class="keyword">template</span> <span class="special"><</span><span class="keyword">class</span> <span class="identifier">RealType</span><span class="special">></span>
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<span class="identifier">RealType</span> <span class="identifier">cdf</span><span class="special">(</span><span class="keyword">const</span> <span class="identifier">any_distribution</span><span class="special"><</span><span class="identifier">RealType</span><span class="special">>&</span> <span class="identifier">d</span><span class="special">,</span> <span class="identifier">RealType</span> <span class="identifier">x</span><span class="special">);</span>
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<span class="comment">// etc....</span>
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</pre>
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<p>
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Such a class would facilitate the writing of non-template code that can function
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with any distribution type.
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</p>
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<p>
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The <a href="http://sourceforge.net/projects/distexplorer/" target="_top">Statistical
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Distribution Explorer</a> utility for Windows is a usage example.
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</p>
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<p>
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It's not clear yet whether there is a compelling use case though. Possibly
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tests for goodness of fit might provide such a use case: this needs more investigation.
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</p>
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<h5>
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<a name="math_toolkit.future.h2"></a>
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<span class="phrase"><a name="math_toolkit.future.higher_level_hypothesis_tests"></a></span><a class="link" href="future.html#math_toolkit.future.higher_level_hypothesis_tests">Higher
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Level Hypothesis Tests</a>
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</h5>
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<p>
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Higher-level tests roughly corresponding to the <a href="http://documents.wolfram.com/mathematica/Add-onsLinks/StandardPackages/Statistics/HypothesisTests.html" target="_top">Mathematica
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Hypothesis Tests</a> package could be added reasonably easily, for example:
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</p>
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<pre class="programlisting"><span class="keyword">template</span> <span class="special"><</span><span class="keyword">class</span> <span class="identifier">InputIterator</span><span class="special">></span>
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<span class="keyword">typename</span> <span class="identifier">std</span><span class="special">::</span><span class="identifier">iterator_traits</span><span class="special"><</span><span class="identifier">InputIterator</span><span class="special">>::</span><span class="identifier">value_type</span>
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<span class="identifier">test_equal_mean</span><span class="special">(</span>
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<span class="identifier">InputIterator</span> <span class="identifier">a</span><span class="special">,</span>
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<span class="identifier">InputIterator</span> <span class="identifier">b</span><span class="special">,</span>
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<span class="keyword">typename</span> <span class="identifier">std</span><span class="special">::</span><span class="identifier">iterator_traits</span><span class="special"><</span><span class="identifier">InputIterator</span><span class="special">>::</span><span class="identifier">value_type</span> <span class="identifier">expected_mean</span><span class="special">);</span>
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</pre>
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<p>
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Returns the probability that the data in the sequence [a,b) has the mean <span class="emphasis"><em>expected_mean</em></span>.
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</p>
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<h5>
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<a name="math_toolkit.future.h3"></a>
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<span class="phrase"><a name="math_toolkit.future.integration_with_statistical_acc"></a></span><a class="link" href="future.html#math_toolkit.future.integration_with_statistical_acc">Integration
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With Statistical Accumulators</a>
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</h5>
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<p>
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<a href="http://boost-sandbox.sourceforge.net/libs/accumulators/doc/html/index.html" target="_top">Eric
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Niebler's accumulator framework</a> - also work in progress - provides
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the means to calculate various statistical properties from experimental data.
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There is an opportunity to integrate the statistical tests with this framework
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at some later date:
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</p>
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<pre class="programlisting"><span class="comment">// Define an accumulator, all required statistics to calculate the test</span>
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<span class="comment">// are calculated automatically:</span>
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<span class="identifier">accumulator_set</span><span class="special"><</span><span class="keyword">double</span><span class="special">,</span> <span class="identifier">features</span><span class="special"><</span><span class="identifier">tag</span><span class="special">::</span><span class="identifier">test_expected_mean</span><span class="special">></span> <span class="special">></span> <span class="identifier">acc</span><span class="special">(</span><span class="identifier">expected_mean</span><span class="special">=</span><span class="number">4</span><span class="special">);</span>
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<span class="comment">// Pass our data to the accumulator:</span>
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<span class="identifier">acc</span> <span class="special">=</span> <span class="identifier">std</span><span class="special">::</span><span class="identifier">for_each</span><span class="special">(</span><span class="identifier">mydata</span><span class="special">.</span><span class="identifier">begin</span><span class="special">(),</span> <span class="identifier">mydata</span><span class="special">.</span><span class="identifier">end</span><span class="special">(),</span> <span class="identifier">acc</span><span class="special">);</span>
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<span class="comment">// Extract the result:</span>
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<span class="keyword">double</span> <span class="identifier">p</span> <span class="special">=</span> <span class="identifier">probability</span><span class="special">(</span><span class="identifier">acc</span><span class="special">);</span>
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</pre>
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</div>
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<table xmlns:rev="http://www.cs.rpi.edu/~gregod/boost/tools/doc/revision" width="100%"><tr>
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<td align="left"></td>
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<td align="right"><div class="copyright-footer">Copyright © 2006-2010, 2012-2014 Nikhar Agrawal,
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Anton Bikineev, Paul A. Bristow, Marco Guazzone, Christopher Kormanyos, Hubert
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Holin, Bruno Lalande, John Maddock, Jeremy Murphy, Johan Råde, Gautam Sewani,
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Benjamin Sobotta, Thijs van den Berg, Daryle Walker and Xiaogang Zhang<p>
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Distributed under the Boost Software License, Version 1.0. (See accompanying
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file LICENSE_1_0.txt or copy at <a href="http://www.boost.org/LICENSE_1_0.txt" target="_top">http://www.boost.org/LICENSE_1_0.txt</a>)
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</p>
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</div></td>
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<hr>
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