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<a href="Bio-module.html">Package Bio</a> ::
<a href="Bio.Statistics-module.html">Package Statistics</a> ::
Module lowess
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<h1 class="epydoc">Source Code for <a href="Bio.Statistics.lowess-module.html">Module Bio.Statistics.lowess</a></h1>
<pre class="py-src">
<a name="L1"></a><tt class="py-lineno"> 1</tt> <tt class="py-line"><tt class="py-docstring">"""</tt> </tt>
<a name="L2"></a><tt class="py-lineno"> 2</tt> <tt class="py-line"><tt class="py-docstring">This module implements the Lowess function for nonparametric regression.</tt> </tt>
<a name="L3"></a><tt class="py-lineno"> 3</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L4"></a><tt class="py-lineno"> 4</tt> <tt class="py-line"><tt class="py-docstring">Functions:</tt> </tt>
<a name="L5"></a><tt class="py-lineno"> 5</tt> <tt class="py-line"><tt class="py-docstring">lowess Fit a smooth nonparametric regression curve to a scatterplot.</tt> </tt>
<a name="L6"></a><tt class="py-lineno"> 6</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L7"></a><tt class="py-lineno"> 7</tt> <tt class="py-line"><tt class="py-docstring">For more information, see</tt> </tt>
<a name="L8"></a><tt class="py-lineno"> 8</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L9"></a><tt class="py-lineno"> 9</tt> <tt class="py-line"><tt class="py-docstring">William S. Cleveland: "Robust locally weighted regression and smoothing</tt> </tt>
<a name="L10"></a><tt class="py-lineno">10</tt> <tt class="py-line"><tt class="py-docstring">scatterplots", Journal of the American Statistical Association, December 1979,</tt> </tt>
<a name="L11"></a><tt class="py-lineno">11</tt> <tt class="py-line"><tt class="py-docstring">volume 74, number 368, pp. 829-836.</tt> </tt>
<a name="L12"></a><tt class="py-lineno">12</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L13"></a><tt class="py-lineno">13</tt> <tt class="py-line"><tt class="py-docstring">William S. Cleveland and Susan J. Devlin: "Locally weighted regression: An</tt> </tt>
<a name="L14"></a><tt class="py-lineno">14</tt> <tt class="py-line"><tt class="py-docstring">approach to regression analysis by local fitting", Journal of the American</tt> </tt>
<a name="L15"></a><tt class="py-lineno">15</tt> <tt class="py-line"><tt class="py-docstring">Statistical Association, September 1988, volume 83, number 403, pp. 596-610.</tt> </tt>
<a name="L16"></a><tt class="py-lineno">16</tt> <tt class="py-line"><tt class="py-docstring">"""</tt> </tt>
<a name="L17"></a><tt class="py-lineno">17</tt> <tt class="py-line"> </tt>
<a name="L18"></a><tt class="py-lineno">18</tt> <tt class="py-line"><tt class="py-keyword">try</tt><tt class="py-op">:</tt> </tt>
<a name="L19"></a><tt class="py-lineno">19</tt> <tt class="py-line"> <tt class="py-keyword">from</tt> <tt class="py-name">Numeric</tt> <tt class="py-keyword">import</tt> <tt class="py-op">*</tt> </tt>
<a name="L20"></a><tt class="py-lineno">20</tt> <tt class="py-line"> <tt class="py-keyword">from</tt> <tt class="py-name">LinearAlgebra</tt> <tt class="py-keyword">import</tt> <tt class="py-name">solve_linear_equations</tt> </tt>
<a name="L21"></a><tt class="py-lineno">21</tt> <tt class="py-line"><tt class="py-keyword">except</tt> <tt class="py-name">ImportError</tt><tt class="py-op">,</tt> <tt id="link-0" class="py-name" targets="Variable Bio.MarkovModel.x=Bio.MarkovModel-module.html#x,Variable Bio.Statistics.lowess.x=Bio.Statistics.lowess-module.html#x"><a title="Bio.MarkovModel.x
Bio.Statistics.lowess.x" class="py-name" href="#" onclick="return doclink('link-0', 'x', 'link-0');">x</a></tt><tt class="py-op">:</tt> </tt>
<a name="L22"></a><tt class="py-lineno">22</tt> <tt class="py-line"> <tt class="py-keyword">raise</tt> <tt class="py-name">ImportError</tt><tt class="py-op">,</tt> <tt class="py-string">"This module requires Numeric (precursor to NumPy) with the LinearAlgebra and MLab libraries"</tt> </tt>
<a name="L23"></a><tt class="py-lineno">23</tt> <tt class="py-line"> </tt>
<a name="L24"></a><tt class="py-lineno">24</tt> <tt class="py-line"><tt class="py-keyword">try</tt><tt class="py-op">:</tt> </tt>
<a name="L25"></a><tt class="py-lineno">25</tt> <tt class="py-line"> <tt class="py-keyword">from</tt> <tt id="link-1" class="py-name" targets="Package Bio=Bio-module.html"><a title="Bio" class="py-name" href="#" onclick="return doclink('link-1', 'Bio', 'link-1');">Bio</a></tt><tt class="py-op">.</tt><tt id="link-2" class="py-name" targets="Package Bio.Cluster=Bio.Cluster-module.html"><a title="Bio.Cluster" class="py-name" href="#" onclick="return doclink('link-2', 'Cluster', 'link-2');">Cluster</a></tt> <tt class="py-keyword">import</tt> <tt class="py-name">median</tt> </tt>
<a name="L26"></a><tt class="py-lineno">26</tt> <tt class="py-line"> <tt class="py-comment"># The function median in Bio.Cluster is faster than the function median</tt> </tt>
<a name="L27"></a><tt class="py-lineno">27</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-comment"># in Numeric's MLab, as it does not require a full sort.</tt> </tt>
<a name="L28"></a><tt class="py-lineno">28</tt> <tt class="py-line"><tt class="py-comment"></tt><tt class="py-keyword">except</tt> <tt class="py-name">ImportError</tt><tt class="py-op">,</tt> <tt id="link-3" class="py-name"><a title="Bio.MarkovModel.x
Bio.Statistics.lowess.x" class="py-name" href="#" onclick="return doclink('link-3', 'x', 'link-0');">x</a></tt><tt class="py-op">:</tt> </tt>
<a name="L29"></a><tt class="py-lineno">29</tt> <tt class="py-line"> <tt class="py-comment"># Use the median function in Numeric's MLab if Bio.Cluster is not available</tt> </tt>
<a name="L30"></a><tt class="py-lineno">30</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-keyword">try</tt><tt class="py-op">:</tt> </tt>
<a name="L31"></a><tt class="py-lineno">31</tt> <tt class="py-line"> <tt class="py-keyword">from</tt> <tt class="py-name">MLab</tt> <tt class="py-keyword">import</tt> <tt class="py-name">median</tt> </tt>
<a name="L32"></a><tt class="py-lineno">32</tt> <tt class="py-line"> <tt class="py-keyword">except</tt> <tt class="py-name">ImportError</tt><tt class="py-op">,</tt> <tt id="link-4" class="py-name"><a title="Bio.MarkovModel.x
Bio.Statistics.lowess.x" class="py-name" href="#" onclick="return doclink('link-4', 'x', 'link-0');">x</a></tt><tt class="py-op">:</tt> </tt>
<a name="L33"></a><tt class="py-lineno">33</tt> <tt class="py-line"> <tt class="py-keyword">raise</tt> <tt class="py-name">ImportError</tt><tt class="py-op">,</tt> <tt class="py-string">"This module requires Numeric (precursor to NumPy) with the LinearAlgebra and MLab libraries"</tt> </tt>
<a name="L34"></a><tt class="py-lineno">34</tt> <tt class="py-line"> </tt>
<a name="lowess"></a><div id="lowess-def"><a name="L35"></a><tt class="py-lineno">35</tt> <a class="py-toggle" href="#" id="lowess-toggle" onclick="return toggle('lowess');">-</a><tt class="py-line"><tt class="py-keyword">def</tt> <a class="py-def-name" href="Bio.Statistics.lowess-module.html#lowess">lowess</a><tt class="py-op">(</tt><tt class="py-param">x</tt><tt class="py-op">,</tt> <tt class="py-param">y</tt><tt class="py-op">,</tt> <tt class="py-param">f</tt><tt class="py-op">=</tt><tt class="py-number">2.</tt><tt class="py-op">/</tt><tt class="py-number">3.</tt><tt class="py-op">,</tt> <tt class="py-param">iter</tt><tt class="py-op">=</tt><tt class="py-number">3</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="lowess-collapsed" style="display:none;" pad="++" indent="++++"></div><div id="lowess-expanded"><a name="L36"></a><tt class="py-lineno">36</tt> <tt class="py-line"> <tt class="py-docstring">"""lowess(x, y, f=2./3., iter=3) -> yest</tt> </tt>
<a name="L37"></a><tt class="py-lineno">37</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L38"></a><tt class="py-lineno">38</tt> <tt class="py-line"><tt class="py-docstring">Lowess smoother: Robust locally weighted regression.</tt> </tt>
<a name="L39"></a><tt class="py-lineno">39</tt> <tt class="py-line"><tt class="py-docstring">The lowess function fits a nonparametric regression curve to a scatterplot.</tt> </tt>
<a name="L40"></a><tt class="py-lineno">40</tt> <tt class="py-line"><tt class="py-docstring">The arrays x and y contain an equal number of elements; each pair</tt> </tt>
<a name="L41"></a><tt class="py-lineno">41</tt> <tt class="py-line"><tt class="py-docstring">(x[i], y[i]) defines a data point in the scatterplot. The function returns</tt> </tt>
<a name="L42"></a><tt class="py-lineno">42</tt> <tt class="py-line"><tt class="py-docstring">the estimated (smooth) values of y.</tt> </tt>
<a name="L43"></a><tt class="py-lineno">43</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L44"></a><tt class="py-lineno">44</tt> <tt class="py-line"><tt class="py-docstring">The smoothing span is given by f. A larger value for f will result in a</tt> </tt>
<a name="L45"></a><tt class="py-lineno">45</tt> <tt class="py-line"><tt class="py-docstring">smoother curve. The number of robustifying iterations is given by iter. The</tt> </tt>
<a name="L46"></a><tt class="py-lineno">46</tt> <tt class="py-line"><tt class="py-docstring">function will run faster with a smaller number of iterations."""</tt> </tt>
<a name="L47"></a><tt class="py-lineno">47</tt> <tt class="py-line"> <tt class="py-name">n</tt> <tt class="py-op">=</tt> <tt class="py-name">len</tt><tt class="py-op">(</tt><tt id="link-5" class="py-name"><a title="Bio.MarkovModel.x
Bio.Statistics.lowess.x" class="py-name" href="#" onclick="return doclink('link-5', 'x', 'link-0');">x</a></tt><tt class="py-op">)</tt> </tt>
<a name="L48"></a><tt class="py-lineno">48</tt> <tt class="py-line"> <tt class="py-name">r</tt> <tt class="py-op">=</tt> <tt class="py-name">int</tt><tt class="py-op">(</tt><tt id="link-6" class="py-name" targets="Variable Bio.Affy.CelFile.ceil=Bio.Affy.CelFile-module.html#ceil,Variable Bio.LogisticRegression.ceil=Bio.LogisticRegression-module.html#ceil,Variable Bio.MarkovModel.ceil=Bio.MarkovModel-module.html#ceil,Variable Bio.MaxEntropy.ceil=Bio.MaxEntropy-module.html#ceil,Variable Bio.NaiveBayes.ceil=Bio.NaiveBayes-module.html#ceil,Variable Bio.Statistics.lowess.ceil=Bio.Statistics.lowess-module.html#ceil,Variable Bio.distance.ceil=Bio.distance-module.html#ceil,Variable Bio.kNN.ceil=Bio.kNN-module.html#ceil"><a title="Bio.Affy.CelFile.ceil
Bio.LogisticRegression.ceil
Bio.MarkovModel.ceil
Bio.MaxEntropy.ceil
Bio.NaiveBayes.ceil
Bio.Statistics.lowess.ceil
Bio.distance.ceil
Bio.kNN.ceil" class="py-name" href="#" onclick="return doclink('link-6', 'ceil', 'link-6');">ceil</a></tt><tt class="py-op">(</tt><tt class="py-name">f</tt><tt class="py-op">*</tt><tt class="py-name">n</tt><tt class="py-op">)</tt><tt class="py-op">)</tt> </tt>
<a name="L49"></a><tt class="py-lineno">49</tt> <tt class="py-line"> <tt class="py-name">h</tt> <tt class="py-op">=</tt> <tt class="py-op">[</tt><tt id="link-7" class="py-name" targets="Method Bio.PDB.Residue.DisorderedResidue.sort()=Bio.PDB.Residue.DisorderedResidue-class.html#sort,Method Bio.PDB.Residue.Residue.sort()=Bio.PDB.Residue.Residue-class.html#sort,Method Bio.Sequencing.Ace.ACEFileRecord.sort()=Bio.Sequencing.Ace.ACEFileRecord-class.html#sort"><a title="Bio.PDB.Residue.DisorderedResidue.sort
Bio.PDB.Residue.Residue.sort
Bio.Sequencing.Ace.ACEFileRecord.sort" class="py-name" href="#" onclick="return doclink('link-7', 'sort', 'link-7');">sort</a></tt><tt class="py-op">(</tt><tt id="link-8" class="py-name" targets="Method Bio.FSSP.FSSPAlignDict.abs()=Bio.FSSP.FSSPAlignDict-class.html#abs"><a title="Bio.FSSP.FSSPAlignDict.abs" class="py-name" href="#" onclick="return doclink('link-8', 'abs', 'link-8');">abs</a></tt><tt class="py-op">(</tt><tt id="link-9" class="py-name"><a title="Bio.MarkovModel.x
Bio.Statistics.lowess.x" class="py-name" href="#" onclick="return doclink('link-9', 'x', 'link-0');">x</a></tt><tt class="py-op">-</tt><tt id="link-10" class="py-name"><a title="Bio.MarkovModel.x
Bio.Statistics.lowess.x" class="py-name" href="#" onclick="return doclink('link-10', 'x', 'link-0');">x</a></tt><tt class="py-op">[</tt><tt id="link-11" class="py-name" targets="Variable Bio.PDB.Polypeptide.i=Bio.PDB.Polypeptide-module.html#i"><a title="Bio.PDB.Polypeptide.i" class="py-name" href="#" onclick="return doclink('link-11', 'i', 'link-11');">i</a></tt><tt class="py-op">]</tt><tt class="py-op">)</tt><tt class="py-op">)</tt><tt class="py-op">[</tt><tt class="py-name">r</tt><tt class="py-op">]</tt> <tt class="py-keyword">for</tt> <tt id="link-12" class="py-name"><a title="Bio.PDB.Polypeptide.i" class="py-name" href="#" onclick="return doclink('link-12', 'i', 'link-11');">i</a></tt> <tt class="py-keyword">in</tt> <tt class="py-name">range</tt><tt class="py-op">(</tt><tt class="py-name">n</tt><tt class="py-op">)</tt><tt class="py-op">]</tt> </tt>
<a name="L50"></a><tt class="py-lineno">50</tt> <tt class="py-line"> <tt class="py-name">w</tt> <tt class="py-op">=</tt> <tt class="py-name">clip</tt><tt class="py-op">(</tt><tt id="link-13" class="py-name"><a title="Bio.FSSP.FSSPAlignDict.abs" class="py-name" href="#" onclick="return doclink('link-13', 'abs', 'link-8');">abs</a></tt><tt class="py-op">(</tt><tt class="py-op">(</tt><tt class="py-op">[</tt><tt id="link-14" class="py-name"><a title="Bio.MarkovModel.x
Bio.Statistics.lowess.x" class="py-name" href="#" onclick="return doclink('link-14', 'x', 'link-0');">x</a></tt><tt class="py-op">]</tt><tt class="py-op">-</tt><tt class="py-name">transpose</tt><tt class="py-op">(</tt><tt class="py-op">[</tt><tt id="link-15" class="py-name"><a title="Bio.MarkovModel.x
Bio.Statistics.lowess.x" class="py-name" href="#" onclick="return doclink('link-15', 'x', 'link-0');">x</a></tt><tt class="py-op">]</tt><tt class="py-op">)</tt><tt class="py-op">)</tt><tt class="py-op">/</tt><tt class="py-name">h</tt><tt class="py-op">)</tt><tt class="py-op">,</tt><tt class="py-number">0.0</tt><tt class="py-op">,</tt><tt class="py-number">1.0</tt><tt class="py-op">)</tt> </tt>
<a name="L51"></a><tt class="py-lineno">51</tt> <tt class="py-line"> <tt class="py-name">w</tt> <tt class="py-op">=</tt> <tt class="py-number">1</tt><tt class="py-op">-</tt><tt class="py-name">w</tt><tt class="py-op">*</tt><tt class="py-name">w</tt><tt class="py-op">*</tt><tt class="py-name">w</tt> </tt>
<a name="L52"></a><tt class="py-lineno">52</tt> <tt class="py-line"> <tt class="py-name">w</tt> <tt class="py-op">=</tt> <tt class="py-name">w</tt><tt class="py-op">*</tt><tt class="py-name">w</tt><tt class="py-op">*</tt><tt class="py-name">w</tt> </tt>
<a name="L53"></a><tt class="py-lineno">53</tt> <tt class="py-line"> <tt class="py-name">yest</tt> <tt class="py-op">=</tt> <tt class="py-name">zeros</tt><tt class="py-op">(</tt><tt class="py-name">n</tt><tt class="py-op">,</tt><tt class="py-string">'d'</tt><tt class="py-op">)</tt> </tt>
<a name="L54"></a><tt class="py-lineno">54</tt> <tt class="py-line"> <tt class="py-name">delta</tt> <tt class="py-op">=</tt> <tt class="py-name">ones</tt><tt class="py-op">(</tt><tt class="py-name">n</tt><tt class="py-op">,</tt><tt class="py-string">'d'</tt><tt class="py-op">)</tt> </tt>
<a name="L55"></a><tt class="py-lineno">55</tt> <tt class="py-line"> <tt class="py-keyword">for</tt> <tt class="py-name">iteration</tt> <tt class="py-keyword">in</tt> <tt class="py-name">range</tt><tt class="py-op">(</tt><tt class="py-name">iter</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
<a name="L56"></a><tt class="py-lineno">56</tt> <tt class="py-line"> <tt class="py-keyword">for</tt> <tt id="link-16" class="py-name"><a title="Bio.PDB.Polypeptide.i" class="py-name" href="#" onclick="return doclink('link-16', 'i', 'link-11');">i</a></tt> <tt class="py-keyword">in</tt> <tt class="py-name">range</tt><tt class="py-op">(</tt><tt class="py-name">n</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
<a name="L57"></a><tt class="py-lineno">57</tt> <tt class="py-line"> <tt class="py-name">weights</tt> <tt class="py-op">=</tt> <tt class="py-name">delta</tt> <tt class="py-op">*</tt> <tt class="py-name">w</tt><tt class="py-op">[</tt><tt class="py-op">:</tt><tt class="py-op">,</tt><tt id="link-17" class="py-name"><a title="Bio.PDB.Polypeptide.i" class="py-name" href="#" onclick="return doclink('link-17', 'i', 'link-11');">i</a></tt><tt class="py-op">]</tt> </tt>
<a name="L58"></a><tt class="py-lineno">58</tt> <tt class="py-line"> <tt class="py-name">b</tt> <tt class="py-op">=</tt> <tt class="py-name">array</tt><tt class="py-op">(</tt><tt class="py-op">[</tt><tt id="link-18" class="py-name" targets="Method Bio.Nexus.Nexus.StepMatrix.sum()=Bio.Nexus.Nexus.StepMatrix-class.html#sum,Function Bio.utils.sum()=Bio.utils-module.html#sum"><a title="Bio.Nexus.Nexus.StepMatrix.sum
Bio.utils.sum" class="py-name" href="#" onclick="return doclink('link-18', 'sum', 'link-18');">sum</a></tt><tt class="py-op">(</tt><tt class="py-name">weights</tt><tt class="py-op">*</tt><tt class="py-name">y</tt><tt class="py-op">)</tt><tt class="py-op">,</tt> <tt id="link-19" class="py-name"><a title="Bio.Nexus.Nexus.StepMatrix.sum
Bio.utils.sum" class="py-name" href="#" onclick="return doclink('link-19', 'sum', 'link-18');">sum</a></tt><tt class="py-op">(</tt><tt class="py-name">weights</tt><tt class="py-op">*</tt><tt class="py-name">y</tt><tt class="py-op">*</tt><tt id="link-20" class="py-name"><a title="Bio.MarkovModel.x
Bio.Statistics.lowess.x" class="py-name" href="#" onclick="return doclink('link-20', 'x', 'link-0');">x</a></tt><tt class="py-op">)</tt><tt class="py-op">]</tt><tt class="py-op">)</tt> </tt>
<a name="L59"></a><tt class="py-lineno">59</tt> <tt class="py-line"> <tt class="py-name">A</tt> <tt class="py-op">=</tt> <tt class="py-name">array</tt><tt class="py-op">(</tt><tt class="py-op">[</tt><tt class="py-op">[</tt><tt id="link-21" class="py-name"><a title="Bio.Nexus.Nexus.StepMatrix.sum
Bio.utils.sum" class="py-name" href="#" onclick="return doclink('link-21', 'sum', 'link-18');">sum</a></tt><tt class="py-op">(</tt><tt class="py-name">weights</tt><tt class="py-op">)</tt><tt class="py-op">,</tt> <tt id="link-22" class="py-name"><a title="Bio.Nexus.Nexus.StepMatrix.sum
Bio.utils.sum" class="py-name" href="#" onclick="return doclink('link-22', 'sum', 'link-18');">sum</a></tt><tt class="py-op">(</tt><tt class="py-name">weights</tt><tt class="py-op">*</tt><tt id="link-23" class="py-name"><a title="Bio.MarkovModel.x
Bio.Statistics.lowess.x" class="py-name" href="#" onclick="return doclink('link-23', 'x', 'link-0');">x</a></tt><tt class="py-op">)</tt><tt class="py-op">]</tt><tt class="py-op">,</tt> </tt>
<a name="L60"></a><tt class="py-lineno">60</tt> <tt class="py-line"> <tt class="py-op">[</tt><tt id="link-24" class="py-name"><a title="Bio.Nexus.Nexus.StepMatrix.sum
Bio.utils.sum" class="py-name" href="#" onclick="return doclink('link-24', 'sum', 'link-18');">sum</a></tt><tt class="py-op">(</tt><tt class="py-name">weights</tt><tt class="py-op">*</tt><tt id="link-25" class="py-name"><a title="Bio.MarkovModel.x
Bio.Statistics.lowess.x" class="py-name" href="#" onclick="return doclink('link-25', 'x', 'link-0');">x</a></tt><tt class="py-op">)</tt><tt class="py-op">,</tt> <tt id="link-26" class="py-name"><a title="Bio.Nexus.Nexus.StepMatrix.sum
Bio.utils.sum" class="py-name" href="#" onclick="return doclink('link-26', 'sum', 'link-18');">sum</a></tt><tt class="py-op">(</tt><tt class="py-name">weights</tt><tt class="py-op">*</tt><tt id="link-27" class="py-name"><a title="Bio.MarkovModel.x
Bio.Statistics.lowess.x" class="py-name" href="#" onclick="return doclink('link-27', 'x', 'link-0');">x</a></tt><tt class="py-op">*</tt><tt id="link-28" class="py-name"><a title="Bio.MarkovModel.x
Bio.Statistics.lowess.x" class="py-name" href="#" onclick="return doclink('link-28', 'x', 'link-0');">x</a></tt><tt class="py-op">)</tt><tt class="py-op">]</tt><tt class="py-op">]</tt><tt class="py-op">)</tt> </tt>
<a name="L61"></a><tt class="py-lineno">61</tt> <tt class="py-line"> <tt class="py-name">beta</tt> <tt class="py-op">=</tt> <tt class="py-name">solve_linear_equations</tt><tt class="py-op">(</tt><tt class="py-name">A</tt><tt class="py-op">,</tt><tt class="py-name">b</tt><tt class="py-op">)</tt> </tt>
<a name="L62"></a><tt class="py-lineno">62</tt> <tt class="py-line"> <tt class="py-name">yest</tt><tt class="py-op">[</tt><tt id="link-29" class="py-name"><a title="Bio.PDB.Polypeptide.i" class="py-name" href="#" onclick="return doclink('link-29', 'i', 'link-11');">i</a></tt><tt class="py-op">]</tt> <tt class="py-op">=</tt> <tt class="py-name">beta</tt><tt class="py-op">[</tt><tt class="py-number">0</tt><tt class="py-op">]</tt> <tt class="py-op">+</tt> <tt class="py-name">beta</tt><tt class="py-op">[</tt><tt class="py-number">1</tt><tt class="py-op">]</tt><tt class="py-op">*</tt><tt id="link-30" class="py-name"><a title="Bio.MarkovModel.x
Bio.Statistics.lowess.x" class="py-name" href="#" onclick="return doclink('link-30', 'x', 'link-0');">x</a></tt><tt class="py-op">[</tt><tt id="link-31" class="py-name"><a title="Bio.PDB.Polypeptide.i" class="py-name" href="#" onclick="return doclink('link-31', 'i', 'link-11');">i</a></tt><tt class="py-op">]</tt> </tt>
<a name="L63"></a><tt class="py-lineno">63</tt> <tt class="py-line"> <tt class="py-name">residuals</tt> <tt class="py-op">=</tt> <tt class="py-name">y</tt><tt class="py-op">-</tt><tt class="py-name">yest</tt> </tt>
<a name="L64"></a><tt class="py-lineno">64</tt> <tt class="py-line"> <tt id="link-32" class="py-name" targets="Variable Martel.test.test_swissprot38.s=Martel.test.test_swissprot38-module.html#s"><a title="Martel.test.test_swissprot38.s" class="py-name" href="#" onclick="return doclink('link-32', 's', 'link-32');">s</a></tt> <tt class="py-op">=</tt> <tt class="py-name">median</tt><tt class="py-op">(</tt><tt id="link-33" class="py-name"><a title="Bio.FSSP.FSSPAlignDict.abs" class="py-name" href="#" onclick="return doclink('link-33', 'abs', 'link-8');">abs</a></tt><tt class="py-op">(</tt><tt class="py-name">residuals</tt><tt class="py-op">)</tt><tt class="py-op">)</tt> </tt>
<a name="L65"></a><tt class="py-lineno">65</tt> <tt class="py-line"> <tt class="py-name">delta</tt> <tt class="py-op">=</tt> <tt class="py-name">clip</tt><tt class="py-op">(</tt><tt class="py-name">residuals</tt><tt class="py-op">/</tt><tt class="py-op">(</tt><tt class="py-number">6</tt><tt class="py-op">*</tt><tt id="link-34" class="py-name"><a title="Martel.test.test_swissprot38.s" class="py-name" href="#" onclick="return doclink('link-34', 's', 'link-32');">s</a></tt><tt class="py-op">)</tt><tt class="py-op">,</tt><tt class="py-op">-</tt><tt class="py-number">1</tt><tt class="py-op">,</tt><tt class="py-number">1</tt><tt class="py-op">)</tt> </tt>
<a name="L66"></a><tt class="py-lineno">66</tt> <tt class="py-line"> <tt class="py-name">delta</tt> <tt class="py-op">=</tt> <tt class="py-number">1</tt><tt class="py-op">-</tt><tt class="py-name">delta</tt><tt class="py-op">*</tt><tt class="py-name">delta</tt> </tt>
<a name="L67"></a><tt class="py-lineno">67</tt> <tt class="py-line"> <tt class="py-name">delta</tt> <tt class="py-op">=</tt> <tt class="py-name">delta</tt><tt class="py-op">*</tt><tt class="py-name">delta</tt> </tt>
<a name="L68"></a><tt class="py-lineno">68</tt> <tt class="py-line"> <tt class="py-keyword">return</tt> <tt class="py-name">yest</tt> </tt>
</div><a name="L69"></a><tt class="py-lineno">69</tt> <tt class="py-line"> </tt><script type="text/javascript">
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