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generate_explanations_TransE_allow_all_tails.html
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<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN">
<html><head><title>Python: module generate_explanations_TransE_allow_all_tails</title>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
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<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="heading">
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<td valign=bottom> <br>
<font color="#ffffff" face="helvetica, arial"> <br><big><big><strong>generate_explanations_TransE_allow_all_tails</strong></big></big></font></td
><td align=right valign=bottom
><font color="#ffffff" face="helvetica, arial"><a href=".">index</a><br><a href="file:/data/s6jetraj/Development/TEST/CBRF-FB13-/generate_explanations_TransE_allow_all_tails.py">/data/s6jetraj/Development/TEST/CBRF-FB13-/generate_explanations_TransE_allow_all_tails.py</a></font></td></tr></table>
<p><tt>@author: Jelena Trajkovic<br>
<br>
This file contains the methods for explanations generation by using slightly adapted<br>
Polleti/Cozman model (composed_relations are introduced).</tt></p>
<p>
<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="section">
<tr bgcolor="#aa55cc">
<td colspan=3 valign=bottom> <br>
<font color="#ffffff" face="helvetica, arial"><big><strong>Modules</strong></big></font></td></tr>
<tr><td bgcolor="#aa55cc"><tt> </tt></td><td> </td>
<td width="100%"><table width="100%" summary="list"><tr><td width="25%" valign=top><a href="numpy.html">numpy</a><br>
</td><td width="25%" valign=top><a href="pandas.html">pandas</a><br>
</td><td width="25%" valign=top><a href="TransE_model_allow_all_tails.html">TransE_model_allow_all_tails</a><br>
</td><td width="25%" valign=top><a href="train_model.html">train_model</a><br>
</td></tr></table></td></tr></table><p>
<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="section">
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<td colspan=3 valign=bottom> <br>
<font color="#ffffff" face="helvetica, arial"><big><strong>Functions</strong></big></font></td></tr>
<tr><td bgcolor="#eeaa77"><tt> </tt></td><td> </td>
<td width="100%"><dl><dt><a name="-generate_explanations"><strong>generate_explanations</strong></a>(head, features_names, relation, tail, head_samples, dataset, model)</dt><dd><tt>generate explanations for the best hit(tail) for (head, relation) pair.</tt></dd></dl>
<dl><dt><a name="-predict_head_samples_tails"><strong>predict_head_samples_tails</strong></a>(head, features_names, head_samples, dataset, relation, model)</dt><dd><tt>returns predicted tails for the given head samples, for the selected features, <br>
by using TransE model, which is not restricted to the ground truth dataset.</tt></dd></dl>
</td></tr></table>
</body></html>