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<title>Table of Contents — Spectral Methods in Data Processing - Final Project 1.0 documentation</title>
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Spectral Methods in Data Processing - Final Project</a>
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<li class="toctree-l2"><a class="reference internal" href="#getting-started">Getting Started</a></li>
<li class="toctree-l2"><a class="reference internal" href="#running-the-unit-tests">Running the Unit-Tests</a></li>
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<div class="section" id="spectral-methods-in-data-processing-0372-4001-final-project">
<h1>Spectral Methods in Data Processing (0372-4001) - Final Project<a class="headerlink" href="#spectral-methods-in-data-processing-0372-4001-final-project" title="Permalink to this headline">¶</a></h1>
<a class="reference external image-reference" href="https://colab.research.google.com/github/RedCrow9564/SpectralMethodsProject-RandomSVD/blob/master/Spectral_Methods_Project_Random_SVD.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a>
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<a class="reference external image-reference" href="https://github.com/RedCrow9564/SpectralMethodsProject-RandomSVD/workflows/Run%20Unit-Tests/badge.svg"><img alt="Run Unit-Tests" src="https://github.com/RedCrow9564/SpectralMethodsProject-RandomSVD/workflows/Run%20Unit-Tests/badge.svg" /></a>
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<p>This is a project submitted as a requirement for this course. <a class="reference external" href="https://www30.tau.ac.il/yedion/syllabus.asp?course=0372400101">The course</a> was administered in Fall 2019-2020 (before the Coronavirus outbreak…) in <a class="reference external" href="https://en-exact-sciences.tau.ac.il/math">Tel-Aviv University - School of Mathematical Sciences</a>, and taught by <a class="reference external" href="https://english.tau.ac.il/profile/yoelsh">Prof. Yoel Shkolnisky</a>.
This project is a reconstruction of experiments of <a class="reference external" href="#1">[1]</a> for algorithms for Randomized SVD and Randomized Interpolative Decompositions. A complete documentation of the code is available <a class="reference external" href="docs/main_doc.html">here</a>.</p>
<div class="section" id="getting-started">
<h2>Getting Started<a class="headerlink" href="#getting-started" title="Permalink to this headline">¶</a></h2>
<p>The code can be fetched from <a class="reference external" href="https://github.com/RedCrow9564/SpectralMethodsProject-RandomSVD.git">this repo</a>. The Jupyter Notebook version does the same work, and can be deployed to <a class="reference external" href="https://colab.research.google.com/github/RedCrow9564/SpectralMethodsProject-RandomSVD/blob/master/Spectral_Methods_Project_Random_SVD.ipynb">Google Colab</a>. While the the notebook version can be used immediately, this code has some prerequisites.
Any questions about this project may be sent by mail to ‘eladeatah’ at mail.tau.ac.il (replace ‘at’ by @).</p>
<div class="section" id="prerequisites">
<h3>Prerequisites<a class="headerlink" href="#prerequisites" title="Permalink to this headline">¶</a></h3>
<p>This code was developed for Windows10 OS and tested using the following Python 3.7.6 dependencies. These dependencies are listed in <a class="reference external" href="requirements.txt">requirements.txt</a>.
All these packages can be installed using the ‘pip’ package manager (when the command window is in the main directory where requirements.txt is located):</p>
<div class="highlight-guess notranslate"><div class="highlight"><pre><span></span>pip install -r requirements.txt
</pre></div>
</div>
<p>All the packages, except for Sacred, are available as well using ‘conda’ package manager.</p>
</div>
</div>
<div class="section" id="running-the-unit-tests">
<h2>Running the Unit-Tests<a class="headerlink" href="#running-the-unit-tests" title="Permalink to this headline">¶</a></h2>
<p>The Unit-Test files are:</p>
<ul class="simple">
<li><p><a class="reference external" href="UnitTests/test_data_creation.py">test_data_creation.py</a> - Tests the data created for all experiments is the data described in the paper <a class="reference external" href="#1">[1]</a> .</p></li>
<li><p><a class="reference external" href="UnitTests/test_random_svd.py">test_random_svd.py</a> - Tests the implementation of the Random SVD algorithm.</p></li>
<li><p><a class="reference external" href="UnitTests/test_random_id.py">test_random_id.py</a> - Tests the implementation of the Random Interpolative Decompositino algorithm.</p></li>
</ul>
<p>Running any of these tests can be performed by:</p>
<div class="highlight-guess notranslate"><div class="highlight"><pre><span></span><python_path> -m unittest <test_file_path>
</pre></div>
</div>
</div>
<div class="section" id="acknowledgments">
<h2>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">¶</a></h2>
<p>Credits for the original algorithms, paper and results of <a class="reference external" href="#1">[1]</a> belong to its respectful authors: P.-G. Martinsson, V. Rokhlin, and M. Tygert.</p>
</div>
<div class="section" id="license">
<h2>License<a class="headerlink" href="#license" title="Permalink to this headline">¶</a></h2>
<p>This project is licensed under the MIT License - see the <a class="reference external" href="LICENSE">LICENSE</a> file for details</p>
</div>
<div class="section" id="references">
<h2>References<a class="headerlink" href="#references" title="Permalink to this headline">¶</a></h2>
<p><span class="raw-html-m2r"><a id="1">[1]</a></span> <a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S1063520310000242">P.-G. Martinsson, V. Rokhlin, and M. Tygert. A randomized al-
gorithm for the decomposition of matrices. Applied and Computa-
tional Harmonic Analysis, 30(1):47—68, 2011</a>.</p>
</div>
</div>
<div class="section" id="indices-and-tables">
<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this headline">¶</a></h1>
<ul class="simple">
<li><p><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></p></li>
<li><p><a class="reference internal" href="py-modindex.html"><span class="std std-ref">Module Index</span></a></p></li>
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