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<title>Welcome to SysIdentPy’s documentation! — NARMAX models</title>
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Polynomial NARX
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NARX Neural Network
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Catboost-narx
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Catboost without NARX configuration
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<div class="section" id="welcome-to-sysidentpy-s-documentation">
<h1>Welcome to SysIdentPy’s documentation!<a class="headerlink" href="#welcome-to-sysidentpy-s-documentation" title="Permalink to this headline">¶</a></h1>
<p><strong>SysIdentPy</strong> is a Python module for System Identification using <strong>NARMAX</strong> models built on top of <strong>numpy</strong> and is distributed under the 3-Clause BSD license.</p>
<p>The project was started in by Wilson R. L. Junior, Luan Pascoal C. Andrade and Samir A. M. Martins as a project for System Identification discipline. Samuel joined early in 2019 and since then have contributed.</p>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<p>The examples directory has several Jupyter notebooks presenting basic tutorials of how to use the package and some specific applications of <strong>SysIdentPy</strong>. <a class="reference external" href="http://sysidentpy.org/notebooks.html">Try it out!</a></p>
</div>
<div class="admonition tip">
<p class="admonition-title">Tip</p>
<p>SysIdentPy now support NARX Neural Network and General estimators, e.g., sklearn estimators and Catboost. Check it out!</p>
</div>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">sysidentpy.metrics</span> <span class="kn">import</span> <span class="n">mean_squared_error</span>
<span class="kn">from</span> <span class="nn">sysidentpy.utils.generate_data</span> <span class="kn">import</span> <span class="n">get_siso_data</span>
<span class="c1"># Generate a dataset of a simulated dynamical system</span>
<span class="n">x_train</span><span class="p">,</span> <span class="n">x_valid</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_valid</span> <span class="o">=</span> <span class="n">get_siso_data</span><span class="p">(</span>
<span class="n">n</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
<span class="n">colored_noise</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">sigma</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span>
<span class="n">train_percentage</span><span class="o">=</span><span class="mi">80</span>
<span class="p">)</span>
</pre></div>
</div>
<div class="section" id="polynomial-narx">
<h2>Polynomial NARX<a class="headerlink" href="#polynomial-narx" title="Permalink to this headline">¶</a></h2>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sysidentpy.polynomial_basis</span> <span class="kn">import</span> <span class="n">PolynomialNarmax</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">PolynomialNarmax</span><span class="p">(</span>
<span class="n">non_degree</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="n">order_selection</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">n_info_values</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
<span class="n">extended_least_squares</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">ylag</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="n">xlag</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="n">info_criteria</span><span class="o">=</span><span class="s1">'aic'</span><span class="p">,</span>
<span class="n">estimator</span><span class="o">=</span><span class="s1">'least_squares'</span>
<span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">yhat</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_valid</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">)</span>
<span class="n">results</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">results</span><span class="p">(</span><span class="n">err_precision</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="s1">'dec'</span><span class="p">),</span>
<span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'Regressors'</span><span class="p">,</span> <span class="s1">'Parameters'</span><span class="p">,</span> <span class="s1">'ERR'</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
<span class="n">Regressors</span> <span class="n">Parameters</span> <span class="n">ERR</span>
<span class="mi">0</span> <span class="n">x1</span><span class="p">(</span><span class="n">k</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span> <span class="mf">0.9000</span> <span class="mf">0.95556574</span>
<span class="mi">1</span> <span class="n">y</span><span class="p">(</span><span class="n">k</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="mf">0.1999</span> <span class="mf">0.04107943</span>
<span class="mi">2</span> <span class="n">x1</span><span class="p">(</span><span class="n">k</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="n">y</span><span class="p">(</span><span class="n">k</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="mf">0.1000</span> <span class="mf">0.00335113</span>
<span class="n">ee</span><span class="p">,</span> <span class="n">ex</span><span class="p">,</span> <span class="n">extras</span><span class="p">,</span> <span class="n">lam</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">residuals</span><span class="p">(</span><span class="n">x_valid</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">,</span> <span class="n">yhat</span><span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">plot_result</span><span class="p">(</span><span class="n">y_valid</span><span class="p">,</span> <span class="n">yhat</span><span class="p">,</span> <span class="n">ee</span><span class="p">,</span> <span class="n">ex</span><span class="p">)</span>
</pre></div>
</div>
<img alt="_images/polynomial_narmax.png" src="_images/polynomial_narmax.png" />
</div>
<div class="section" id="narx-neural-network">
<h2>NARX Neural Network<a class="headerlink" href="#narx-neural-network" title="Permalink to this headline">¶</a></h2>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
<span class="kn">from</span> <span class="nn">sysidentpy.neural_network</span> <span class="kn">import</span> <span class="n">NARXNN</span>
<span class="k">class</span> <span class="nc">NARX</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">lin</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">lin2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">lin3</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tanh</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Tanh</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">xb</span><span class="p">):</span>
<span class="n">z</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lin</span><span class="p">(</span><span class="n">xb</span><span class="p">)</span>
<span class="n">z</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tanh</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
<span class="n">z</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lin2</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
<span class="n">z</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tanh</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
<span class="n">z</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lin3</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
<span class="k">return</span> <span class="n">z</span>
<span class="n">narx_net</span> <span class="o">=</span> <span class="n">NARXNN</span><span class="p">(</span>
<span class="n">net</span><span class="o">=</span><span class="n">NARX</span><span class="p">(),</span>
<span class="n">ylag</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="n">xlag</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="n">loss_func</span><span class="o">=</span><span class="s1">'mse_loss'</span><span class="p">,</span>
<span class="n">optimizer</span><span class="o">=</span><span class="s1">'Adam'</span><span class="p">,</span>
<span class="n">epochs</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span>
<span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">optim_params</span><span class="o">=</span><span class="p">{</span><span class="s1">'betas'</span><span class="p">:</span> <span class="p">(</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.999</span><span class="p">),</span> <span class="s1">'eps'</span><span class="p">:</span> <span class="mf">1e-05</span><span class="p">}</span> <span class="c1"># optional parameters of the optimizer</span>
<span class="p">)</span>
<span class="n">train_dl</span> <span class="o">=</span> <span class="n">narx_net</span><span class="o">.</span><span class="n">data_transform</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">valid_dl</span> <span class="o">=</span> <span class="n">narx_net</span><span class="o">.</span><span class="n">data_transform</span><span class="p">(</span><span class="n">x_valid</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">)</span>
<span class="n">narx_net</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_dl</span><span class="p">,</span> <span class="n">valid_dl</span><span class="p">)</span>
<span class="n">yhat</span> <span class="o">=</span> <span class="n">narx_net</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_valid</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">)</span>
<span class="n">ee</span><span class="p">,</span> <span class="n">ex</span><span class="p">,</span> <span class="n">extras</span><span class="p">,</span> <span class="n">lam</span> <span class="o">=</span> <span class="n">narx_net</span><span class="o">.</span><span class="n">residuals</span><span class="p">(</span><span class="n">x_valid</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">,</span> <span class="n">yhat</span><span class="p">)</span>
<span class="n">narx_net</span><span class="o">.</span><span class="n">plot_result</span><span class="p">(</span><span class="n">y_valid</span><span class="p">,</span> <span class="n">yhat</span><span class="p">,</span> <span class="n">ee</span><span class="p">,</span> <span class="n">ex</span><span class="p">)</span>
</pre></div>
</div>
<img alt="_images/narx_network.png" src="_images/narx_network.png" />
</div>
<div class="section" id="catboost-narx">
<h2>Catboost-narx<a class="headerlink" href="#catboost-narx" title="Permalink to this headline">¶</a></h2>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sysidentpy.general_estimators</span> <span class="kn">import</span> <span class="n">NARX</span>
<span class="kn">from</span> <span class="nn">catboost</span> <span class="kn">import</span> <span class="n">CatBoostRegressor</span>
<span class="n">catboost_narx</span> <span class="o">=</span> <span class="n">NARX</span><span class="p">(</span>
<span class="n">base_estimator</span><span class="o">=</span><span class="n">CatBoostRegressor</span><span class="p">(</span>
<span class="n">iterations</span><span class="o">=</span><span class="mi">300</span><span class="p">,</span>
<span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
<span class="n">depth</span><span class="o">=</span><span class="mi">6</span><span class="p">),</span>
<span class="n">xlag</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="n">ylag</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="n">fit_params</span><span class="o">=</span><span class="p">{</span><span class="s1">'verbose'</span><span class="p">:</span> <span class="kc">False</span><span class="p">}</span>
<span class="p">)</span>
<span class="n">catboost_narx</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">yhat</span> <span class="o">=</span> <span class="n">catboost_narx</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_valid</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">)</span>
<span class="n">ee</span><span class="p">,</span> <span class="n">ex</span><span class="p">,</span> <span class="n">extras</span><span class="p">,</span> <span class="n">lam</span> <span class="o">=</span> <span class="n">catboost_narx</span><span class="o">.</span><span class="n">residuals</span><span class="p">(</span><span class="n">x_valid</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">,</span> <span class="n">yhat</span><span class="p">)</span>
<span class="n">catboost_narx</span><span class="o">.</span><span class="n">plot_result</span><span class="p">(</span><span class="n">y_valid</span><span class="p">,</span> <span class="n">yhat</span><span class="p">,</span> <span class="n">ee</span><span class="p">,</span> <span class="n">ex</span><span class="p">)</span>
</pre></div>
</div>
<img alt="_images/catboost_narx.png" src="_images/catboost_narx.png" />
</div>
<div class="section" id="catboost-without-narx-configuration">
<h2>Catboost without NARX configuration<a class="headerlink" href="#catboost-without-narx-configuration" title="Permalink to this headline">¶</a></h2>
<p>The following is the Catboost performance <em>without</em> the NARX configuration.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">plot_results</span><span class="p">(</span><span class="n">yvalid</span><span class="p">,</span> <span class="n">yhat</span><span class="p">):</span>
<span class="n">_</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">y_valid</span><span class="p">[:</span><span class="mi">200</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Data'</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'o'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">yhat</span><span class="p">[:</span><span class="mi">200</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Prediction'</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'*'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">"$n$"</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">18</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"$y[n]$"</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">18</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">18</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="n">catboost</span> <span class="o">=</span> <span class="n">CatBoostRegressor</span><span class="p">(</span>
<span class="n">iterations</span><span class="o">=</span><span class="mi">300</span><span class="p">,</span>
<span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
<span class="n">depth</span><span class="o">=</span><span class="mi">6</span>
<span class="p">)</span>
<span class="n">catboost</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">plot_results</span><span class="p">(</span><span class="n">y_valid</span><span class="p">,</span> <span class="n">catboost</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_valid</span><span class="p">))</span>
</pre></div>
</div>
<img alt="_images/catboost.png" src="_images/catboost.png" />
<div class="section" id="changelog">
<h3>Changelog<a class="headerlink" href="#changelog" title="Permalink to this headline">¶</a></h3>
<p>See the <a class="reference external" href="https://github.com/wilsonrljr/sysidentpy.whats_is_new.md">changelog</a>
for a history of notable changes to <strong>SysIdentPy</strong>.</p>
</div>
<div class="section" id="development">
<h3>Development<a class="headerlink" href="#development" title="Permalink to this headline">¶</a></h3>
<p>We welcome new contributors of all experience levels. The <strong>SysIdentPy</strong> community goals are to be helpful, welcoming, and effective.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>We use the <cite>pytest</cite> package for testing. The test functions are located in tests subdirectories at each folder inside <strong>SysIdentPy</strong>, which check the validity of the algorithms.</p>
</div>
<p>Run the <cite>pytest</cite> in the respective folder to perform all the tests of the corresponding sub-packages.</p>
<p>Currently, we have around 81% of code coverage.</p>
<p>You can install pytest using</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="o">-</span><span class="n">U</span> <span class="n">pytest</span>
</pre></div>
</div>
</div>
<div class="section" id="example-of-how-to-run-the-tests">
<h3>Example of how to run the tests:<a class="headerlink" href="#example-of-how-to-run-the-tests" title="Permalink to this headline">¶</a></h3>
<p>Open a terminal emulator of your choice and go to a subdirectory, e.g,</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>\<span class="n">sysidentpy</span>\<span class="n">metrics</span>\
</pre></div>
</div>
<p>Just type <code class="code docutils literal notranslate"><span class="pre">pytest</span></code> and you get a result like</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">==========</span> <span class="n">test</span> <span class="n">session</span> <span class="n">starts</span> <span class="o">==========</span>
<span class="n">platform</span> <span class="n">linux</span> <span class="o">--</span> <span class="n">Python</span> <span class="mf">3.7</span><span class="o">.</span><span class="mi">6</span><span class="p">,</span> <span class="n">pytest</span><span class="o">-</span><span class="mf">5.4</span><span class="o">.</span><span class="mi">2</span><span class="p">,</span> <span class="n">py</span><span class="o">-</span><span class="mf">1.8</span><span class="o">.</span><span class="mi">1</span><span class="p">,</span> <span class="n">pluggy</span><span class="o">-</span><span class="mf">0.13</span><span class="o">.</span><span class="mi">1</span>
<span class="n">rootdir</span><span class="p">:</span> <span class="o">~/</span><span class="n">sysidentpy</span>
<span class="n">plugins</span><span class="p">:</span> <span class="n">cov</span><span class="o">-</span><span class="mf">2.8</span><span class="o">.</span><span class="mi">1</span>
<span class="n">collected</span> <span class="mi">12</span> <span class="n">items</span>
<span class="n">tests</span><span class="o">/</span><span class="n">test_regression</span><span class="o">.</span><span class="n">py</span> <span class="o">............</span> <span class="p">[</span><span class="mi">100</span><span class="o">%</span><span class="p">]</span>
<span class="o">==========</span> <span class="mi">12</span> <span class="n">passed</span> <span class="ow">in</span> <span class="mf">2.45</span><span class="n">s</span> <span class="o">==================</span>
</pre></div>
</div>
<p>You can also see the code coverage using the <code class="code docutils literal notranslate"><span class="pre">pytest-cov</span></code> package. First, install <code class="code docutils literal notranslate"><span class="pre">pytest-cov</span></code> using</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">pytest</span><span class="o">-</span><span class="n">cov</span>
</pre></div>
</div>
<p>Run the command below in the <strong>SysIdentPy</strong> root directory, to generate the report.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pytest</span> <span class="o">--</span><span class="n">cov</span><span class="o">=.</span>
</pre></div>
</div>
</div>
<div class="section" id="source-code">
<h3>Source code<a class="headerlink" href="#source-code" title="Permalink to this headline">¶</a></h3>
<p>You can check the latest sources with the command:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">git</span> <span class="n">clone</span> <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">github</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">wilsonrljr</span><span class="o">/</span><span class="n">sysidentpy</span><span class="o">.</span><span class="n">git</span>
</pre></div>
</div>
</div>
<div class="section" id="project-history">
<h3>Project History<a class="headerlink" href="#project-history" title="Permalink to this headline">¶</a></h3>
<p>The project was started by Wilson R. L. Junior, Luan Pascoal and Samir A. M. Martins as a project for System Identification discipline. Samuel joined early in 2019 and since then have contributed.</p>
<p>The initial purpose was to learn the python language. Over time, the project has matured to the state it is in today.</p>
<p>The project is currently maintained by its creators and looking for
contributors.</p>
</div>
<div class="section" id="communication">
<h3>Communication<a class="headerlink" href="#communication" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li><p>Discord server: <a class="reference external" href="https://discord.gg/8eGE3PQ">https://discord.gg/8eGE3PQ</a></p></li>
<li><p>Website(soon): <a class="reference external" href="http://sysidentpy.org">http://sysidentpy.org</a></p></li>
</ul>
</div>
<div class="section" id="citation">
<h3>Citation<a class="headerlink" href="#citation" title="Permalink to this headline">¶</a></h3>
<p>If you use <strong>SysIdentPy</strong> on your project, please <a class="reference external" href="mailto:wilsonrljr%40outlook.com">drop me a line</a>.</p>
<p>If you use <strong>SysIdentPy</strong> on your scientific publication, we would appreciate citations to the following paper:</p>
<ul>
<li><p>Lacerda et al., (2020). SysIdentPy: A Python package for System Identification using NARMAX models. Journal of Open Source Software, 5(54), 2384, <a class="reference external" href="https://doi.org/10.21105/joss.02384">https://doi.org/10.21105/joss.02384</a></p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@article</span><span class="p">{</span><span class="n">Lacerda2020</span><span class="p">,</span>
<span class="n">doi</span> <span class="o">=</span> <span class="p">{</span><span class="mf">10.21105</span><span class="o">/</span><span class="n">joss</span><span class="o">.</span><span class="mi">02384</span><span class="p">},</span>
<span class="n">url</span> <span class="o">=</span> <span class="p">{</span><span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">doi</span><span class="o">.</span><span class="n">org</span><span class="o">/</span><span class="mf">10.21105</span><span class="o">/</span><span class="n">joss</span><span class="o">.</span><span class="mi">02384</span><span class="p">},</span>
<span class="n">year</span> <span class="o">=</span> <span class="p">{</span><span class="mi">2020</span><span class="p">},</span>
<span class="n">publisher</span> <span class="o">=</span> <span class="p">{</span><span class="n">The</span> <span class="n">Open</span> <span class="n">Journal</span><span class="p">},</span>
<span class="n">volume</span> <span class="o">=</span> <span class="p">{</span><span class="mi">5</span><span class="p">},</span>
<span class="n">number</span> <span class="o">=</span> <span class="p">{</span><span class="mi">54</span><span class="p">},</span>
<span class="n">pages</span> <span class="o">=</span> <span class="p">{</span><span class="mi">2384</span><span class="p">},</span>
<span class="n">author</span> <span class="o">=</span> <span class="p">{</span><span class="n">Wilson</span> <span class="n">Rocha</span> <span class="n">Lacerda</span> <span class="n">Junior</span> <span class="ow">and</span> <span class="n">Luan</span> <span class="n">Pascoal</span> <span class="n">Costa</span> <span class="n">da</span> <span class="n">Andrade</span> <span class="ow">and</span> <span class="n">Samuel</span> <span class="n">Carlos</span> <span class="n">Pessoa</span> <span class="n">Oliveira</span> <span class="ow">and</span> <span class="n">Samir</span> <span class="n">Angelo</span> <span class="n">Milani</span> <span class="n">Martins</span><span class="p">},</span>
<span class="n">title</span> <span class="o">=</span> <span class="p">{</span><span class="n">SysIdentPy</span><span class="p">:</span> <span class="n">A</span> <span class="n">Python</span> <span class="n">package</span> <span class="k">for</span> <span class="n">System</span> <span class="n">Identification</span> <span class="n">using</span> <span class="n">NARMAX</span> <span class="n">models</span><span class="p">},</span>
<span class="n">journal</span> <span class="o">=</span> <span class="p">{</span><span class="n">Journal</span> <span class="n">of</span> <span class="n">Open</span> <span class="n">Source</span> <span class="n">Software</span><span class="p">}</span>
<span class="p">}</span>
</pre></div>
</div>
</li>
</ul>
</div>
<div class="section" id="inspiration">
<h3>Inspiration<a class="headerlink" href="#inspiration" title="Permalink to this headline">¶</a></h3>
<p>The documentation and structure (even this section) is openly inspired by sklearn, einsteinpy, and many others as we used (and keep using) them to learn.</p>
</div>
<div class="section" id="contents">
<h3>Contents<a class="headerlink" href="#contents" title="Permalink to this headline">¶</a></h3>
<div class="toctree-wrapper compound">
<ul>
<li class="toctree-l1"><a class="reference internal" href="installation.html">Install Guide</a></li>
<li class="toctree-l1"><a class="reference internal" href="introduction_to_narmax.html">A brief introduction to NARMAX models.</a></li>
<li class="toctree-l1"><a class="reference internal" href="user_guide.html">User Guide</a></li>
<li class="toctree-l1"><a class="reference internal" href="dev_guide.html">Contributing</a></li>
<li class="toctree-l1"><a class="reference internal" href="notebooks.html">Jupyter notebooks</a></li>
<li class="toctree-l1"><a class="reference internal" href="changelog/v0.1.5.html">Changes in SysIdentPy</a></li>
<li class="toctree-l1"><a class="reference internal" href="code.html">Codes</a></li>
<li class="toctree-l1"><a class="reference internal" href="code.html#module-sysidentpy.base">sysidentpy base</a></li>
<li class="toctree-l1"><a class="reference internal" href="code.html#sysidentpy-narmax">sysidentpy narmax</a></li>
<li class="toctree-l1"><a class="reference internal" href="code.html#sysidentpy-simulation">sysidentpy simulation</a></li>
<li class="toctree-l1"><a class="reference internal" href="code.html#sysidentpy-narx-neural-network">sysidentpy narx_neural_network</a></li>
<li class="toctree-l1"><a class="reference internal" href="code.html#sysidentpy-general-estimators">sysidentpy general_estimators</a></li>
<li class="toctree-l1"><a class="reference internal" href="code.html#module-sysidentpy.residues.residues_correlation">sysidentpy residues</a></li>
<li class="toctree-l1"><a class="reference internal" href="code.html#module-sysidentpy.metrics._regression">sysidentpy metrics</a></li>
<li class="toctree-l1"><a class="reference internal" href="code.html#module-sysidentpy.parameter_estimation.estimators">sysidentpy estimators</a></li>
<li class="toctree-l1"><a class="reference internal" href="code.html#module-sysidentpy.utils._check_arrays">sysidentpy utils</a></li>
<li class="toctree-l1"><a class="reference internal" href="code.html#module-sysidentpy.utils.generate_data">sysidentpy generate data</a></li>
<li class="toctree-l1"><a class="reference internal" href="code.html#indices-and-tables">Indices and tables</a></li>
</ul>
</div>
</div>
</div>
</div>
</div>
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By Wilson Rocha, Luan Pascoal, Samuel Oliveira, Samir Martins<br/>
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