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Merge pull request #158 from wilsonrljr/fix/152
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‎CHANGELOG.md

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# Changes in SysIdentPy
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## v0.5.1
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### CONTRIBUTORS
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- wilsonrljr
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### CHANGES
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This update addresses a critical bug related to the unbiased estimator. The issue previously impacted all basis functions but has now been resolved. This release keep providing crucial groundwork for the future development of SysIdentPy, making easier to add new features and improve the code, setting the stage for a robust and feature-complete 1.0.0 release in the feature.
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- **Documentation:**
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- Remove unnecessary code when importing basis functions in many examples.
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- **API Changes:**
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- Fix unbiased estimator for every basis function.
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## v0.5.0
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### CONTRIBUTORS

‎docs/changelog/changelog.md

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# Changes in SysIdentPy
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## v0.5.1
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### CONTRIBUTORS
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- wilsonrljr
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### CHANGES
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This update addresses a critical bug related to the unbiased estimator. The issue previously impacted all basis functions but has now been resolved. This release keep providing crucial groundwork for the future development of SysIdentPy, making easier to add new features and improve the code, setting the stage for a robust and feature-complete 1.0.0 release in the feature.
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- **Documentation:**
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- Remove unnecessary code when importing basis functions in many examples.
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- **API Changes:**
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- Fix unbiased estimator for every basis function.
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## v0.5.0
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### CONTRIBUTORS

‎docs/examples/NFIR.ipynb

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"import numpy as np\n",
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"from sysidentpy.utils.generate_data import get_siso_data\n",
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"from sysidentpy.metrics import root_relative_squared_error\n",
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"from sysidentpy.basis_function._basis_function import Polynomial\n",
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"from sysidentpy.basis_function import Polynomial\n",
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"from sysidentpy.parameter_estimation import LeastSquares, RecursiveLeastSquares\n",
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"from sysidentpy.utils.display_results import results\n",
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"from sysidentpy.utils.plotting import plot_results\n",

‎docs/examples/aols.ipynb

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"import pandas as pd\n",
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"from sysidentpy.utils.generate_data import get_siso_data\n",
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"from sysidentpy.metrics import root_relative_squared_error\n",
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"from sysidentpy.basis_function._basis_function import Polynomial\n",
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"from sysidentpy.basis_function import Polynomial\n",
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"from sysidentpy.utils.display_results import results\n",
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"from sysidentpy.utils.plotting import plot_residues_correlation, plot_results\n",
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"from sysidentpy.residues.residues_correlation import (\n",

‎docs/examples/basic_steps.ipynb

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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"from sysidentpy.model_structure_selection import FROLS\n",
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"from sysidentpy.basis_function._basis_function import Polynomial\n",
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"from sysidentpy.basis_function import Polynomial\n",
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"from sysidentpy.parameter_estimation import LeastSquares\n",
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"from sysidentpy.metrics import root_relative_squared_error\n",
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"from sysidentpy.utils.generate_data import get_siso_data\n",
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Generating 1 input 1 output sample data \n",
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"## Generating 1 input 1 output sample data\n",
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"\n",
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"The data is generated by simulating the following model:\n",
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"\n",
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"source": [
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"```{note}\n",
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" Here we are creating random samples with white noise and letting the algorithm choose\n",
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" the number of terms based on the minimum value of information criteria. \n",
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" This is not the best approach in System Identification, but serves as a simple example. \n",
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" The information criteria must be used as an __auxiliary tool__ to select *n_terms*. \n",
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" the number of terms based on the minimum value of information criteria.\n",
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" This is not the best approach in System Identification, but serves as a simple example.\n",
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" The information criteria must be used as an __auxiliary tool__ to select *n_terms*.\n",
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" Plot the information values to help you on that!\n",
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"\n",
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" If you run the example above several times you might find some cases where the\n",
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"```"
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]
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"cell_type": "markdown",
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"metadata": {},
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"```{note}\n",
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"This documentation and the examples below are written with MyST Markdown, a form\n",
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"of markdown that works with Sphinx. For more information about MyST markdown, and\n",
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"to use MyST markdown with your Sphinx website,\n",
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"see [the MyST-parser documentation](https://myst-parser.readthedocs.io/)\n",
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"```"
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]
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},
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"cell_type": "markdown",
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"metadata": {},

‎docs/examples/defining_lags.ipynb

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"outputs": [],
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"source": [
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"from sysidentpy.model_structure_selection import FROLS\n",
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"from sysidentpy.basis_function._basis_function import Polynomial"
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"from sysidentpy.basis_function import Polynomial"
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]
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},
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{

‎docs/examples/entropic_regression.ipynb

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"import numpy as np\n",
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"import pandas as pd\n",
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"from sysidentpy.model_structure_selection import ER\n",
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"from sysidentpy.basis_function._basis_function import Polynomial\n",
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"from sysidentpy.basis_function import Polynomial\n",
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"from sysidentpy.parameter_estimation import RecursiveLeastSquares\n",
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"from sysidentpy.metrics import root_relative_squared_error\n",
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"from sysidentpy.utils.display_results import results\n",

‎docs/examples/extended_least_squares.ipynb

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‎docs/examples/f_16_benchmark.ipynb

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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"from sysidentpy.model_structure_selection import FROLS\n",
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"from sysidentpy.basis_function._basis_function import Polynomial\n",
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"from sysidentpy.basis_function import Polynomial\n",
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"from sysidentpy.parameter_estimation import LeastSquares\n",
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"from sysidentpy.metrics import root_relative_squared_error\n",
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"from sysidentpy.utils.display_results import results\n",

‎docs/examples/general_estimators.ipynb

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"from sklearn.ensemble import GradientBoostingRegressor\n",
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"from catboost import CatBoostRegressor\n",
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"\n",
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"from sysidentpy.basis_function._basis_function import Polynomial, Fourier\n",
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"from sysidentpy.basis_function import Polynomial, Fourier\n",
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"from sysidentpy.utils.plotting import plot_residues_correlation, plot_results\n",
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"from sysidentpy.residues.residues_correlation import (\n",
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" compute_residues_autocorrelation,\n",

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