8000 MNT Remove encoding declarations: # -*- coding: utf-8 -*- · scikit-learn/scikit-learn@571474c · GitHub
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DimitriPapadopoulosDimitriPapadopoulos
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MNT Remove encoding declarations: # -*- coding: utf-8 -*-
The default source file encoding is UTF-8 in Python 3.
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‎doc/conf.py

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# -*- coding: utf-8 -*-
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#
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# scikit-learn documentation build configuration file, created by
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# sphinx-quickstart on Fri Jan 8 09:13:42 2010.
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#

‎sklearn/cluster/_dbscan.py

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# -*- coding: utf-8 -*-
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"""
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DBSCAN: Density-Based Spatial Clustering of Applications with Noise
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"""

‎sklearn/cluster/_optics.py

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# -*- coding: utf-8 -*-
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"""Ordering Points To Identify the Clustering Structure (OPTICS)
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These routines execute the OPTICS algorithm, and implement various

‎sklearn/cluster/_spectral.py

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# -*- coding: utf-8 -*-
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"""Algorithms for spectral clustering"""
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# Author: Gael Varoquaux gael.varoquaux@normalesup.org

‎sklearn/feature_extraction/tests/test_text.py

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# -*- coding: utf-8 -*-
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from collections.abc import Mapping
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import re
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‎sklearn/feature_extraction/text.py

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# -*- coding: utf-8 -*-
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# Authors: Olivier Grisel <olivier.grisel@ensta.org>
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# Mathieu Blondel <mathieu@mblondel.org>
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# Lars Buitinck

‎sklearn/feature_selection/_base.py

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# -*- coding: utf-8 -*-
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"""Generic feature selection mixin"""
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# Authors: G. Varoquaux, A. Gramfort, L. Buitinck, J. Nothman

‎sklearn/gaussian_process/__init__.py

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# -*- coding: utf-8 -*-
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# Author: Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
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# Vincent Dubourg <vincent.dubourg@gmail.com>
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# (mostly translation, see implementation details)

‎sklearn/linear_model/_ransac.py

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# coding: utf-8
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# Author: Johannes Schönberger
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#
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# License: BSD 3 clause

‎sklearn/linear_model/_theil_sen.py

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# -*- coding: utf-8 -*-
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"""
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A Theil-Sen Estimator for Multiple Linear Regression Model
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"""

‎sklearn/metrics/pairwise.py

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# -*- coding: utf-8 -*-
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# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
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# Mathieu Blondel <mathieu@mblondel.org>
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# Robert Layton <robertlayton@gmail.com>

‎sklearn/naive_bayes.py

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# -*- coding: utf-8 -*-
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"""
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The :mod:`sklearn.naive_bayes` module implements Naive Bayes algorithms. These
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are supervised learning methods based on applying Bayes' theorem with strong

‎sklearn/neighbors/_nca.py

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# coding: utf-8
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"""
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Neighborhood Component Analysis
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"""

‎sklearn/neighbors/_nearest_centroid.py

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"""
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Nearest Centroid Classification
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"""

‎sklearn/neighbors/tests/test_nca.py

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# coding: utf-8
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"""
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Testing for Neighborhood Component Analysis module (sklearn.neighbors.nca)
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"""

‎sklearn/preprocessing/_discretization.py

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# -*- coding: utf-8 -*-
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# Author: Henry Lin <hlin117@gmail.com>
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# Tom Dupré la Tour
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‎sklearn/preprocessing/tests/test_encoders.py

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# -*- coding: utf-8 -*-
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import re
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import numpy as np

‎sklearn/random_projection.py

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"""Random Projection transformers.
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Random Projections are a simple and computationally efficient way to

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