2017-07-07 17:54:28 +04:00
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from distutils.core import setup
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2018-02-26 20:11:29 +04:00
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2017-07-07 17:54:28 +04:00
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setup(
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2018-02-26 20:11:29 +04:00
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name='pyFTS',
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2018-02-26 20:29:11 +04:00
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packages=['pyFTS', 'pyFTS.benchmarks', 'pyFTS.common', 'pyFTS.data', 'pyFTS.models.ensemble',
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'pyFTS.models', 'pyFTS.models.seasonal', 'pyFTS.partitioners', 'pyFTS.probabilistic',
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2018-03-01 21:13:17 +04:00
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'pyFTS.tests', 'pyFTS.models.nonstationary', 'pyFTS.models.multivariate'],
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2018-02-26 22:57:59 +04:00
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#package_dir={}
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2018-02-26 20:29:11 +04:00
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package_data={'benchmarks': ['*'], 'common': ['*'], 'data': ['*'],
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2018-03-01 21:13:17 +04:00
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'models': ['*'], 'partitioners': ['*'], 'probabilistic': ['*'], 'tests': ['*']},
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2018-02-26 22:57:59 +04:00
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#data_files=[('data', ['pyFTS/data/Enrollments.csv', 'pyFTS/data/AirPassengers.csv'])],
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include_package_data=True,
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2018-03-06 01:05:52 +04:00
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version='1.2.3',
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2018-02-26 20:11:29 +04:00
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description='Fuzzy Time Series for Python',
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author='Petronio Candido L. e Silva',
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author_email='petronio.candido@gmail.com',
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url='https://github.com/petroniocandido/pyFTS',
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2018-03-06 01:05:52 +04:00
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download_url='https://github.com/petroniocandido/pyFTS/archive/pkg1.2.3.tar.gz',
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2018-02-26 20:11:29 +04:00
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keywords=['forecasting', 'fuzzy time series', 'fuzzy', 'time series forecasting'],
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classifiers=[],
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2018-03-06 01:05:52 +04:00
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install_requires=[
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'numpy','pandas','matplotlib','dill','copy','dispy','multiprocessing','joblib','statsmodels'
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]
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2017-07-07 17:54:28 +04:00
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)
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