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.gitignore
vendored
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57
.gitignore
vendored
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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env/
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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*.egg-info/
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.installed.cfg
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*.egg
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*,cover
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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target/
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2
MANIFEST
2
MANIFEST
@ -104,7 +104,7 @@ pyFTS/models/seasonal/common.py
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pyFTS/models/seasonal/msfts.py
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pyFTS/models/seasonal/partitioner.py
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pyFTS/models/seasonal/sfts.py
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pyFTS/partitioners/CMeans.py
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pyFTS/partitioners/KMeans.py
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pyFTS/partitioners/Entropy.py
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pyFTS/partitioners/FCM.py
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pyFTS/partitioners/Grid.py
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@ -38,7 +38,7 @@ Fuzzy Time Series (FTS) are non parametric methods for time series forecasting b
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2. **Universe of Discourse Partitioning**: This is the most important step. Here, the range of values of the numerical time series *Y(t)* will be splited in overlapped intervals and for each interval will be created a Fuzzy Set. This step is performed by pyFTS.partition module and its classes (for instance GridPartitioner, EntropyPartitioner, etc). The main parameters are:
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- the number of intervals
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- which fuzzy membership function (on [pyFTS.common.Membership](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/common/Membership.py))
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- partition scheme ([GridPartitioner](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Grid.py), [EntropyPartitioner](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Entropy.py)[3], [FCMPartitioner](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/FCM.py), [CMeansPartitioner](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/CMeans.py), [HuarngPartitioner](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Huarng.py)[4])
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- partition scheme ([GridPartitioner](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Grid.py), [EntropyPartitioner](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Entropy.py)[3], [FCMPartitioner](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/FCM.py), [KMeansPartitioner](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/KMeans.py), [HuarngPartitioner](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Huarng.py)[4])
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Check out the jupyter notebook on [notebooks/Partitioners.ipynb](https://github.com/PYFTS/notebooks/blob/master/Partitioners.ipynb) for sample codes.
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@ -28,10 +28,10 @@ pyFTS.partitioners.Class module
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:undoc-members:
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:show-inheritance:
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pyFTS.partitioners.CMeans module
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pyFTS.partitioners.KMeans module
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--------------------------------
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.. automodule:: pyFTS.partitioners.CMeans
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.. automodule:: pyFTS.partitioners.KMeans
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:members:
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:undoc-members:
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:show-inheritance:
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@ -14,7 +14,7 @@ def distance(x, y):
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return math.sqrt(tmp)
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def c_means(k, dados, tam):
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def k_means(k, dados, tam):
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# Инициализирует центроиды, выбирая случайные элементы из множества
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centroides = [dados[rnd.randint(0, len(dados)-1)] for kk in range(0, k)]
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@ -77,16 +77,16 @@ def c_means(k, dados, tam):
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return centroides
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class CMeansPartitioner(partitioner.Partitioner):
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class KMeansPartitioner(partitioner.Partitioner):
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def __init__(self, **kwargs):
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super(CMeansPartitioner, self).__init__(name="CMeans", **kwargs)
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super(KMeansPartitioner, self).__init__(name="KMeans", **kwargs)
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def build(self, data):
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sets = {}
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kwargs = {'type': self.type, 'variable': self.variable}
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centroides = c_means(self.partitions, data, 1)
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centroides = k_means(self.partitions, data, 1)
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centroides = [v[0] for v in centroides]
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centroides.append(self.max)
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centroides.append(self.min)
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@ -11,7 +11,7 @@ from mpl_toolkits.mplot3d import Axes3D
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import datetime
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import pandas as pd
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from pyFTS.partitioners import Grid, CMeans, FCM, Entropy
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from pyFTS.partitioners import Grid, KMeans, FCM, Entropy
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from pyFTS.common import FLR, FuzzySet, Membership, Transformations, Util, fts
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from pyFTS import sfts
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from pyFTS.models import msfts
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1
setup.py
1
setup.py
@ -26,6 +26,7 @@ setuptools.setup(
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'Programming Language :: Python :: 3.6',
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'Programming Language :: Python :: 3.8',
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'Programming Language :: Python :: 3.10',
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'Programming Language :: Python :: 3.11',
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'Intended Audience :: Science/Research',
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'Intended Audience :: Developers',
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'Intended Audience :: Education',
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