.gitignore

@@ -1,57 +0,0 @@
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-env/
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-*.egg-info/
-.installed.cfg
-*.egg
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*,cover
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-target/

MANIFEST

@@ -104,7 +104,7 @@ pyFTS/models/seasonal/common.py
 pyFTS/models/seasonal/msfts.py
 pyFTS/models/seasonal/partitioner.py
 pyFTS/models/seasonal/sfts.py
-pyFTS/partitioners/KMeans.py
+pyFTS/partitioners/CMeans.py
 pyFTS/partitioners/Entropy.py
 pyFTS/partitioners/FCM.py
 pyFTS/partitioners/Grid.py

README.md

@@ -38,7 +38,7 @@ Fuzzy Time Series (FTS) are non parametric methods for time series forecasting b
 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:
  - the number of intervals
  - which fuzzy membership function (on [pyFTS.common.Membership](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/common/Membership.py))
- - 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])
+ - 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])
  
  Check out the jupyter notebook on [notebooks/Partitioners.ipynb](https://github.com/PYFTS/notebooks/blob/master/Partitioners.ipynb) for sample codes.
  

docs/pyFTS.partitioners.rst

@@ -28,10 +28,10 @@ pyFTS.partitioners.Class module
     :undoc-members:
     :show-inheritance:
 
-pyFTS.partitioners.KMeans module
+pyFTS.partitioners.CMeans module
 --------------------------------
 
-.. automodule:: pyFTS.partitioners.KMeans
+.. automodule:: pyFTS.partitioners.CMeans
     :members:
     :undoc-members:
     :show-inheritance:

pyFTS/partitioners/CMeans.py

@@ -14,7 +14,7 @@ def distance(x, y):
     return math.sqrt(tmp)
 
 
-def k_means(k, dados, tam):
+def c_means(k, dados, tam):
     # Инициализирует центроиды, выбирая случайные элементы из множества
     centroides = [dados[rnd.randint(0, len(dados)-1)] for kk in range(0, k)]
 
@@ -77,16 +77,16 @@ def k_means(k, dados, tam):
     return centroides
 
 
-class KMeansPartitioner(partitioner.Partitioner):
+class CMeansPartitioner(partitioner.Partitioner):
     def __init__(self, **kwargs):
-        super(KMeansPartitioner, self).__init__(name="KMeans", **kwargs)
+        super(CMeansPartitioner, self).__init__(name="CMeans", **kwargs)
 
     def build(self, data):
         sets = {}
 
         kwargs = {'type': self.type, 'variable': self.variable}
 
-        centroides = k_means(self.partitions, data, 1)
+        centroides = c_means(self.partitions, data, 1)
         centroides = [v[0] for v in centroides]
         centroides.append(self.max)
         centroides.append(self.min)

pyFTS/tests/cmsfts.py

@@ -11,7 +11,7 @@ from mpl_toolkits.mplot3d import Axes3D
 import datetime
 
 import pandas as pd
-from pyFTS.partitioners import Grid, KMeans, FCM, Entropy
+from pyFTS.partitioners import Grid, CMeans, FCM, Entropy
 from pyFTS.common import FLR, FuzzySet, Membership, Transformations, Util, fts
 from pyFTS import sfts
 from pyFTS.models import msfts

setup.py

@@ -26,7 +26,6 @@ setuptools.setup(
         'Programming Language :: Python :: 3.6',
         'Programming Language :: Python :: 3.8',
         'Programming Language :: Python :: 3.10',
-        'Programming Language :: Python :: 3.11',
         'Intended Audience :: Science/Research',
         'Intended Audience :: Developers',
         'Intended Audience :: Education',