rename KMeans

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/CMeans.py
+pyFTS/partitioners/KMeans.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), [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])
+ - 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])
  
  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.CMeans module
+pyFTS.partitioners.KMeans module
 --------------------------------
 
-.. automodule:: pyFTS.partitioners.CMeans
+.. automodule:: pyFTS.partitioners.KMeans
     :members:
     :undoc-members:
     :show-inheritance:

pyFTS/partitioners/KMeans.py

@@ -14,7 +14,7 @@ def distance(x, y):
     return math.sqrt(tmp)
 
 
-def c_means(k, dados, tam):
+def k_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 c_means(k, dados, tam):
     return centroides
 
 
-class CMeansPartitioner(partitioner.Partitioner):
+class KMeansPartitioner(partitioner.Partitioner):
     def __init__(self, **kwargs):
-        super(CMeansPartitioner, self).__init__(name="CMeans", **kwargs)
+        super(KMeansPartitioner, self).__init__(name="KMeans", **kwargs)
 
     def build(self, data):
         sets = {}
 
         kwargs = {'type': self.type, 'variable': self.variable}
 
-        centroides = c_means(self.partitions, data, 1)
+        centroides = k_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, CMeans, FCM, Entropy
+from pyFTS.partitioners import Grid, KMeans, FCM, Entropy
 from pyFTS.common import FLR, FuzzySet, Membership, Transformations, Util, fts
 from pyFTS import sfts
 from pyFTS.models import msfts