rename KMeans
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@ -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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