Update wmvfts.py

.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
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-# Sphinx documentation
-docs/_build/
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-# 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/common/transformations/trend.py

@@ -1,5 +1,5 @@
 from pyFTS.common.transformations.transformation import Transformation 
-# from pandas import datetime
+from pandas import datetime
 from sklearn.linear_model import LinearRegression
 import numpy as np
 import pandas as pd

pyFTS/models/multivariate/cmvfts.py

@@ -146,7 +146,7 @@ class ClusteredMVFTS(mvfts.MVFTS):
 
             new_data_point[self.target_variable.data_label] = tmp.expected_value()
 
-            sample = pd.concat([sample, pd.DataFrame([new_data_point])], ignore_index=True)
+            sample = sample.append(new_data_point, ignore_index=True)
 
         return ret[-steps:]
 
@@ -199,7 +199,7 @@ class ClusteredMVFTS(mvfts.MVFTS):
         for k in np.arange(0, steps):
             sample = ret.iloc[k:self.order+k]
             tmp = self.forecast_multivariate(sample, **kwargs)
-            ret = pd.concat([ret, pd.DataFrame([tmp])], ignore_index=True)
+            ret = ret.append(tmp, ignore_index=True)
 
         return ret
 

pyFTS/models/multivariate/mvfts.py

@@ -211,7 +211,7 @@ class MVFTS(fts.FTS):
 
             new_data_point[self.target_variable.data_label] = tmp
 
-            ndata = pd.concat([ndata, pd.DataFrame([new_data_point])], ignore_index=True)
+            ndata = ndata.append(new_data_point, ignore_index=True)
 
         return ret[-steps:]
 
@@ -307,8 +307,8 @@ class MVFTS(fts.FTS):
             new_data_point_lo[self.target_variable.data_label] = min(tmp_lo)
             new_data_point_up[self.target_variable.data_label] = max(tmp_up)
 
-            lo = pd.concat([lo, pd.DataFrame([new_data_point_lo])], ignore_index=True)
-            up = pd.concat([up, pd.DataFrame([new_data_point_up])], ignore_index=True)
+            lo = lo.append(new_data_point_lo, ignore_index=True)
+            up = up.append(new_data_point_up, ignore_index=True)
 
         return ret[-steps:]
 

pyFTS/partitioners/CMeans.py

@@ -14,15 +14,15 @@ def distance(x, y):
     return math.sqrt(tmp)
 
 
-def k_means(k, dados, tam):
-    # Инициализирует центроиды, выбирая случайные элементы из множества
+def c_means(k, dados, tam):
+    # Inicializa as centróides escolhendo elementos aleatórios dos conjuntos
     centroides = [dados[rnd.randint(0, len(dados)-1)] for kk in range(0, k)]
 
     grupos = [-1 for x in range(0, len(dados))]
 
     it_semmodificacao = 0
 
-    # для каждого экземпляра
+    # para cada instância
     iteracoes = 0
     while iteracoes < 1000 and it_semmodificacao < 10:
         inst_count = 0
@@ -31,7 +31,7 @@ def k_means(k, dados, tam):
 
         for instancia in dados:
 
-            # проверяет расстояние до каждого центроида
+            # verifica a distância para cada centroide
             grupo_count = 0
             dist = 10000
 
@@ -41,7 +41,7 @@ def k_means(k, dados, tam):
                 tmp = distance(instancia, grupo)
                 if tmp < dist:
                     dist = tmp
-                    # ассоциирует центроид с наименьшим расстоянием до экземпляра
+                    # associa a a centroide de menor distância à instância
                     grupos[inst_count] = grupo_count
                 grupo_count = grupo_count + 1
 
@@ -55,7 +55,7 @@ def k_means(k, dados, tam):
         else:
             it_semmodificacao = 0
 
-        # обновляет каждый центроид на основе средних значений всех связанных с ним экземпляров
+        # atualiza cada centroide com base nos valores médios de todas as instâncias à ela associadas
         grupo_count = 0
         for grupo in centroides:
             total_inst = functools.reduce(operator.add, [1 for xx in grupos if xx == grupo_count], 0)
@@ -77,21 +77,21 @@ 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.append(self.max)
         centroides.append(self.min)
         centroides = list(set(centroides))
         centroides.sort()
-        for c in range(1, len(centroides) - 1):
+        for c in np.arange(1, len(centroides) - 1):
             _name = self.get_name(c)
             sets[_name] = FuzzySet.FuzzySet(_name, Membership.trimf,
                                  [round(centroides[c - 1], 3), round(centroides[c], 3), round(centroides[c + 1], 3)],

pyFTS/partitioners/FCM.py

@@ -36,7 +36,7 @@ def fuzzy_cmeans(k, data, size, m, deltadist=0.001):
     centroids = [data[rnd.randint(0, data_length - 1)] for kk in range(0, k)]
 
     # Membership table
-    membership_table = np.zeros((data_length, k))
+    membership_table = np.zeros((k, data_length)) #[[0 for kk in range(0, k)] for xx in range(0, data_length)]
 
     mean_change = 1000
 
@@ -50,12 +50,12 @@ def fuzzy_cmeans(k, data, size, m, deltadist=0.001):
         inst_count = 0
         for instance in data:
 
-            dist_groups = np.zeros(k)
+            dist_groups = np.zeros(k) #[0 for xx in range(0, k)]
 
             for group_count, group in enumerate(centroids):
                 dist_groups[group_count] = fuzzy_distance(group, instance)
 
-            # dist_groups_total = functools.reduce(operator.add, [xk for xk in dist_groups])
+            dist_groups_total = functools.reduce(operator.add, [xk for xk in dist_groups])
 
             for grp in range(0, k):
                 if dist_groups[grp] == 0:

pyFTS/partitioners/Huarng.py

@@ -19,13 +19,13 @@ class HuarngPartitioner(partitioner.Partitioner):
     def build(self, data):
         diff = Transformations.Differential(1)
         data2 = diff.apply(data)
-        divs = np.abs( np.mean(data2) / 2 )
+        davg = np.abs( np.mean(data2) / 2 )
 
-        if divs <= 1.0:
+        if davg <= 1.0:
             base = 0.1
-        elif 1 < divs <= 10:
+        elif 1 < davg <= 10:
             base = 1.0
-        elif 10 < divs <= 100:
+        elif 10 < davg <= 100:
             base = 10
         else:
             base = 100

pyFTS/partitioners/Util.py

@@ -18,20 +18,19 @@ all_methods = [Grid.GridPartitioner, Entropy.EntropyPartitioner, FCM.FCMPartitio
 mfs = [Membership.trimf, Membership.gaussmf, Membership.trapmf]
 
 
-def plot_sets(sets: dict, titles : list, size=[12, 10], save=False, file=None, axis=None):
+def plot_sets(data, sets: dict, titles : list, size=[12, 10], save=False, file=None, axis=None):
     """
     Plot all fuzzy sets in a Partitioner
     
     """
     num = len(sets)
-    num_cols_plot = 1
 
     if axis is None:
-        fig, axes = plt.subplots(nrows=num, ncols=num_cols_plot, figsize=size, squeeze=False)
-    for k in range(num):
+        fig, axes = plt.subplots(nrows=num, ncols=1,figsize=size)
+    for k in np.arange(0,num):
         ticks = []
         x = []
-        ax = axes[k, num_cols_plot-1] if axis is None else axis
+        ax = axes[k] if axis is None else axis
         ax.set_title(titles[k])
         ax.set_ylim([0, 1.1])
         for key in sets[k].keys():
@@ -55,7 +54,7 @@ def plot_sets(sets: dict, titles : list, size=[12, 10], save=False, file=None, a
         Util.show_and_save_image(fig, file, save)
 
 
-def plot_partitioners(objs, tam=[12, 10], save=False, file=None, axis=None):
+def plot_partitioners(data, objs, tam=[12, 10], save=False, file=None, axis=None):
     sets = [k.sets for k in objs]
     titles = [k.name for k in objs]
     plot_sets(sets, titles, tam, save, file, axis)

pyFTS/partitioners/partitioner.py

@@ -1,7 +1,6 @@
 from pyFTS.common import FuzzySet, Membership
 import numpy as np
 from scipy.spatial import KDTree
-import warnings
 
 
 class Partitioner(object):
@@ -47,9 +46,6 @@ class Partitioner(object):
 
             data = kwargs.get('data',[None])
 
-            if isinstance(data, np.ndarray) and len(data.shape) > 1:
-                warnings.warn(f"An ndarray of dimension greater than 1 is used. shape.len(): {len(data.shape)}")
-
             if self.indexer is not None:
                 ndata = self.indexer.get_data(data)
             else:

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',