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/KMeans.py

@@ -87,9 +87,9 @@ class KMeansPartitioner(partitioner.Partitioner):
         kwargs = {'type': self.type, 'variable': self.variable}
 
         centroides = k_means(self.partitions, data, 1)
+        centroides = [v[0] for v in centroides]
         centroides.append(self.max)
         centroides.append(self.min)
-        centroides = list(set(centroides))
         centroides.sort()
         for c in range(1, len(centroides) - 1):
             _name = self.get_name(c)

pyFTS/partitioners/Util.py

@@ -24,14 +24,13 @@ def plot_sets(sets: dict, titles : list, size=[12, 10], save=False, file=None, a
     
     """
     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():

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):
@@ -46,9 +45,6 @@ class Partitioner(object):
         if kwargs.get('preprocess',True):
 
             data = kwargs.get('data',[None])
-            
-            if 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)