FCM
Apos 20 iteracoes, os centroides convergiram para:
[[ 0.04511  1.       1.       1.     ]
 [ 0.10135  0.71794  0.71794  1.     ]
 [ 0.08665  0.65422  0.65422  1.     ]
 [ 0.84488  0.98308  1.       0.98505]
 [ 0.09111  0.67752  1.       0.67748]
 [ 0.12139  0.99517  1.       0.99517]
 [ 0.14244  0.99224  1.       0.99224]]

Matriz de confusão:
[[ 112.    0.    0.    0.    0.    0.    0.]
 [   0.   88.    0.    0.    0.    0.    0.]
 [   0.    0.   92.    0.    0.    0.    0.]
 [   0.    0.    0.   92.    0.    0.    0.]
 [   0.    0.    0.    0.   92.    0.    0.]
 [   0.    0.    0.    0.    0.  108.    4.]
 [   0.    0.    0.    0.    0.    0.  116.]]
Exatidão = 99.4318181818%

/usr/local/lib/python2.6/dist-packages/sklearn/neighbors/base.py:23: UserWarning: kneighbors: neighbor k+1 and neighbor k have the same distance: results will be dependent on data order.
  warnings.warn(msg)
/usr/local/lib/python2.6/dist-packages/sklearn/svm/classes.py:184: FutureWarning: SVM: scale_C will be True by default in scikit-learn 0.11
  cache_size, scale_C)

1-NN Decision Boundary

OPF Decision Boundary

SVM Decision Boundary

DT Decision Boundary