diff --git a/balance-scale.arff b/balance-scale.arff
new file mode 100755
index 0000000..c30a673
--- /dev/null
+++ b/balance-scale.arff
@@ -0,0 +1,632 @@
+@relation balance-scale
+@attribute 'f1' real
+@attribute 'f2' real
+@attribute 'f3' real
+@attribute 'f4' real
+@attribute 'clase' {0,1,2}
+@data
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diff --git a/balance-scale.csv b/balance-scale.csv
new file mode 100644
index 0000000..4867e74
--- /dev/null
+++ b/balance-scale.csv
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+1.0,1.0,0.7500003538370086,1.0,1
+1.0,1.0,1.0,0.0,1
+1.0,1.0,1.0,0.24999964616299147,1
+1.0,1.0,1.0,0.5,1
+1.0,1.0,1.0,0.7500003538370086,1
+1.0,1.0,1.0,1.0,0
diff --git a/k.py b/k.py
index 5ef993d..c8bd820 100644
--- a/k.py
+++ b/k.py
@@ -3,10 +3,35 @@ from sklearn.datasets import load_wine
 from mfs import MFS
 from mfs.Metrics import Metrics
 from stree import Stree
+import numpy as np
+from scipy.io import arff
+from sklearn.preprocessing import StandardScaler
 
 mfsc = MFS(discrete=False)
 mfsd = MFS(discrete=True)
-X, y = load_wine(return_X_y=True)
+
+# Xo, y = load_wine(return_X_y=True)
+# X = Xo.copy()
+# scaler = StandardScaler()
+# for c in range(X.shape[1]):
+#     X[:, c] = scaler.fit_transform(X[:, c].reshape(-1, 1)).reshape(-1)
+
+
+# data = np.genfromtxt("balance-scale.csv")
+# X = data[:, -1:]
+# y = data[:, -1]
+
+
+data, meta = arff.loadarff(
+    "/Users/rmontanana/Code/stree_datasets/data/tanveer/balance-scale/balance-scale.arff"
+)
+train = np.array([data["f1"], data["f2"], data["f3"], data["f4"]])
+y = data["clase"]
+X = train.T
+
+
+for c in range(X.shape[1]):
+    print(f"Mean: {np.mean(X[:,c])} Std: {np.std(X[:,c])}")
 m, n = X.shape
 print("* Differential entropy in X")
 for i in range(n):
@@ -39,5 +64,14 @@ clf = Stree(random_state=0)
 print("cfs discreto", clf.fit(X[:, cfs_d], y).score(X[:, cfs_d], y))
 print("cfs continuo", clf.fit(X[:, cfs_f], y).score(X[:, cfs_f], y))
 clf = Stree(random_state=0)
-subf = fcfb_f[:6]
+# subf = fcfb_f[:6]
+subf = fcfb_f
 print("fcfb", clf.fit(X[:, subf], y).score(X[:, subf], y))
+
+for c in range(X.shape[1]):
+    for k in range(X.shape[1]):
+        ac = 0
+        for v in range(X[:, c].shape[0]):
+            if X[v, c] == X[v, k]:
+                ac += 1
+        print(f"{c} {k} {ac}")
diff --git a/mfs/Selection.py b/mfs/Selection.py
index 0733684..539fea2 100755
--- a/mfs/Selection.py
+++ b/mfs/Selection.py
@@ -157,6 +157,9 @@ class MFS:
                     id_selected = idx
                     merit = merit_new
                 candidates.pop()
+            if id_selected is None:
+                # Every merit computed is 0
+                break
             candidates.append(feature_order[id_selected])
             self._scores.append(merit)
             del feature_order[id_selected]