update and complete sample code

sample.py

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+import warnings
+from mfs import MFS
+from mfs.Metrics import Metrics
+from stree import Stree
+import numpy as np
+from scipy.io import arff
+
+mfsc = MFS(discrete=False)
+
+filename = "conn-bench-sonar-mines-rocks.arff"
+data, meta = arff.loadarff(filename)
+train = np.array([data[i] for i in meta])
+X = train.T
+X = X[:, :-1].astype("float64")
+y = data["clase"]
+
+m, n = X.shape
+print("* Differential entropy in X")
+for i in range(n):
+    print(i, Metrics.differential_entropy(X[:, i], k=10))
+print("* Information Gain")
+print("- Continuous features")
+print(Metrics.information_gain_cont(X, y))
+for i in range(n):
+    print(i, Metrics.information_gain_cont(X[:, i], y))
+# Classification
+warnings.filterwarnings("ignore")
+print("CFS")
+cfs_f = mfsc.cfs(X, y).get_results()
+print(cfs_f)
+print("FCBF")
+fcfb_f = mfsc.fcbf(X, y, 5e-2).get_results()
+print(fcfb_f, len(fcfb_f))
+print("X.shape=", X.shape)
+clf = Stree(random_state=0)
+print("Accuracy whole dataset", clf.fit(X, y).score(X, y))
+clf = Stree(random_state=0)
+print("Accuracy cfs", clf.fit(X[:, cfs_f], y).score(X[:, cfs_f], y))
+clf = Stree(random_state=0)
+subf = fcfb_f
+print("Accuracy fcfb", clf.fit(X[:, subf], y).score(X[:, subf], y))