Select implementaion of diff entropy and mi

k.py

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+import warnings
+from sklearn.datasets import load_wine
+from mfs import MFS
+from mfs.Metrics import Metrics
+from stree import Stree
+
+mfsc = MFS(discrete=False)
+mfsd = MFS(discrete=True)
+X, y = load_wine(return_X_y=True)
+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("- Discrete features")
+print(Metrics.information_gain(X, y))
+for i in range(n):
+    print(i, Metrics.information_gain(X[:, i], y))
+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 Discrete")
+cfs_d = mfsd.cfs(X, y).get_results()
+print(cfs_d)
+print("CFS continuous")
+cfs_f = mfsc.cfs(X, y).get_results()
+print(cfs_f)
+print("FCBF Discrete")
+print(mfsd.fcbf(X, y, 5e-2).get_results())
+print("FCBF continuous")
+fcfb_f = mfsc.fcbf(X, y, 5e-2).get_results()
+print(fcfb_f, len(fcfb_f), "X.shape=", X.shape)
+clf = Stree(random_state=0)
+print("completo", clf.fit(X, y).score(X, y))
+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]
+print("fcfb", clf.fit(X[:, subf], y).score(X[:, subf], y))