def cfr(X,y,k):
"""
CFR
"""
t1=[]
n_samples, n_features = X.shape
for i in range(n_features): #MI
f = X[:, i]
t1.append(ees.midd(f, y))
F=[]
tt =np.zeros(n_features)
while len(F)<k:
if len(F)==0:
index=t1.index(max(t1))
F.append(index)
f_select = X[:, index]
j_mim = -1000000000000
for i in xrange(n_features):
if i not in F:
fi=X[:, i]
cmi=ees.cmidd(y,fi ,f_select)
inter =t1[i]-cmi
tt[i] +=cmi-inter
t=tt[i]
if t> j_mim:
j_mim = t
idx = i
F.append(idx)
f_select = X[:, idx]
return F
def conditional_entropy(f1, f2):
"""
This function calculates the conditional entropy, where ce = H(f1) - I(f1;f2)
Input
-----
f1: {numpy array}, shape (n_samples,)
f2: {numpy array}, shape (n_samples,)
Output
------
ce: {float}
ce is conditional entropy of f1 and f2
"""
ce = ee.entropyd(f1) - ee.midd(f1, f2)
return ce
def conditional_entropy(f1, f2):
"""
This function calculates the conditional entropy, where ce = H(f1) - I(f1;f2)
Input
-----
f1: {numpy array}, shape (n_samples,)
f2: {numpy array}, shape (n_samples,)
Output
------
ce: {float}
ce is conditional entropy of f1 and f2
"""
ce = ee.entropyd(f1) - ee.midd(f1, f2)
return ce