我需要基于粗糙集来实现特征选择的快速归约算法，为此，我使用了癌症乳腺癌数据集，即使代码运行结果为假（与R相比），也会出现一些错误。
import numpy as np
import pandas as pd
#_______________________ File selection box
filename = 'breast10D.csv' # show an "Open" dialog box and return the path to the selected file
#Cfilename ='breast10.csv'
#_______________________ Converting csv file to list
df = pd.read_csv(filename)#,index_col=True)
U = df.values.tolist()
U = [[index] + value for index , value in enumerate(U) ]
#________________________ Equivalence partition function
def equivalence_partition( iterable , index ):
classes = []
dclasses = {}
for o in iterable: # for each object
# find the class it is in
found = False
for c in classes:
indice_ele = next(iter(c))
element = [iterable[indice_ele][ind] == o[ind] for ind in index]
if all(element): # is it equivalent to this class?
c.add( o[0])
dclasses[o[0]] = c
found = True
break
if not found: # it is in a new class
classes.append( set([o[0]]))
dclasses[o[0]] = classes[-1]
return classes,dclasses
#_________________________ Finding lower approximation and positif region
def lower_appr(B):
ind_B = equivalence_partition( U , B )[1]
ind_d = equivalence_partition( U , D )[1]
lower_appr_set = set()
for x,ele in enumerate(U):
if ind_B[x].issubset(ind_d[x]):
lower_appr_set.add(x)
return lower_appr_set
#________________________ Finding dependencey of features
def gamma(B):
return float(len(lower_appr(B)))/float(len(U))
#_________________________ Rough set feature selection quickreduct algorithm
def qreduct(C):
R = set()
while True:
T = R
for x in C-R:
if gamma(R.union(set([x]))) > gamma(T):
T = R.union(set([x]))
R = T
if gamma(R) == gamma(C):
break
return R
#_________________________ Main fuction
decision=len(df.columns)#_________ defining le decision index
D = [decision]
B = set([ i for i in range(1,decision)]) #__________ defining condition index
Features= qreduct(B)
有人有建议吗？