def rd_fr_classify(tran_data, test_data):
forests = random_fr(tran_data)
res_clses = []
cls = []
for tree in forests:
res_clses.append(tree.classify(test_data))
clses_T = map(list, zip(*res_clses))
for c in clses_T:
vote_cls = collections.Counter(c).most_common(1)[0][0]
cls.append(vote_cls)
accurcy = check_accurcy(test_data, cls)
return accurcy
if __name__ == '__main__':
#dataset = read_data("breast-cancer-assignment5.txt")
dataset = read_data("german-assignment5.txt")
DiscType = get_disc_val(dataset)
attrset = range(len(dataset[0]))
#forests = random_fr(dataset)
#accurcy = rd_fr_classify(dataset, dataset[1:])
#print accurcy
print fcv(dataset, rd_fr_classify)
if 1.0 - sum(data_wh[:-1]) < 0:
print "less than 0-------------------------", 1-sum(data_wh[:-1])
data_wh[-1] = 1.0 - sum(data_wh[:-1]) #确保权重之和为1
'''
def get_pre_res(dataset, res_cls):
'''
根据分类结果以及训练集本身的标签，对正确以及错误分类进行统计
'''
pre_statis = []
for d, cls in zip(dataset, res_cls):
if d[-1] == cls:
pre_statis.append(1)
else:
pre_statis.append(0)
return pre_statis
if __name__ == '__main__':
#datasets = read_data("german-assignment5.txt")
datasets = read_data("breast-cancer-assignment5.txt")
#datasets = read_data("test.txt")
DiscType = get_disc_val(datasets)
AttrSet = range(len(datasets[0]))
#print ada_classify(datasets[1:255], datasets[255:])
#print ada_classify(datasets[1:10], datasets[10:])
print fcv(datasets, ada_classify)
'''
if 1.0 - sum(data_wh[:-1]) < 0:
print "less than 0-------------------------", 1-sum(data_wh[:-1])
data_wh[-1] = 1.0 - sum(data_wh[:-1]) #确保权重之和为1
'''
def get_pre_res(dataset, res_cls):
'''
根据分类结果以及训练集本身的标签，对正确以及错误分类进行统计
'''
pre_statis = []
for d, cls in zip(dataset, res_cls):
if d[-1] == cls:
pre_statis.append(1)
else:
pre_statis.append(0)
return pre_statis
if __name__ == '__main__':
#datasets = read_data("german-assignment5.txt")
datasets = read_data("breast-cancer-assignment5.txt")
#datasets = read_data("test.txt")
DiscType = get_disc_val(datasets)
AttrSet = range(len(datasets[0]))
#print ada_classify(datasets[1:255], datasets[255:])
#print ada_classify(datasets[1:10], datasets[10:])
print fcv(datasets, ada_classify)
return forests
def rd_fr_classify(tran_data, test_data):
forests = random_fr(tran_data)
res_clses = []
cls = []
for tree in forests:
res_clses.append(tree.classify(test_data))
clses_T = map(list, zip(*res_clses))
for c in clses_T:
vote_cls = collections.Counter(c).most_common(1)[0][0]
cls.append(vote_cls)
accurcy = check_accurcy(test_data, cls)
return accurcy
if __name__ == '__main__':
#dataset = read_data("breast-cancer-assignment5.txt")
dataset = read_data("german-assignment5.txt")
DiscType = get_disc_val(dataset)
attrset = range(len(dataset[0]))
#forests = random_fr(dataset)
#accurcy = rd_fr_classify(dataset, dataset[1:])
#print accurcy
print fcv(dataset, rd_fr_classify)
