如何将一下代码加封装？
import numpy as np
def createDataSet():
dataSet = [[0, 0, 0, 0, 'no'], #数据集
[0, 0, 0, 1, 'no'],
[0, 1, 0, 1, 'yes'],
[0, 1, 1, 0, 'yes'],
[0, 0, 0, 0, 'no'],
[1, 0, 0, 0, 'no'],
[1, 0, 0, 1, 'no'],
[1, 1, 1, 1, 'yes'],
[1, 0, 1, 2, 'yes'],
[1, 0, 1, 2, 'yes'],
[2, 0, 1, 2, 'yes'],
[2, 0, 1, 1, 'yes'],
[2, 1, 0, 1, 'yes'],
[2, 1, 0, 2, 'yes'],
[2, 0, 0, 0, 'no']]
labels = ['年龄', '有工作', '有自己的房子', '信贷情况'] #特征标签
return dataSet, labels #返回数据集和分类属性
# 获取概率模型, 输入feat np.array格式 大小[N,D]
def trainPbmodel_X(feats):
N,D = np.shape(feats)
model = {}
# 对每一维度的特征进行概率统计
for d in range(D):
data = feats[:,d].tolist()
keys = set(data)
N = len(data)
model[d] ={}
for key in keys:
model[d][key] = float(data.count(key)/N)
return model
# datas： list格式 每个元素表示1个特征序列
# labs： list格式 每个元素表示一个标签
def trainPbmodel(datas,labs):
# 定义模型
model = {}
# 获取分类的类别
keys = set(labs)
for key in keys:
# 获得P(Y)
Pbmodel_Y = labs.count(key)/len(labs)
# 收集标签为Y的数据
index = np.where(np.array(labs)==key)[0].tolist()
feats = np.array(datas)[index]
# 获得 P(X|Y)
Pbmodel_X = trainPbmodel_X(feats)
# 模型保存
model[key]={}
model[key]["PY"] = Pbmodel_Y
model[key]["PX"] = Pbmodel_X
return model
# feat : list格式 一条输入特征
# model: 训练的概率模型
# keys ：考察标签的种类
def getPbfromModel(feat,model,keys):
results ={}
eps = 0.00001
for key in keys:
# 获取P(Y)
PY = model.get(key,eps).get("PY")
# 分别获取 P(X|Y)
model_X = model.get(key,eps).get("PX")
list_px=[]
for d in range(len(feat)):
pb = model_X.get(d,eps).get(feat[d],eps)
list_px.append(pb)
result = np.log(PY) + np.sum(np.log(list_px))
results[key]= result
return results
if __name__ == '__main__':
'''实验一 自制贷款数据集'''
# # 获取数据集
# dataSet, labels = createDataSet()
# # 截取数据和标签
# datas = [i[:-1] for i in dataSet]
# labs = [i[-1] for i in dataSet]
# # 获取标签种类
# keys = set(labs)
# # 进行模型训练
# model = trainPbmodel(datas,labs)
# print(model)
# # 根据输入数据获得预测结果
# feat = [0,1,0,1]
# result = getPbfromModel(feat,model,keys)
# print(result)
# # 遍历结果找到概率最大值进行数据
# for key,value in result.items():
# if(value == max(result.values())):
# print("预测结果是",key)
# '''实验二 隐形眼睛数据集'''
# 读取数据文件 截取数据和标签
with open("train-lenses.txt",'r',encoding="utf-8") as f:
lines = f.read().splitlines()
dataSet = [line.split('\t') for line in lines]
datas = [i[:-1] for i in dataSet]
labs = [i[-1] for i in dataSet]
# 获取标签种类
keys = set(labs)
# 进行模型训练
model = trainPbmodel(datas,labs)
print(model)
# 测试
# 读取测试文件
with open("test-lenses.txt",'r',encoding="utf-8") as f:
lines = f.read().splitlines()
# 逐行读取数据并测试
for line in lines:
data = line.split('\t')
lab_true = data[-1]
feat = data[:-1]
result = getPbfromModel(feat,model,keys)
key_out = ""
for key,value in result.items():
if(value == max(result.values())):
key_out=key
print("输入特征：")
print(data)
print(result)
print("预测结果 %s 医生推荐 %s"%(key_out,lab_true))
将代码写成类的方式表示，每个函数定义为类的示例方法。