from collections import defaultdict
from sklearn.cross_validation import train_test_split
def load_data():
加载鸢尾花数据
data load_iris()
return data[ data ],data[ target ]
class NBClassifier(object):
def __init__(self):
self.y []#标签集合
self.x []#每个属性的数值集合
self.py defaultdict(float)#标签的概率分布
self.pxy defaultdict(dict)#每个标签下的每个属性的概率分布
self.n 5#分级的级数
def prob(self,element,arr):
计算元素在列表中出现的频率
prob 0.0
for a in arr:
if element a:
prob 1/len(arr)
if prob 0.0:
prob 0.001
return prob
def get_set(self,x,y):
self.y list(set(y))
for i in range(x.shape[1]):
self.x.append(list(set(x[:,i])))#记录下每一列的数值集
def fit(self,x,y):
训练模型
x self.preprocess(x)
self.get_set(x,y)
#1. 获取p(y)
for yi in self.y:
self.py[yi] self.prob(yi,y)
#2. 获取p(x|y)
for yi in self.y:
for i in range(x.shape[1]):
sample x[y yi,i]#标签yi下的样本
#获取该列的概率分布
pxy [self.prob(xi,sample) for xi in self.x[i]]
self.pxy[yi][i] pxy
print( train score ,self.score(x,y))
def predict_one(self,x):
预测单个样本
max_prob 0.0
max_yi self.y[0]
for yi in self.y:
prob_y self.py[yi]
for i in range(len(x)):
prob_x_y self.pxy[yi][i][self.x[i].index(x[i])]#p(xi|y)
prob_y * prob_x_y#计算p(x1|y)p(x2|y)...p(xn|y)p(y)
if prob_y max_prob:
max_prob prob_y
max_yi yi
return max_yi
def predict(self,samples):
预测函数
samples self.preprocess(samples)
y_list []
for m in range(samples.shape[0]):
yi self.predict_one(samples[m,:])
y_list.append(yi)
return np.array(y_list)
def preprocess(self,x):
因为不同特征的数值集大小相差巨大 造成部分概率矩阵变得稀疏 需要进行数据分割
for i in range(x.shape[1]):
x[:,i] self.step(x[:,i],self.n)
return x
def step(self,arr,n):
分为n阶
ma max(arr)
mi min(arr)
for i in range(len(arr)):
for j in range(n):
a mi (ma-mi)*(j/n)
b mi (ma-mi)*((j 1)/n)
if arr[i] a and arr[i] b:
arr[i] j 1
break
return arr
def score(self,x,y):
y_test self.predict(x)
score 0.0
for i in range(len(y)):
if y_test[i] y[i]:
score 1/len(y)
return score
if __name__ __main__ :
x,y load_data()
x_train,x_test,y_train,y_test train_test_split(x,y,test_size 0.5,random_state 100)
clf NBClassifier()
clf.fit(x_train,y_train)
score clf.score(x_test,y_test)
print( test score ,score)
