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new 1.py
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self.n_samples, self.n_features = X.shape
# 计算类别的先验联合概率
Pypa = {}
# 计算联合概率的的条件概率
Pxypa = {}
yset = np.unique(y)
# 第一层是不同的分类
for yi in yset:
Pypa[yi] = {}; Pxypa[yi] = {}
# 第二层是不同的超父属性，如果是连续值则，不能当作超父，离散值当作超父属性
for paIdx in range(self.n_features):
if columnsMark[paIdx] == 1:
continue
Pypa[yi][paIdx] = {}; Pxypa[yi][paIdx] = {}
paset = np.unique(X[:, paIdx])
# 第三层是不同的超父属性的属性值，分离出来对应的Xarr，和yarr
for pai in paset:
yi_pai_idx = np.nonzero((X[:,paIdx]==pai)&(y==yi))#返回不是0的元素的索引值
# if paIdx==2 and pai==1:
# print(yi, '\n', yi_pai_idx)
yarr = y[yi_pai_idx]
## 保存类别的先验联合概率
Pypa[yi][paIdx][pai] = self.__calyproba(yarr, self.n_samples, len(yset), len(paset))
Pxypa[yi][paIdx][pai] = {}
# 第四层是不同的其他特征，若是超父属性则跳过，离散归离散统计，连续归连续统计
for xiIdx in range(self.n_features):
if xiIdx == paIdx:
continue
allxiset = np.unique(X[:, xiIdx])
Xarr = X[yi_pai_idx, xiIdx].flatten()#默认按行降维到一维，只适用数组
if columnsMark[xiIdx] == 0:
## 保存离散特征的条件概率
Pxypa[yi][paIdx][pai][xiIdx] = self.__categorytrain(Xarr, allxiset)
else:
## 保存连续特征的条件概率
Pxypa[yi][paIdx][pai][xiIdx] = self.__continuoustrain(Xarr)
# if xiIdx == 4 and paIdx==2 and pai==1:
# print(Xarr)
print('P(y,pa)训练完毕!')
print('P(x|y,pa)训练完毕!')
self.yProba = Pypa
self.xyProba = Pxypa
self.trainSet = X
self.trainLabel = y
self.columnsMark = columnsMark
return
# 计算离散特征的条件概率
def __categorytrain(self, Xarr, xiset):
pxypa = {}
for xivalue in xiset:
pxypa[xivalue] = {}
pxypa[xivalue]['count'] = sum(Xarr==xivalue) + self.ls
pxypa[xivalue]['ratio'] = self.classifyProba(xivalue, Xarr, len(xiset))
return pxypa
# 计算连续特征的均值和标准差
def __continuoustrain(self, Xarr):
pxypa = (Xarr.mean(), Xarr.std())
return pxypa
# 计算先验联合概率
def __calyproba(self, yarr, ysum, ysetsum, pasetsum):
yproba = {}
yproba['count'] = len(yarr) + self.ls
yproba['ratio'] = (len(yarr) + self.ls) / (ysum + ysetsum * pasetsum)
return yproba
# 预测
def aodepredict(self, X, minSet=0):
n_samples, n_features = X.shape
proba = np.zeros((n_samples, len(self.yProba)))
for i in range(n_samples):
for idx, (yi, Padict) in enumerate(self.yProba.items()):
sumvalue = 0.
for paIdx, Pavaluedict in Padict.items():
subvalue = 1.
pavalue = X[i, paIdx]
Statsdict = Pavaluedict[pavalue]
if Statsdict['count'] <= minSet:
continue
Pypa = Statsdict['ratio']
subvalue *= Pypa
Pxypadict = self.xyProba[yi][paIdx][pavalue]
for xiIdx, xiparams in Pxypadict.items():
xi = X[i, xiIdx]
if isinstance(xiparams, dict):
Pxypa = xiparams[xi]['ratio']
else:
if np.isnan(xiparams[0]) or np.isnan(xiparams[1]):
Pxypa = 1.0e-5
else:
miu = xiparams[0]; sigma = xiparams[1] + 1.0e-5
Pxypa = np.exp(-(xi-miu)**2/(2*sigma**2))/(np.power(2*np.pi, 0.5)*sigma) + 1.0e-5
subvalue *= Pxypa
sumvalue += subvalue
proba[i, idx] = sumvalue
return proba
dataSet = [
['青绿', '蜷缩', '浊响', '清晰', '凹陷', '硬滑', '好瓜'],
['乌黑', '蜷缩', '沉闷', '清晰', '凹陷', '硬滑', '好瓜'],
['乌黑', '蜷缩', '浊响', '清晰', '凹陷', '硬滑', '好瓜'],
['青绿', '蜷缩', '沉闷', '清晰', '凹陷', '硬滑', '好瓜'],
['浅白', '蜷缩', '浊响', '清晰', '凹陷', '硬滑', '好瓜'],
['青绿', '稍蜷', '浊响', '清晰', '稍凹', '软粘', '好瓜'],
['乌黑', '稍蜷', '浊响', '稍糊', '稍凹', '软粘', '好瓜'],
['乌黑', '稍蜷', '浊响', '清晰', '稍凹', '硬滑', '好瓜'],
['乌黑', '稍蜷', '沉闷', '稍糊', '稍凹', '硬滑', '坏瓜'],
['青绿', '硬挺', '清脆', '清晰', '平坦', '软粘', '坏瓜'],
['浅白', '硬挺', '清脆', '模糊', '平坦', '硬滑', '坏瓜'],
['浅白', '蜷缩', '浊响', '模糊', '平坦', '软粘', '坏瓜'],
['青绿', '稍蜷', '浊响', '稍糊', '凹陷', '硬滑', '坏瓜'],
['浅白', '稍蜷', '沉闷', '稍糊', '凹陷', '硬滑', '坏瓜'],
['乌黑', '稍蜷', '浊响', '清晰', '稍凹', '软粘', '坏瓜'],
['浅白', '蜷缩', '浊响', '模糊', '平坦', '硬滑', '坏瓜'],
['青绿', '蜷缩', '沉闷', '稍糊', '稍凹', '硬滑', '坏瓜']
]
#特征值列表
labels = ['色泽', '根蒂', '敲击', '纹理', '脐部', '触感']