def adaClassify(datToClass, classifierArr):
dataMatrix = mat(datToClass)
m = shape(dataMatrix)[0]
aggClassEst = mat(zeros((m,1)))
#iterate all classifier, and estimate a value of each classifier using stumpClassify()
for i in range(len(classifierArr)):
classEst = boost.stumpClassify(dataMatrix, classifierArr[i]['dim'], classifierArr[i]['thresh'],
classifierArr[i]['ineq'])
aggClassEst += classifierArr[i]['alpha']*classEst
return sign(aggClassEst)
def adaClassify(datToClass, classifierArr):
dataMatrix = mat(datToClass)
m = shape(dataMatrix)[0]
aggClassEst = mat(zeros((m, 1)))
for i in range(len(classifierArr)):
classEst = stumpClassify(dataMatrix, classifierArr[i]['dim'],
classifierArr[i]['thresh'],
classifierArr[i]['ineq'])
aggClassEst += classifierArr[i]['alpha'] * classEst
print(aggClassEst.T)
return sign(aggClassEst.T)
def adaClassify(datToClass, classifierArr):
'''
classify data with our trained classifiers
'''
dataMatrix = np.mat(datToClass)
m = np.shape(dataMatrix)[0]
aggClassEst = np.mat(np.zeros((m, 1)))
for i in range(len(classifierArr)):
classEst = stumpClassify(dataMatrix, classifierArr[i]['dim'],\
classifierArr[i]['thresh'],\
classifierArr[i]['ineq'])
aggClassEst += classifierArr[i]['alpha'] * classEst
#print(aggClassEst)
return np.sign(aggClassEst)
def adaClassify(dataToClass, classifierArr):
'''
AdaBoost分类函数
input:一个或多个待分类样例、多个弱分类器组成的数组
'''
dataMatrix = np.mat(dataToClass)
m = np.shape(dataMatrix)[0]
aggClassEst = np.mat(np.zeros((m, 1))) #同上
for i in range(len(classifierArr)):
classEst = boost.stumpClassify(dataMatrix,classifierArr[i]['dim'],\
classifierArr[i]['thresh'],\
classifierArr[i]['ineq'])
aggClassEst += classifierArr[i]['alpha'] * classEst
print(aggClassEst)
return np.sign(aggClassEst)
def adaClassify(datToClass, classifierArray):
'''
输入参数:
datToClass:一个或者多个待分类样例
classifierArray:多个弱分类进行分类的函数
'''
dataMatrix = np.mat(datToClass)
m = np.shape(dataMatrix)[0]
#记录每个数据点的类别估计累计值
aggClassEst = np.mat(np.zeros((m, 1)))
#遍历classifierArray中的所有弱分类器
for i in range(len(classifierArray)):
classEst = boost.stumpClassify(dataMatrix, classifierArray[i]['dim'], \
classifierArray[i]['thresh'], \
classifierArray[i]['ineq'])
aggClassEst += classifierArray[i]['alpha'] * classEst
print(aggClassEst)
return np.sign(aggClassEst)
def adaClassify(dataToClass, classifierArr):
"""
根据训练得到的一组弱分类器及其alpha权重，对数据进行分类
:param datToClass: 待分类数据的新数据
:param classifierArr: 弱分类器集合
:return:
"""
dataMatrix = mat(dataToClass)
m = dataMatrix.shape[0]
aggClassEst = mat(zeros((m, 1)))
print("#############Prediction begin......##########")
for i in range(len(classifierArr)):
print("==========%d th classifier predict result:===========" % i)
classEst = boost.stumpClassify(dataMatrix, classifierArr[i]['dim'],
classifierArr[i]['thresh'],
classifierArr[i]['ineq'])
aggClassEst += classEst * classifierArr[i]['alpha']
print(aggClassEst)
print()
return sign(aggClassEst)