svm--李航统计学习方法
错误提示：Traceback (most recent call last):
File "D:/code/pycharm/dingwei.py", line 400, in <module>
svm.train()
File "D:/code/pycharm/dingwei.py", line 241, in train
if self.isSatisfyKKT(i) is not True:
File "D:/code/pycharm/dingwei.py", line 112, in isSatisfyKKT
if (math.fabs(self.alpha) < self.toler) and (yi * gxi >= 1):
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
错误相关代码：
def isSatisfyKKT(self, i):
gxi =self.calc_gxi(i)
yi = self.trainLabelMat
if (math.fabs(self.alpha) < self.toler) and (yi * gxi >= 1):
return True
elif (math.fabs(self.alpha - self.C) < self.toler) and (yi * gxi <= 1):
return True
elif (self.alpha > -self.toler) and (self.alpha < (self.C + self.toler)) \
and (math.fabs(yi * gxi - 1) < self.toler):
return True
else:
return False
while (iterStep < iter) and (parameterChanged > 0):
#打印当前迭代轮数
print('iter:%d:%d'%( iterStep, iter))
#迭代步数加1
iterStep += 1
#新的一轮将参数改变标志位重新置0
parameterChanged = 0
#大循环遍历所有样本，用于找SMO中第一个变量
for i in range(self.m):
#查看第一个遍历是否满足KKT条件，如果不满足则作为SMO中第一个变量从而进行优化
if self.isSatisfyKKT(i) is not True:
#如果下标为i的α不满足KKT条件，则进行优化
#第一个变量α的下标i已经确定，接下来按照“7.4.2 变量的选择方法”第二步
#选择变量2。由于变量2的选择中涉及到|E1 - E2|，因此先计算E1
E1 = self.calcEi(i)
#选择第2个变量
E2, j = self.getAlphaJ(E1, i)
if __name__ == '__main__':
start = time.time()
# 获取训练集及标签
print('start read transSet')
trainDataList, trainLabelList = loadData('train.txt')
# 获取测试集及标签
print('start read testSet')
testDataList, testLabelList = loadData('test.txt')
#初始化SVM类
print('start init SVM')
svm = SVM(trainDataList[:1000], trainLabelList[:1000], 10, 200, 0.001)
# 开始训练
print('start to train')
svm.train()
# 开始测试
print('start to test')
accuracy = svm.test(testDataList[:100, testLabelList[:100])
print('the accuracy is:%d'%(accuracy * 100), '%')
# 打印时间
print('time span:', time.time() - start)
请问一下该怎么改
报错意思是你将一个数值和多给数值一次性进行比较了，检查检查比较的是不是数组吧
666666666666666666 来学习一下
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