老师给了个csv数据，还有些实例代码
def GradientDescent_sigmoid(x,y):
alpha = 0.002
n=100
x=np.mat(x) # x为m*(d+1)
y=np.mat(y) #y为m*1
w=np.mat([[1],[1],[1]]) #w为(d+1)*1
for i in range(n):
f=sigmoid(x*w)
error=y-f
w=w+alpha*x.T*error
cost=y.T*np.log(f)+(1-y).T*np.log(1-f)
cost=cost/m
if abs(cost-precost)<0.001:
break;
precost=cost
return w
数据：
0.697,0.46,1
0.774,0.376,1
0.634,0.264,1
0.608,0.318,1
0.556,0.215,1
0.403,0.237,1
0.481,0.149,1
0.437,0.211,1
0.666,0.091,0
0.243,0.267,0
0.245,0.057,0
0.343,0.099,0
0.639,0.161,0
0.657,0.198,0
0.360 ,0.370 ,0
0.593,0.042,0
0.719,0.103,0