def full_info_upperbound(self,x,S):
fx = self.f([x])
marg = fx
if len(S) == 0:
return marg
xi = max(S,key=lambda xi:garea(self.dist(x,xi),fx,self.f([xi])))
return fx - garea(self.dist(x,xi),fx,self.f([xi]))
def upperbound(self,x,S,a):
fx = self.f([x])
marg = fx
if len(S) == 0:
return marg
xi = max(S,key=lambda xi:garea(self.dist(x,xi),fx,a))
return max([fx - garea(self.dist(x,xi),fx,a),0])
def augmented_upperbound(self,x,Xin,Xout,a,Xin_val):
fx_Xin = self.fast_delta(x,Xin,Xin_val)
fx = self.f([x])
marg = fx_Xin
if len(Xout) == 0:
return marg
x_h = max(Xout,key=lambda xi:garea(self.dist(x,xi),fx,a))
return max([fx_Xin - garea(self.dist(x,x_h),fx,a),0])
def full_info_lowerbound(self,x,S):
fx = self.f([x])
marg = fx
for s in S:
d = self.dist(x,s)
marg += - garea(d,fx,self.f([s]))
return marg
def lowerbound(self,x,S,b):
fx = self.f([x])
marg = fx
for s in S:
d = self.dist(x,s)
marg += - garea(d,fx,b)
return max([marg,0])
def augmented_lowerbound(self,x,Xin,Xout,b,Xin_val):
fx_Xin = self.fast_delta(x,Xin,Xin_val)
fx = self.f([x])
marg = fx_Xin
for x_out in Xout:
d = self.dist(x,x_out)
marg += -garea(d,fx,b)
return max([marg,0])