def T_givenq(p):
_,H_ = getH(q,V)
out = eigen(H_.data)
lam = out[0]
Q = out[1]
temp = softabs_map(lam,alpha)
inv_exp_H = torch.mm(torch.mm(Q,torch.diag(1/temp)),torch.t(Q))
o = 0.5 * torch.dot(p.data,torch.mv(inv_exp_H,p.data))
temp2 = 0.5 * torch.log((temp)).sum()
return(o + temp2)
def T_givenq(p):
_, H_ = getH(q, V)
#debug_dict.update({"explicit":_.data.clone()})
out = eigen(H_.data)
lam = out[0]
Q = out[1]
temp = softabs_map(lam, alpha)
#print("explicit p {}".format(q.data))
inv_exp_H = torch.mm(torch.mm(Q, torch.diag(1 / temp)), torch.t(Q))
o = 0.5 * torch.dot(p.data, torch.mv(inv_exp_H, p.data))
temp2 = 0.5 * torch.log((temp)).sum()
print("explicit tau {}".format(o))
print("explicit logdetmetric {}".format(temp2))
return (o + temp2)
def generate_momentum(q):
# if lam == None or Q == None:
# H_ = self.linkedV.getH_tensor()
# lam, Q = eigen(H_)
_, H_ = getH(q, V)
lam, Q = eigen(H_.data)
# print(lam)
# print(Q)
#exit()
# print(lam.shape)
# print(type(lam))
# print(type(lam[0]))
# exit()
#print(lam)
#exit()
temp = torch.mm(Q, torch.diag(torch.sqrt(softabs_map(lam, alpha))))
out = temp.mv(torch.randn(len(lam)))
# print(temp)
# exit()
return (out)
temp = softabs_map(lam,alpha)
inv_exp_H = torch.mm(torch.mm(Q,torch.diag(1/temp)),torch.t(Q))
o = 0.5 * torch.dot(p.data,torch.mv(inv_exp_H,p.data))
temp2 = 0.5 * torch.log((temp)).sum()
return(o + temp2)
return(T_givenq)
def H(q,p,alpha):
# returns float
return(V(q).data[0] + T(q,alpha)(p))
store = torch.zeros((chain_l,dim))
g,H_ = getH(q,V)
lam,Q = eigen(H_.data)
begin = time.time()
for i in range(chain_l):
print("round {}".format(i))
out = rmhmc_step(q,H,0.1,10,alp,0.1,V)
store[i,]=out[0].data
q.data = out[0].data
totalt = time.time() - begin
store = store[burn_in:,]
store = store.numpy()
empCov = np.cov(store,rowvar=False)
emmean = np.mean(store,axis=0)
print("length of chain is {}".format(chain_l))
def p_sharp(q, p):
_, H = getH(q, V)
lam, Q = eigen(H.data)
p_s = dtaudp(p.data, alp, lam, Q)
return (p_s)
def generate(q):
lam, Q = eigen(getH(q, V).data)
temp = torch.mm(Q, torch.diag(torch.sqrt(softabs_map(lam, alpha))))
out = torch.mv(temp, torch.randn(len(lam)))
return (out)
def p_sharp(q, p):
lam, Q = eigen(getH(q, V).data)
p_s = dtaudp(p.data, alp, lam, Q)
return (p_s)
