def bp_update_params_new(args):
lmds, pis = args.lmds, args.pis
vert_parent, vert_children = args.vert_parent, args.vert_children
#print pis[0].shape
I, T, L = args.X.shape
K = args.gamma.shape[0]
theta, alpha, beta, gamma, emit_probs, X = (args.theta, args.alpha, args.beta, args.gamma, args.emit_probs,
args.X)
evidence = args.evidence #evid_allchild(lmds, vert_children)
emit_probs_mat = sp.exp(args.log_obs_mat)
#emit_probs_mat /= emit_probs_mat.max(axis=2).reshape(I, T, 1)
gamma_p = copy.copy(gamma)
alpha_p = copy.copy(alpha)
beta_p = copy.copy(beta)
theta_p = copy.copy(theta)
emit_probs_p = copy.copy(emit_probs)
theta[:] = args.pseudocount
alpha[:] = args.pseudocount
beta[:] = args.pseudocount
gamma[:] = args.pseudocount
emit_probs[:] = args.pseudocount
#evidence = evid_allchild(lmds, args.vert_children)
##support = casual_support(pis)
emit_sum = sp.zeros(K)
for i in xrange(I):
vp = vert_parent[i]
for t in xrange(T):
if i==0 and t==0:
gamma += emit_probs_mat[i, t, :]*evidence[i,t,:]
Q = emit_probs_mat[i, t, :]*evidence[i,t,:]
else:
if i == 0:
tmp1, tmp2 = sp.ix_(pis[i][-1,t-1,:], emit_probs_mat[i, t, :]*evidence[i,t,:])
tmp = alpha_p * (tmp1*tmp2)
#tmp /= tmp.sum()
Q = tmp.sum(axis=0)
alpha += tmp/tmp.sum()
elif t == 0:
tmp1, tmp2 = sp.ix_(pis[vp][i-1,t,:], emit_probs_mat[i, t, :]*evidence[i,t,:])
tmp = beta_p *(tmp1*tmp2)
Q = tmp.sum(axis=0)
beta += tmp/tmp.sum()
else:
tmp1, tmp2, tmp3 = sp.ix_(pis[vp][i-1,t,:], pis[i][-1, t-1,:], emit_probs_mat[i, t, :]*evidence[i,t,:])
tmp = theta_p *(tmp1*tmp2 *tmp3)
Q = (tmp.sum(axis=0)).sum(axis=0)
theta += tmp/tmp.sum()
Q /= Q.sum()
for l in xrange(L):
if X[i,t,l]:
emit_probs[:, l] += Q
emit_sum += Q
normalize_trans(theta, alpha, beta, gamma)
emit_probs[:] = sp.dot(sp.diag(1./emit_sum), emit_probs)
args.emit_sum = emit_sum
make_log_obs_matrix(args)
def independent_update_params(args, renormalize=True):
X = args.X
Q, Q_pairs, theta, alpha, beta, gamma, vert_parent, vert_children, log_obs_mat, pseudocount = (
args.Q, args.Q_pairs, args.theta,
args.alpha, args.beta,
args.gamma, args.vert_parent, args.vert_children, args.log_obs_mat, args.pseudocount)
I, T, K = Q.shape
L = X.shape[2]
if args.continuous_observations:
new_means = np.zeros_like(args.means)
new_variances = np.zeros_like(args.variances)
total_q = np.zeros_like(args.variances)
else:
emit_probs = args.emit_probs
emit_probs[:] = pseudocount
theta[:] = pseudocount
alpha[:] = pseudocount
beta[:] = pseudocount
gamma[:] = pseudocount
for i in xrange(I):
vp = vert_parent[i]
for t in xrange(T):
for k in xrange(K):
if i==0 and t==0:
gamma[k] += Q[i, t, k]
else:
for v in xrange(K):
if t == 0:
beta[v,k] += Q[vp,t,v] * Q[i,t,k]
else:
alpha[v,k] += Q_pairs[i,t,v,k]
if not args.continuous_observations:
for l in xrange(L):
if X[i,t,l]:
emit_probs[k, l] += Q[i, t, k]
if args.continuous_observations:
for i in xrange(I):
for t in xrange(T):
for k in xrange(K):
for l in xrange(L):
new_means[k,l] += Q[i, t, k] * X[i,t,l] # expectation of X wrt Q
total_q[k,l] += Q[i,t,k]
args.means[:] = new_means = new_means / total_q + 1e-50
np.seterr(under='ignore')
for i in xrange(I):
for t in xrange(T):
for k in xrange(K):
for l in xrange(L):
new_variances[k,l] += Q[i, t, k] * (X[i,t,l] - new_means[k,l]) * (X[i,t,l] - new_means[k,l])
np.seterr(under='print')
args.variances[:] = new_variances / total_q # 1 / N_k
args.variances += pseudocount
else:
normalize_emit(Q, emit_probs, pseudocount, args, renormalize)
if renormalize:
theta += theta.max() * (pseudocount * 1e-20)
alpha += alpha.max() * (pseudocount * 1e-20)
beta += beta.max() * (pseudocount * 1e-20)
gamma += gamma.max() * (pseudocount * 1e-20)
normalize_trans(theta, alpha, beta, gamma)
if args.continuous_observations:
make_log_obs_matrix_gaussian(args)
else:
make_log_obs_matrix(args)
def independent_update_params(args, renormalize=True):
X = args.X
Q, Q_pairs, theta, alpha, beta, gamma, vert_parent, vert_children, log_obs_mat, pseudocount = (
args.Q, args.Q_pairs, args.theta, args.alpha, args.beta, args.gamma,
args.vert_parent, args.vert_children, args.log_obs_mat,
args.pseudocount)
I, T, K = Q.shape
L = X.shape[2]
if args.continuous_observations:
new_means = np.zeros_like(args.means)
new_variances = np.zeros_like(args.variances)
total_q = np.zeros_like(args.variances)
else:
emit_probs = args.emit_probs
emit_probs[:] = pseudocount
theta[:] = pseudocount
alpha[:] = pseudocount
beta[:] = pseudocount
gamma[:] = pseudocount
for i in xrange(I):
vp = vert_parent[i]
for t in xrange(T):
for k in xrange(K):
if i == 0 and t == 0:
gamma[k] += Q[i, t, k]
else:
for v in xrange(K):
if t == 0:
beta[v, k] += Q[vp, t, v] * Q[i, t, k]
else:
alpha[v, k] += Q_pairs[i, t, v, k]
if not args.continuous_observations:
for l in xrange(L):
if X[i, t, l]:
emit_probs[k, l] += Q[i, t, k]
if args.continuous_observations:
for i in xrange(I):
for t in xrange(T):
for k in xrange(K):
for l in xrange(L):
new_means[k,
l] += Q[i, t,
k] * X[i, t,
l] # expectation of X wrt Q
total_q[k, l] += Q[i, t, k]
args.means[:] = new_means = new_means / total_q + 1e-50
np.seterr(under='ignore')
for i in xrange(I):
for t in xrange(T):
for k in xrange(K):
for l in xrange(L):
new_variances[k, l] += Q[i, t, k] * (
X[i, t, l] - new_means[k, l]) * (X[i, t, l] -
new_means[k, l])
np.seterr(under='print')
args.variances[:] = new_variances / total_q # 1 / N_k
args.variances += pseudocount
else:
normalize_emit(Q, emit_probs, pseudocount, args, renormalize)
if renormalize:
theta += theta.max() * (pseudocount * 1e-20)
alpha += alpha.max() * (pseudocount * 1e-20)
beta += beta.max() * (pseudocount * 1e-20)
gamma += gamma.max() * (pseudocount * 1e-20)
normalize_trans(theta, alpha, beta, gamma)
if args.continuous_observations:
make_log_obs_matrix_gaussian(args)
else:
make_log_obs_matrix(args)
def bp_update_params_new(args, renormalize=True):
lmds, pis = args.lmds, args.pis
vert_parent, vert_children = args.vert_parent, args.vert_children
#print pis[0].shape
I, T, L = args.X.shape
K = args.gamma.shape[0]
theta, alpha, beta, gamma, emit_probs, X = (args.theta, args.alpha,
args.beta, args.gamma,
args.emit_probs, args.X)
evidence = args.evidence #evid_allchild(lmds, vert_children)
emit_probs_mat = sp.exp(args.log_obs_mat)
#emit_probs_mat /= emit_probs_mat.max(axis=2).reshape(I, T, 1)
gamma_p = copy.copy(gamma)
alpha_p = copy.copy(alpha)
beta_p = copy.copy(beta)
theta_p = copy.copy(theta)
# emit_probs_p = copy.copy(emit_probs)
theta[:] = args.pseudocount
alpha[:] = args.pseudocount
beta[:] = args.pseudocount
gamma[:] = args.pseudocount
emit_probs[:] = args.pseudocount
#evidence = evid_allchild(lmds, args.vert_children)
##support = casual_support(pis)
emit_sum = sp.zeros((K, L))
for i in xrange(I):
vp = vert_parent[i]
if i != 0:
idx_i = vert_children[vp].tolist().index(i)
for t in xrange(T):
if i == 0 and t == 0:
gamma += emit_probs_mat[i, t, :] * evidence[i, t, :]
Q = emit_probs_mat[i, t, :] * evidence[i, t, :]
elif i == 0:
tmp1, tmp2 = sp.ix_(
pis[i][-1, t - 1, :],
emit_probs_mat[i, t, :] * evidence[i, t, :])
tmp = alpha_p * (tmp1 * tmp2) # belief
#tmp /= tmp.sum()
Q = tmp.sum(axis=0)
alpha += tmp / tmp.sum()
elif t == 0:
tmp1, tmp2 = sp.ix_(pis[vp][idx_i,
t, :], emit_probs_mat[i, t, :] *
evidence[i, t, :]) # i-1->0
tmp = beta_p * (tmp1 * tmp2) # belief
Q = tmp.sum(axis=0)
beta += tmp / tmp.sum()
else:
tmp1, tmp2, tmp3 = sp.ix_(
pis[vp][idx_i, t, :], pis[i][-1, t - 1, :],
emit_probs_mat[i, t, :] * evidence[i, t, :])
tmp = theta_p * (tmp1 * tmp2 * tmp3)
Q = (tmp.sum(axis=0)).sum(axis=0)
theta += tmp / tmp.sum()
Q /= Q.sum()
for l in xrange(L):
if args.mark_avail[i, l] and X[i, t, l]:
emit_probs[:, l] += Q
emit_sum[:, l] += Q
if renormalize:
normalize_trans(theta, alpha, beta, gamma)
emit_probs[:] = sp.dot(sp.diag(1. / emit_sum), emit_probs)
args.emit_sum = emit_sum
make_log_obs_matrix(args)
