def new_feature(Y, Z, W, tree, ext, K, res, sig, sig_w):
ctree, ptree = tree
Z, W, tree = drop_feature(Z, W, tree)
F, D = get_FD(tree)
if F > 8:
return (Z, W, tree)
else:
if F + ext < K:
more = K - F
else:
more = ext
tree = add(tree, more, res)
Z = draw_feature(Z, tree, res, more)
W = np.vstack((W, np.random.normal(0, sig_w, (more, T))))
#W = sample_W(Y,Z,sig,sig_w)
return (Z, W, tree)
def new_feature(Y, Z, W, tree, ext, K, res, sig, sig_w, drop):
ctree, ptree = tree
#debugging invariant
vec = get_vec(tree)
Z, W, tree = drop_feature(Z, W, tree)
F, D = get_FD(tree)
if F >= 12 or drop:
return (Z, W, tree)
else:
if F + ext < K:
more = K - F
else:
more = ext
tree = add(tree, more, res)
Z = draw_feature(Z, tree, res, more)
W = np.vstack((W, np.random.normal(0, sig_w, (more, T))))
#W = sample_W(Y,Z,sig,sig_w)
return (Z, W, tree)
def add_feature(i,Y,Z,W,tree,vec,prior,sig,sig_w):
N,T = Y.shape
N,K = Z.shape
old = log_data_zw(Y,Z,W,sig)
col = np.zeros((N,1))
col[i,0] = 1
Z_new = np.hstack((Z,col))
W_new = np.vstack((W,np.random.normal(0,sig_w,(1,T))))
#W_new = sample_W(Y,Z_new,sig,sig_w)
new = log_data_zw(Y,Z_new,W_new,sig)
new = new - old
old = 0
roulette = [np.exp(old)*prior[0],np.exp(new)*prior[1]]
normal_roulette = [float(r)/np.sum(roulette) for r in roulette]
chosen = int(np.where(np.random.multinomial(1,normal_roulette) == 1)[0])
if chosen:
Z = Z_new
W = W_new
tree = add(tree,res)
vec = get_vec(tree)
return (Z,W,tree,vec)
