def run(F, Phi, Theta, Phi_r=None, Theta_r=None, cfg=config.default_config()):
"""Em-algo method.
- Return:
val
hdist
it
Phi
Theta
status
- Used params:
"""
#F_norm = normalize_cols(F)
T = Theta.shape[0]
eps = cfg['eps']
schedule = cfg['schedule'].split(',')
meas = cfg['measure'].split(',')
val = np.zeros((cfg['max_iter']+2, len(meas)))
hdist = np.zeros((2, cfg['max_iter']+2))#Phi - first row, Theta - second
for i, fun_name in enumerate(meas):
fun = getattr(measure, fun_name)
val[0, i] = fun(F, np.dot(Phi, Theta))
if cfg['compare_real']:
#m = Munkres()
idx = get_permute(Phi_r, Theta_r, Phi, Theta, cfg['munkres'])
hdist[0][0] = hellinger(Phi[:, idx[:, 1]], Phi_r[:, idx[:, 0]])
hdist[1][0] = hellinger(Theta[idx[:, 1],:], Theta_r[idx[:, 0],:])
if cfg['print_lvl'] > 1:
print('Initial loss:', val[0])
status = 0
methods_num = len(schedule)
it = -1
for it in range(cfg['max_iter']+1):
if cfg['print_lvl'] > 1:
print('Iteration', it+1)
####Phi_old = deepcopy(Phi)
####Theta_old = deepcopy(Theta)
method_name = schedule[it % methods_num]
if cfg['print_lvl'] > 1:
print('Method:', method_name)
method = getattr(methods, method_name)
(Phi, Theta) = method(F, Phi, Theta, method_name, cfg)
#jogging of weights
if cfg['jogging'] == 1 and it < 10:
joh_alpha = 0.25
cfg['phi_sparsity'] = 0.05
cfg['theta_sparsity'] = 0.1
Phi_jog, Theta_jog = gen_init(cfg)
Phi = (1-joh_alpha**(it+1))*Phi + joh_alpha**(it+1)*Phi_jog
Theta = (1-joh_alpha**(it+1))*Theta + joh_alpha**(it+1)*Theta_jog
for j, fun_name in enumerate(meas):
fun = getattr(measure, fun_name)
val[it+1, j] = fun(F, np.dot(Phi, Theta))#fun(F_norm, np.dot(Phi, Theta))
if cfg['compare_real']:
idx = get_permute(Phi_r, Theta_r, Phi, Theta, cfg['munkres'])
hdist[0][it+1] = hellinger(Phi[:, idx[:, 1]], Phi_r[:, idx[:, 0]])
hdist[1][it+1] = hellinger(Theta[idx[:, 1], :], Theta_r[idx[:, 0], :])
if cfg['print_lvl'] > 1:
print(val[it+1])
if all(val[it, :] < eps):
if cfg['print_lvl'] > 1:
print('By cost.')
status = 1
break
'''if abs(Phi_old - Phi).max() < eps and abs(Theta_old - Theta).max() < eps:
if cfg['print_lvl'] > 1:
print('By argument.')
status = 2
break'''
#del W_old
#del H_old
if cfg['print_lvl'] > 1:
print('Final:')
#Phi = normalize_cols(Phi)
#Theta = normalize_cols(Theta)
#for j, fun_name in enumerate(meas):
# fun = getattr(measure, fun_name)
# val[it+2:, j] = fun(F, np.dot(Phi, Theta))#fun(F_norm, np.dot(Phi, Theta))
#if cfg['compare_real']:
# idx = get_permute(Phi_r, Theta_r, Phi, Theta, cfg['munkres'])
# hdist[0][it+2:] = hellinger(Phi[:, idx[:, 1]], Phi_r[:, idx[:, 0]])
# hdist[1][it+2:] = hellinger(Theta[idx[:, 1],:], Theta_r[idx[:, 0], :])
return (val, hdist, it, Phi, Theta, status)
def run(V, W, H, W_r=None, H_r=None, cfg=config.default_config()):
T = H.shape[0]
eps = cfg['eps']
schedule = cfg['schedule'].split(',')
meas = cfg['measure'].split(',')
val = np.zeros((cfg['max_iter'] + 2, len(meas)))
hdist = np.zeros((cfg['max_iter'] + 2, 1))
for i, fun_name in enumerate(meas):
fun = getattr(measure, fun_name)
val[0, i] = fun(V, np.dot(W, H))
if cfg['compare_real']:
#m = Munkres()
idx = get_permute(W_r, H_r, W, H, cfg['munkres'])
hdist[0] = hellinger(W[:, idx[:, 1]], W_r[:, idx[:, 0]]) / T
if cfg['print_lvl'] > 1:
print('Initial loss:', val[0])
status = 0
methods_num = len(schedule)
it = -1
for it in range(cfg['max_iter']):
if cfg['print_lvl'] > 1:
print('Iteration', it + 1)
W_old = deepcopy(W)
H_old = deepcopy(H)
method_name = schedule[it % methods_num]
if cfg['print_lvl'] > 1:
print('Method:', method_name)
method = getattr(methods, method_name)
(W, H) = method(V, W, H, method_name, cfg)
if (it + 1) % cfg['normalize_iter'] == 0:
W = normalize_cols(W)
H = normalize_cols(H)
for j, fun_name in enumerate(meas):
fun = getattr(measure, fun_name)
val[it + 1, j] = fun(V, np.dot(W, H))
if cfg['compare_real']:
idx = get_permute(W_r, H_r, W, H, cfg['munkres'])
hdist[it + 1] = hellinger(W[:, idx[:, 1]], W_r[:, idx[:, 0]]) / T
if cfg['print_lvl'] > 1:
print(val[it + 1])
if all(val[it, :] < eps):
if cfg['print_lvl'] > 1:
print('By cost.')
status = 1
break
if abs(W_old - W).max() < eps and abs(H_old - H).max() < eps:
if cfg['print_lvl'] > 1:
print('By argument.')
status = 2
break
#del W_old
#del H_old
if cfg['print_lvl'] > 1:
print('Final:')
W = normalize_cols(W)
H = normalize_cols(H)
for j, fun_name in enumerate(meas):
fun = getattr(measure, fun_name)
val[it + 2:, j] = fun(V, np.dot(W, H))
if cfg['compare_real']:
idx = get_permute(W_r, H_r, W, H, cfg['munkres'])
hdist[it + 2:] = hellinger(W[:, idx[:, 1]], W_r[:, idx[:, 0]]) / T
return (val, hdist, it, W, H, status)
def run(V, W, H, W_r=None, H_r=None, cfg=config.default_config()):
T = H.shape[0]
eps = cfg['eps']
schedule = cfg['schedule'].split(',')
meas = cfg['measure'].split(',')
val = np.zeros((cfg['max_iter']+2, len(meas)))
hdist = np.zeros((cfg['max_iter']+2, 1))
for i, fun_name in enumerate(meas):
fun = getattr(measure, fun_name)
val[0, i] = fun(V, np.dot(W, H))
if cfg['compare_real']:
#m = Munkres()
idx = get_permute(W_r, H_r, W, H, cfg['munkres'])
hdist[0] = hellinger(W[:, idx[:, 1]], W_r[:, idx[:, 0]]) / T
if cfg['print_lvl'] > 1:
print('Initial loss:', val[0])
status = 0
methods_num = len(schedule)
it = -1
for it in range(cfg['max_iter']):
if cfg['print_lvl'] > 1:
print('Iteration', it+1)
W_old = deepcopy(W)
H_old = deepcopy(H)
method_name = schedule[it % methods_num]
if cfg['print_lvl'] > 1:
print('Method:', method_name)
method = getattr(methods, method_name)
(W, H) = method(V, W, H, method_name, cfg)
if (it+1) % cfg['normalize_iter'] == 0:
W = normalize_cols(W)
H = normalize_cols(H)
for j, fun_name in enumerate(meas):
fun = getattr(measure, fun_name)
val[it+1, j] = fun(V, np.dot(W, H))
if cfg['compare_real']:
idx = get_permute(W_r, H_r, W, H, cfg['munkres'])
hdist[it+1] = hellinger(W[:, idx[:, 1]], W_r[:, idx[:, 0]]) / T
if cfg['print_lvl'] > 1:
print(val[it+1])
if all(val[it, :] < eps):
if cfg['print_lvl'] > 1:
print('By cost.')
status = 1
break
if abs(W_old - W).max() < eps and abs(H_old - H).max() < eps:
if cfg['print_lvl'] > 1:
print('By argument.')
status = 2
break
#del W_old
#del H_old
if cfg['print_lvl'] > 1:
print('Final:')
W = normalize_cols(W)
H = normalize_cols(H)
for j, fun_name in enumerate(meas):
fun = getattr(measure, fun_name)
val[it+2:, j] = fun(V, np.dot(W, H))
if cfg['compare_real']:
idx = get_permute(W_r, H_r, W, H, cfg['munkres'])
hdist[it+2:] = hellinger(W[:, idx[:, 1]], W_r[:, idx[:, 0]]) / T
return (val, hdist, it, W, H, status)