# Generate bars data
my_data = model.generate_data(params_gt, N // comm.size)
# Configure DataLogger
print_list = ('T', 'Q', 'pi', 'sigma', 'N', 'MAE')
store_list = ('*')
dlog.set_handler(print_list, TextPrinter)
dlog.set_handler(print_list, StoreToTxt, output_path + '/terminal.txt')
dlog.set_handler(store_list, StoreToH5, output_path + '/result.h5')
model_params = model.standard_init(my_data)
if 'anneal' in params:
anneal = params.get('anneal')
else:
# Choose annealing schedule
anneal = LinearAnnealing(50)
anneal['T'] = [(0, 2.), (.7, 1.)]
anneal['Ncut_factor'] = [(0, 0.), (2. / 3, 1.)]
anneal['anneal_prior'] = False
# Create and start EM annealing
em = EM(model=model, anneal=anneal)
em.data = my_data
em.lparams = model_params
em.run()
dlog.close()
pprint("Done")
# Number of datapoints to generate
N = 1000
# Each datapoint is of D = size*size
size = 5
# Dimensionality of the model
H=2*size # number of latents
D=size**2 # dimensionality of observed data
# Approximation parameters for Expectation Truncation
Hprime = 7
gamma = 5
# Import and instantiate a model
from prosper.em.camodels.tsc_et import Ternary_ET
model = Ternary_ET(D, H, Hprime, gamma)
# Ground truth parameters. Only used to generate training data.
params_gt = {
'W' : 10*generate_bars_dict(H),
'pi' : 1.0 / size,
'sigma' : 2.0
}
from prosper.em.annealing import LinearAnnealing
anneal = LinearAnnealing(300)
anneal['T'] = [(0, 2.), (.7, 1.)]
anneal['Ncut_factor'] = [(0,0.),(2./3,1.)]
anneal['anneal_prior'] = False
else:
model = BSC_ET(D, H, Hprime, gamma)
# Configure DataLogger
print_list = ('T', 'L', 'pi', 'sigma')
dlog.set_handler(print_list, TextPrinter) # prints things to terminal
txt_list = ('T', 'L', 'pi', 'sigma')
dlog.set_handler(txt_list, StoreToTxt, output_path +
'/results.txt') # stores things in a txt file
h5_list = ('T', 'L', 'pi', 'sigma', 'W')
dlog.set_handler(h5_list, StoreToH5, output_path +
'/results.h5') # stores things in an h5 file
# Choose annealing schedule
from prosper.em.annealing import LinearAnnealing
anneal = LinearAnnealing(20) # decrease
anneal['T'] = [(0, 5.), (.8, 1.)]
anneal['Ncut_factor'] = [(0, 0.), (0.5, 0.), (0.6, 1.)]
# anneal['Ncut_factor'] = [(0,0.),(0.7,1.)]
# anneal['Ncut_factor'] = [(0,0.),(0.7,1.)]
anneal['W_noise'] = [(0, np.std(ts) / 2.), (0.7, 0.)]
# anneal['pi_noise'] = [(0,0.),(0.2,0.1),(0.7,0.)]
anneal['anneal_prior'] = False
assert train_labels.shape[0] == ts.shape[0]
my_data = {'y': ts, 'l': train_labels}
model_params = model.standard_init(my_data)
pp("model defined")
em = EM(model=model, anneal=anneal)
em.data = my_data
em.lparams = model_params
# Number of datapoints to generate
N = 1000
# Each datapoint is of D = size*size
size = 5
# Dimensionality of the model
H = 2 * size # number of latents
D = size**2 # dimensionality of observed data
# Approximation parameters for Expectation Truncation
Hprime = 7
gamma = 5
# Latent states of dsc
states = np.array([0., 1., 2.])
pi = np.array([0.8, 0.15, 0.05])
# Import and instantiate a model
from prosper.em.camodels.dsc_et import DSC_ET
model = DSC_ET(D, H, Hprime, gamma, states=states)
# Ground truth parameters. Only used to generate training data.
params_gt = {'W': 10 * generate_bars_dict(H), 'pi': pi, 'sigma': 2.0}
from prosper.em.annealing import LinearAnnealing
anneal = LinearAnnealing(100)
anneal['T'] = [(0, 2.), (.7, 1.)]
anneal['Ncut_factor'] = [(0, 0.), (2. / 3, 1.)]
anneal['anneal_prior'] = False
# Number of datapoints to generate
N = 1000
# Each datapoint is of D = size*size
size = 5
# Dimensionality of the model
H = 2 * size # number of latents
D = size**2 # dimensionality of observed data
# Approximation parameters for Expectation Truncation
Hprime = 8
gamma = 5
# Import and instantiate a model
from prosper.em.camodels.bsc_et import BSC_ET
model = BSC_ET(D, H, Hprime, gamma)
# Ground truth parameters. Only used to generate training data.
params_gt = {'W': 10 * generate_bars_dict(H), 'pi': 1.0 / size, 'sigma': 2.0}
# Choose annealing schedule
from prosper.em.annealing import LinearAnnealing
anneal = LinearAnnealing(150)
anneal['T'] = [(0, 5.), (.8, 1.)]
anneal['Ncut_factor'] = [(0, 0.), (2. / 3, 1.)]
anneal['anneal_prior'] = False
else:
model = BSC_ET(D, H, Hprime, gamma)
# Configure DataLogger
print_list = ('T', 'L', 'pi', 'sigma')
dlog.set_handler(print_list, TextPrinter) # prints things to terminal
txt_list = ('T', 'L', 'pi', 'sigma')
dlog.set_handler(txt_list, StoreToTxt, output_path +
'/results.txt') # stores things in a txt file
h5_list = ('T', 'L', 'pi', 'sigma', 'W')
dlog.set_handler(h5_list, StoreToH5, output_path +
'/results.h5') # stores things in an h5 file
# Choose annealing schedule
from prosper.em.annealing import LinearAnnealing
anneal = LinearAnnealing(120) # decrease
anneal['T'] = [(0, 5.), (.8, 1.)]
anneal['Ncut_factor'] = [(0, 0.), (0.5, 0.), (0.6, 1.)]
# anneal['Ncut_factor'] = [(0,0.),(0.7,1.)]
# anneal['Ncut_factor'] = [(0,0.),(0.7,1.)]
anneal['W_noise'] = [(0, np.std(ts) / 2.), (0.7, 0.)]
# anneal['pi_noise'] = [(0,0.),(0.2,0.1),(0.7,0.)]
anneal['anneal_prior'] = False
training_labels = np.array(training_labels)
assert training_labels.shape[0] == ts.shape[0]
my_data = {'y': ts, 'l': training_labels}
model_params = model.standard_init(my_data)
print("model defined")
em = EM(model=model, anneal=anneal)
em.data = my_data
"""
Demonstrate how to use the annealing class to generate
piecewise-linear annealing schedules.
"""
import sys
from prosper.em.annealing import LinearAnnealing
Tsteps = 80
Tstart = 20
Tend = 1.05
# Choose annealing schedule
anneal = LinearAnnealing(Tsteps)
anneal['T'] = [(10, Tstart) , (-10, Tend)]
anneal['param_a'] = [(2/3, 0.) , (-10, 1.)]
anneal['param_b'] = 0.5
assert anneal['param_c'] == 0.0
while not anneal.finished:
print("[%3d] T=%.2f parameter_a=%.2f parameter_b=%.2f" % (anneal['step'], anneal['T'], anneal['param_a'], anneal["param_b"]))
anneal.next()