def do_baseline_runs(expt):
gen = DataGenerator(expt.num_phonemes, expt.num_features,
expt.var_diag_interval, expt.var_offdiag_interval)
all_results = []
for run_idx in range(expt.num_runs):
test_data = gen.generate_simulated_data(expt.num_test_frames)
# There's a problem here if there's only one data point, since
# then we end up with a variance of 0. We currently hack
# around this problem by guaranteeing more than one point. We
# could change the models to allow zero variance but this will
# mean not being able to make samples from the models without
# some extra work. Note that we don't care at all about order
# of training data in these experiments, so we just build our
# training data in two parts and cat them together. If you
# hit either of these asserts, you're asking for an error rate
# that's too hig and/or a training data size that's too low.
# We need two correct samples per phoneme.
num_secondary_frames = expt.num_training_frames - expt.num_phonemes * 2
num_errorful_frames = expt.num_training_frames * expt.training_error_rate
assert expt.num_training_frames >= expt.num_phonemes * 2
assert num_secondary_frames > num_errorful_frames
errorless_training_data = gen.generate_simulated_data_per_phoneme(2)
secondary_training_data = gen.generate_simulated_data(
num_secondary_frames)
# Slight trickiness to get a correct error rate for this subset of the data
subset_error_rate = float(num_errorful_frames) / num_secondary_frames
errorful_training_data, num_errors = gen.add_errors_to_data(
secondary_training_data, subset_error_rate)
practice_data = gen.generate_simulated_data(expt.num_practice_frames)
errorful_practice_data, num_errors = gen.add_errors_to_data(
practice_data, expt.practice_error_rate)
training_data = errorless_training_data + errorful_training_data + errorful_practice_data
c = SimpleClassifier(gen.get_labels(), gen.num_features)
c.train_all(training_data)
(rate, results) = measureAccuracy(c, test_data)
name = "Baseline 0.%d" % (run_idx, )
summary = make_summary_string(name, rate, results, c, test_data, gen)
all_results.append((name, rate))
# print "Classifier:\n"
# print c.to_string()
# print summary
print "\n--------------------------Summary-----------------------"
print make_all_runs_summary_string(expt, all_results)
def do_baseline_runs(expt):
gen = DataGenerator(expt.num_phonemes, expt.num_features,
expt.var_diag_interval, expt.var_offdiag_interval)
all_results = []
for run_idx in range(expt.num_runs):
test_data = gen.generate_simulated_data(expt.num_test_frames)
# There's a problem here if there's only one data point, since
# then we end up with a variance of 0. We currently hack
# around this problem by guaranteeing more than one point. We
# could change the models to allow zero variance but this will
# mean not being able to make samples from the models without
# some extra work. Note that we don't care at all about order
# of training data in these experiments, so we just build our
# training data in two parts and cat them together. If you
# hit either of these asserts, you're asking for an error rate
# that's too hig and/or a training data size that's too low.
# We need two correct samples per phoneme.
num_secondary_frames = expt.num_training_frames - expt.num_phonemes * 2
num_errorful_frames = expt.num_training_frames * expt.training_error_rate
assert expt.num_training_frames >= expt.num_phonemes * 2
assert num_secondary_frames > num_errorful_frames
errorless_training_data = gen.generate_simulated_data_per_phoneme(2)
secondary_training_data = gen.generate_simulated_data(num_secondary_frames)
# Slight trickiness to get a correct error rate for this subset of the data
subset_error_rate = float(num_errorful_frames) / num_secondary_frames
errorful_training_data, num_errors = gen.add_errors_to_data(secondary_training_data, subset_error_rate)
practice_data = gen.generate_simulated_data(expt.num_practice_frames)
errorful_practice_data, num_errors = gen.add_errors_to_data(practice_data, expt.practice_error_rate)
training_data = errorless_training_data + errorful_training_data + errorful_practice_data
c = SimpleClassifier(gen.get_labels(), gen.num_features)
c.train_all(training_data)
(rate, results) = measureAccuracy(c, test_data)
name = "Baseline 0.%d" % (run_idx,)
summary = make_summary_string(name, rate, results, c, test_data, gen)
all_results.append((name, rate))
# print "Classifier:\n"
# print c.to_string()
# print summary
print "\n--------------------------Summary-----------------------"
print make_all_runs_summary_string(expt, all_results)
