def _run_top_fisher():
"""Run Linear Kernel with {Top,Fisher}Features."""
# put some constantness into randomness
Math_init_random(dataop.INIT_RANDOM)
data = dataop.get_cubes(4, 8)
prefix = "topfk_"
params = {
prefix + "N": 3,
prefix + "M": 6,
prefix + "pseudo": 1e-1,
prefix + "order": 1,
prefix + "gap": 0,
prefix + "reverse": False,
prefix + "alphabet": "CUBE",
prefix + "feature_class": "string_complex",
prefix + "feature_type": "Word",
prefix + "data_train": numpy.matrix(data["train"]),
prefix + "data_test": numpy.matrix(data["test"]),
}
wordfeats = featop.get_features(
params[prefix + "feature_class"],
params[prefix + "feature_type"],
data,
eval(params[prefix + "alphabet"]),
params[prefix + "order"],
params[prefix + "gap"],
params[prefix + "reverse"],
)
pos_train = HMM(wordfeats["train"], params[prefix + "N"], params[prefix + "M"], params[prefix + "pseudo"])
pos_train.train()
pos_train.baum_welch_viterbi_train(BW_NORMAL)
neg_train = HMM(wordfeats["train"], params[prefix + "N"], params[prefix + "M"], params[prefix + "pseudo"])
neg_train.train()
neg_train.baum_welch_viterbi_train(BW_NORMAL)
pos_test = HMM(pos_train)
pos_test.set_observations(wordfeats["test"])
neg_test = HMM(neg_train)
neg_test.set_observations(wordfeats["test"])
feats = {}
feats["train"] = TOPFeatures(10, pos_train, neg_train, False, False)
feats["test"] = TOPFeatures(10, pos_test, neg_test, False, False)
params[prefix + "name"] = "TOP"
_compute_top_fisher(feats, params)
feats["train"] = FKFeatures(10, pos_train, neg_train)
feats["train"].set_opt_a(-1) # estimate prior
feats["test"] = FKFeatures(10, pos_test, neg_test)
feats["test"].set_a(feats["train"].get_a()) # use prior from training data
params[prefix + "name"] = "FK"
_compute_top_fisher(feats, params)
def _run_top_fisher():
"""Run Linear Kernel with {Top,Fisher}Features."""
# put some constantness into randomness
Math_init_random(dataop.INIT_RANDOM)
data = dataop.get_cubes(4, 8)
prefix = 'topfk_'
params = {
prefix + 'N': 3,
prefix + 'M': 6,
prefix + 'pseudo': 1e-1,
prefix + 'order': 1,
prefix + 'gap': 0,
prefix + 'reverse': False,
prefix + 'alphabet': 'CUBE',
prefix + 'feature_class': 'string_complex',
prefix + 'feature_type': 'Word',
prefix + 'data_train': numpy.matrix(data['train']),
prefix + 'data_test': numpy.matrix(data['test'])
}
wordfeats = featop.get_features(params[prefix + 'feature_class'],
params[prefix + 'feature_type'], data,
eval(params[prefix + 'alphabet']),
params[prefix + 'order'],
params[prefix + 'gap'],
params[prefix + 'reverse'])
pos_train = HMM(wordfeats['train'], params[prefix + 'N'],
params[prefix + 'M'], params[prefix + 'pseudo'])
pos_train.train()
pos_train.baum_welch_viterbi_train(BW_NORMAL)
neg_train = HMM(wordfeats['train'], params[prefix + 'N'],
params[prefix + 'M'], params[prefix + 'pseudo'])
neg_train.train()
neg_train.baum_welch_viterbi_train(BW_NORMAL)
pos_test = HMM(pos_train)
pos_test.set_observations(wordfeats['test'])
neg_test = HMM(neg_train)
neg_test.set_observations(wordfeats['test'])
feats = {}
feats['train'] = TOPFeatures(10, pos_train, neg_train, False, False)
feats['test'] = TOPFeatures(10, pos_test, neg_test, False, False)
params[prefix + 'name'] = 'TOP'
_compute_top_fisher(feats, params)
feats['train'] = FKFeatures(10, pos_train, neg_train)
feats['train'].set_opt_a(-1) #estimate prior
feats['test'] = FKFeatures(10, pos_test, neg_test)
feats['test'].set_a(feats['train'].get_a()) #use prior from training data
params[prefix + 'name'] = 'FK'
_compute_top_fisher(feats, params)
def _run_top_fisher ():
"""Run Linear Kernel with {Top,Fisher}Features."""
# put some constantness into randomness
Math_init_random(dataop.INIT_RANDOM)
data=dataop.get_cubes(4, 8)
prefix='topfk_'
params={
prefix+'N': 3,
prefix+'M': 6,
prefix+'pseudo': 1e-1,
prefix+'order': 1,
prefix+'gap': 0,
prefix+'reverse': False,
prefix+'alphabet': 'CUBE',
prefix+'feature_class': 'string_complex',
prefix+'feature_type': 'Word',
prefix+'data_train': numpy.matrix(data['train']),
prefix+'data_test': numpy.matrix(data['test'])
}
wordfeats=featop.get_features(
params[prefix+'feature_class'], params[prefix+'feature_type'],
data, eval(params[prefix+'alphabet']),
params[prefix+'order'], params[prefix+'gap'], params[prefix+'reverse'])
pos_train=HMM(wordfeats['train'],
params[prefix+'N'], params[prefix+'M'], params[prefix+'pseudo'])
pos_train.train()
pos_train.baum_welch_viterbi_train(BW_NORMAL)
neg_train=HMM(wordfeats['train'],
params[prefix+'N'], params[prefix+'M'], params[prefix+'pseudo'])
neg_train.train()
neg_train.baum_welch_viterbi_train(BW_NORMAL)
pos_test=HMM(pos_train)
pos_test.set_observations(wordfeats['test'])
neg_test=HMM(neg_train)
neg_test.set_observations(wordfeats['test'])
feats={}
feats['train']=TOPFeatures(10, pos_train, neg_train, False, False)
feats['test']=TOPFeatures(10, pos_test, neg_test, False, False)
params[prefix+'name']='TOP'
_compute_top_fisher(feats, params)
feats['train']=FKFeatures(10, pos_train, neg_train)
feats['train'].set_opt_a(-1) #estimate prior
feats['test']=FKFeatures(10, pos_test, neg_test)
feats['test'].set_a(feats['train'].get_a()) #use prior from training data
params[prefix+'name']='FK'
_compute_top_fisher(feats, params)
def _run_hmm():
"""Run generator for Hidden-Markov-Model."""
# put some constantness into randomness
Math_init_random(INIT_RANDOM)
num_examples = 4
params = {
'name': 'HMM',
'accuracy': 1e-6,
'N': 3,
'M': 6,
'num_examples': num_examples,
'pseudo': 1e-10,
'order': 1,
'alphabet': 'CUBE',
'feature_class': 'string_complex',
'feature_type': 'Word',
'data': dataop.get_cubes(num_examples, 1)
}
output = fileop.get_output(category.DISTRIBUTION, params)
feats = featop.get_features(params['feature_class'],
params['feature_type'], params['data'],
eval('features.' + params['alphabet']),
params['order'])
hmm = distribution.HMM(feats['train'], params['N'], params['M'],
params['pseudo'])
hmm.train()
hmm.baum_welch_viterbi_train(distribution.BW_NORMAL)
output[PREFIX + 'likelihood'] = hmm.get_log_likelihood_sample()
output[PREFIX + 'derivatives'] = _get_derivatives(
hmm, feats['train'].get_num_vectors())
output[PREFIX + 'best_path'] = 0
output[PREFIX + 'best_path_state'] = 0
for i in xrange(num_examples):
output[PREFIX + 'best_path'] += hmm.best_path(i)
for j in xrange(params['N']):
output[PREFIX + 'best_path_state'] += hmm.get_best_path_state(i, j)
fileop.write(category.DISTRIBUTION, output)
def _run_hmm ():
"""Run generator for Hidden-Markov-Model."""
# put some constantness into randomness
Math_init_random(INIT_RANDOM)
num_examples=4
params={
'name': 'HMM',
'accuracy': 1e-6,
'N': 3,
'M': 6,
'num_examples': num_examples,
'pseudo': 1e-10,
'order': 1,
'alphabet': 'CUBE',
'feature_class': 'string_complex',
'feature_type': 'Word',
'data': dataop.get_cubes(num_examples, 1)
}
output=fileop.get_output(category.DISTRIBUTION, params)
feats=featop.get_features(
params['feature_class'], params['feature_type'], params['data'],
eval('features.'+params['alphabet']), params['order'])
hmm=distribution.HMM(
feats['train'], params['N'], params['M'], params['pseudo'])
hmm.train()
hmm.baum_welch_viterbi_train(distribution.BW_NORMAL)
output[PREFIX+'likelihood']=hmm.get_log_likelihood_sample()
output[PREFIX+'derivatives']=_get_derivatives(
hmm, feats['train'].get_num_vectors())
output[PREFIX+'best_path']=0
output[PREFIX+'best_path_state']=0
for i in xrange(num_examples):
output[PREFIX+'best_path']+=hmm.best_path(i)
for j in xrange(params['N']):
output[PREFIX+'best_path_state']+=hmm.get_best_path_state(i, j)
fileop.write(category.DISTRIBUTION, output)
