def __init__(self, data_dir, svm_prefix, has_zero_insert):
super(LibSvmClassifier, self).__init__()
self.data_dir = data_dir
self.svm_prefix = svm_prefix
self.has_zero_insert = has_zero_insert
model_path = join(data_dir, svm_prefix + '.model')
if os.path.isfile(model_path):
self.logger.info("Loading libSVM model file '%s'." % model_path)
self.svm_model = svm_model(model_path)
else:
raise IOError("libSVM model file '%s' not found!" % model_path)
range_file = join(data_dir, svm_prefix + '.range')
if isfile(range_file):
self.logger.info("Loading libSVM range file '%s'." % range_file)
self.normalizer = Normalizer(range_file)
else:
raise IOError("libSVM range file '%s' not found!" % range_file)
self.probability = True if self.svm_model.probability == 1 else False
class LibSvmClassifier(LoggerObject):
NAME = 'libSVM'
METHOD = 'Support Vector Machine'
def __init__(self, data_dir, svm_prefix, has_zero_insert):
super(LibSvmClassifier, self).__init__()
self.data_dir = data_dir
self.svm_prefix = svm_prefix
self.has_zero_insert = has_zero_insert
model_path = join(data_dir, svm_prefix+'.model')
if os.path.isfile(model_path):
self.logger.info("Loading libSVM model file '%s'." %model_path)
self.svm_model = svm_model(model_path)
else:
raise IOError("libSVM model file '%s' not found!" %model_path)
range_file = join(data_dir, svm_prefix+'.range')
if isfile(range_file):
self.logger.info("Loading libSVM range file '%s'." %range_file)
self.normalizer = Normalizer(range_file)
else:
raise IOError("libSVM range file '%s' not found!" %range_file)
self.probability = True if self.svm_model.probability == 1 else False
def normalize(self, sample_feature_data):
return self.normalizer.scale(sample_feature_data)
def __call__(self, sample_feature_data):
features_scaled = self.normalize(sample_feature_data)
if self.has_zero_insert:
features_scaled = [0] + features_scaled
if self.probability:
label, prob = self.svm_model.predict_probability(features_scaled)
label = int(label)
else:
label = self.svm_model.predict(features_scaled)
label = int(label)
prob = {label: 1.0}
return label, prob
class LibSvmClassifier(LoggerObject):
NAME = 'libSVM'
METHOD = 'Support Vector Machine'
def __init__(self, data_dir, svm_prefix, has_zero_insert):
super(LibSvmClassifier, self).__init__()
self.data_dir = data_dir
self.svm_prefix = svm_prefix
self.has_zero_insert = has_zero_insert
model_path = join(data_dir, svm_prefix+'.model')
if os.path.isfile(model_path):
self.logger.info("Loading libSVM model file '%s'." %model_path)
self.svm_model = svm_model(model_path)
else:
raise IOError("libSVM model file '%s' not found!" %model_path)
range_file = join(data_dir, svm_prefix+'.range')
if isfile(range_file):
self.logger.info("Loading libSVM range file '%s'." %range_file)
self.normalizer = Normalizer(range_file)
else:
raise IOError("libSVM range file '%s' not found!" %range_file)
self.probability = True if self.svm_model.probability == 1 else False
def normalize(self, sample_feature_data):
return self.normalizer.scale(sample_feature_data)
def __call__(self, sample_feature_data):
features_scaled = self.normalize(sample_feature_data)
if self.has_zero_insert:
features_scaled = [0] + features_scaled
if self.probability:
label, prob = self.svm_model.predict_probability(features_scaled)
label = int(label)
else:
label = self.svm_model.predict(features_scaled)
label = int(label)
prob = {label: 1.0}
return label, prob
def __init__(self, data_dir, svm_prefix, has_zero_insert):
super(LibSvmClassifier, self).__init__()
self.data_dir = data_dir
self.svm_prefix = svm_prefix
self.has_zero_insert = has_zero_insert
model_path = join(data_dir, svm_prefix + ".model")
if os.path.isfile(model_path):
self.logger.info("Loading libSVM model file '%s'." % model_path)
self.svm_model = svm_model(model_path)
else:
raise IOError("libSVM model file '%s' not found!" % model_path)
range_file = join(data_dir, svm_prefix + ".range")
if isfile(range_file):
self.logger.info("Loading libSVM range file '%s'." % range_file)
self.normalizer = Normalizer(range_file)
else:
raise IOError("libSVM range file '%s' not found!" % range_file)
self.probability = True if self.svm_model.probability == 1 else False
