def train(diretory=config.root_dir):
'''Train all the files in diretory 'luyin'
The diretory is made up of two subdiretories named 'normal' and 'abnormal'.
As a result , the script will generate 3 files:
(1)scale : used for scale data by the svm command 'svm-scale -s filename > scale'
(2)dataset_scaled : the scaled dataset for training
(3)model : the svm model file
'''
files = os.listdir(config.normal)
dataset = config.dataset
dataset_scaled = config.dataset_scaled
scale = config.scale
#fs = open(dataset,'a')
for f in files:
f = config.normal + f
voice = Voice()
voice.analyze(f)
voice.calFeatures()
voice.learn(dataset, '+1')
files = os.listdir(config.abnormal)
for f in files:
f = config.abnormal + f
voice = Voice()
voice.analyze(f)
voice.calFeatures()
voice.learn(dataset, '-1')
os.system('svm-scale -s %s %s > %s' % (scale, dataset, dataset_scaled))
y, x = svm_read_problem(dataset_scaled)
m = svm_train(y, x)
svm_save_model(config.model, m)
def train(diretory = config.root_dir):
'''Train all the files in diretory 'luyin'
The diretory is made up of two subdiretories named 'normal' and 'abnormal'.
As a result , the script will generate 3 files:
(1)scale : used for scale data by the svm command 'svm-scale -s filename > scale'
(2)dataset_scaled : the scaled dataset for training
(3)model : the svm model file
'''
files = os.listdir(config.normal)
dataset = config.dataset
dataset_scaled = config.dataset_scaled
scale = config.scale
#fs = open(dataset,'a')
for f in files:
f = config.normal + f
voice = Voice()
voice.analyze(f)
voice.calFeatures()
voice.learn(dataset,'+1')
files = os.listdir(config.abnormal)
for f in files:
f = config.abnormal + f
voice = Voice()
voice.analyze(f)
voice.calFeatures()
voice.learn(dataset,'-1')
os.system('svm-scale -s %s %s > %s'%(scale,dataset,dataset_scaled))
y,x = svm_read_problem(dataset_scaled)
m = svm_train(y,x)
svm_save_model(config.model,m)
