def __init__(self, config):
super(ClassifyDriver, self).__init__(config)
genuineX = []
forgeryX = []
genuineY = []
forgeryY = []
# Training process
for sigs in self.train_set:
personTrain = PersonTraining(self.config, sigs)
genuine, forgery = personTrain.calc_train_set()
genuineX.extend(genuine)
forgeryX.extend(forgery)
genuineY = [1] * len(genuineX)
forgeryY = [0] * len(forgeryX)
trainX = genuineX + forgeryX
trainY = genuineY + forgeryY
self.driver.fit(trainX, trainY)
with open(self.config.ModelDumpFilePath, "w") as fp:
cPickle.dump(self.driver, fp)
def __init__(self, config):
super(RegressionDriver, self).__init__(config)
genuineX = []
forgeryX = []
genuineY = []
forgeryY = []
# Training process
for sigs in self.train_set:
personTrain = PersonTraining(self.config, sigs)
genuine, forgery = personTrain.calc_train_set()
genuineX.extend(genuine)
forgeryX.extend(forgery)
# To adjust PCA result, 0 means genuine and 1 means forgery
genuineY = [0.0] * len(genuineX)
forgeryY = [1.0] * len(forgeryX)
trainX = genuineX + forgeryX
trainY = genuineY + forgeryY
self.driver.fit(trainX, trainY)
def __init__(self, config):
super(RegressionDriver, self).__init__(config)
genuineX = []
forgeryX = []
genuineY = []
forgeryY = []
# Training process
for sigs in self.train_set:
personTrain = PersonTraining(self.config, sigs)
genuine, forgery = personTrain.calc_train_set()
genuineX.extend(genuine)
forgeryX.extend(forgery)
# To adjust PCA result, 0 means genuine and 1 means forgery
genuineY = [0.0] * len(genuineX)
forgeryY = [1.0] * len(forgeryX)
trainX = genuineX + forgeryX
trainY = genuineY + forgeryY
self.driver.fit(trainX, trainY)
def __init__(self, config):
super(ClassifyDriver, self).__init__(config)
genuineX = []
forgeryX = []
genuineY = []
forgeryY = []
# Training process
for sigs in self.train_set:
personTrain = PersonTraining(self.config, sigs)
genuine, forgery = personTrain.calc_train_set()
genuineX.extend(genuine)
forgeryX.extend(forgery)
genuineY = [1] * len(genuineX)
forgeryY = [0] * len(forgeryX)
trainX = genuineX + forgeryX
trainY = genuineY + forgeryY
self.driver.fit(trainX, trainY)
