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(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)