class CorePy(object): def __init__(self, path, predictorType): super(CorePy, self).__init__() self.image = ImageFactory() self.path = path if predictorType == "kppv": self.predictor = Kppv() # elif predictorType == "mlp": # self.predictor = Mlp() else: self.predictor = None self.max_distance = 0 def setImage(self, path_to_image): self.image.initialize(path_to_image) def predict_current(self): predicted_classes, result = np.zeros( (len(self.image.feature_list), 2)), 0 for x in range(0, len(self.image.feature_list)): predicted_classes[x], distance = self.predictor.predict( self.image.feature_list[x]) result += predicted_classes[x] if distance >= 0: self.max_distance = max(self.max_distance, distance) self.image.class_list = predicted_classes pass def train_predictor(self): self.predictor.train(self.image.feature_list, self.image.class_list)
class CorePy(object): def __init__(self, path, predictorType): super(CorePy, self).__init__() self.image = ImageFactory() self.path = path if predictorType == "kppv": self.predictor = Kppv() # elif predictorType == "mlp": # self.predictor = Mlp() else: self.predictor = None self.max_distance = 0 def setImage(self, path_to_image): self.image.initialize(path_to_image) def predict_current(self): predicted_classes, result = np.zeros((len(self.image.feature_list), 2)), 0 for x in range(0,len(self.image.feature_list)): predicted_classes[x], distance = self.predictor.predict(self.image.feature_list[x]) result += predicted_classes[x] if distance >= 0: self.max_distance = max(self.max_distance, distance) self.image.class_list = predicted_classes pass def train_predictor(self): self.predictor.train(self.image.feature_list, self.image.class_list)
def __init__(self, path, predictorType): super(CorePy, self).__init__() self.image = ImageFactory() self.path = path if predictorType == "kppv": self.predictor = Kppv() # elif predictorType == "mlp": # self.predictor = Mlp() else: self.predictor = None self.max_distance = 0