class PredictableModelWrapper(object):
def __init__(self, model):
self.model = model
self.numeric_dataset = NumericDataSet()
def compute(self):
X,y = self.numeric_dataset.get()
self.model.compute(X,y)
def set_data(self, numeric_dataset):
self.numeric_dataset = numeric_dataset
def predict(self, image):
prediction_result = self.model.predict(image)
# Only take label right now:
num_label = prediction_result[0]
str_label = self.numeric_dataset.resolve_by_num(num_label)
return str_label
def update(self, name, image):
self.numeric_dataset.add(name, image)
class_label = self.numeric_dataset.resolve_by_str(name)
extracted_feature = self.feature.extract(image)
self.classifier.update(extracted_feature, class_label)
def __repr__(self):
return "PredictableModelWrapper (Inner Model=%s)" % (str(self.model))
class PredictableModelWrapper(object):
def __init__(self, model):
self.model = model
self.numeric_dataset = NumericDataSet()
def compute(self):
X,y = self.numeric_dataset.get()
self.model.compute(X,y)
def set_data(self, numeric_dataset):
self.numeric_dataset = numeric_dataset
def predict(self, image):
prediction_result = self.model.predict(image)
# Only take label right now:
num_label = prediction_result[0]
str_label = self.numeric_dataset.resolve_by_num(num_label)
return str_label
def update(self, name, image):
self.numeric_dataset.add(name, image)
class_label = self.numeric_dataset.resolve_by_str(name)
extracted_feature = self.feature.extract(image)
self.classifier.update(extracted_feature, class_label)
def __repr__(self):
return "PredictableModelWrapper (Inner Model=%s)" % (str(self.model))
