def __init__(self):
'''
Constructor
'''
self.dsr = DatasetReader()
self.fenc = FreemanEncoder()
self.training_data = []
def __init__(self, n_neighbors=1):
'''
Constructor
'''
self.dsr = DatasetReader()
self.fenc = FreemanEncoder()
self.data = []
self.knn = KNeighborsClassifier(n_neighbors=n_neighbors,
algorithm='auto',
metric=self.lev_metric)
def _init_classifiers(self):
# Initialize classifier objects
self.fenc = FreemanEncoder()
self.knn = KNN.KNN()
self.HMM = HMM.HMM()
self.NaiveBayes = NaiveBayes.NaiveBayes()
self.RandomForest = RandomForest.RandomForests()
self.SVM = svm.SVM_SVC()
self.LogisticReg = LogisticReg.LogisticReg()
self.AdaBoost = adaboost.AdaBoost()
self.GBRT = gbrt.GBRT()
#Train initially on the default data set, if no model saved already
# Initialize KNN, no saved model for KNN
self.knn.knn_train(CharRecognitionGUI_support.training_dataset, 1.0)
# Initialize HMM
self.HMM.training(CharRecognitionGUI_support.training_dataset)
# Initialize Naive Bayes
try:
pickle.load( open( "./Models/naivebayes_model.p", "rb" ) )
except IOError:
self.NaiveBayes.training(CharRecognitionGUI_support.training_dataset)
# Initialize Random Forest
try:
pickle.load( open( "./Models/random_forest.p", "rb" ) )
except IOError:
self.RandomForest.training(CharRecognitionGUI_support.training_dataset)
# Initialize SVM
try:
pickle.load( open( "./Models/svm.p", "rb" ) )
except IOError:
self.SVM.training(CharRecognitionGUI_support.training_dataset)
# Initialize Logistic Regression
try:
pickle.load( open( "./Models/logistic_model.p", "rb" ) )
except IOError:
self.LogisticReg.training(CharRecognitionGUI_support.training_dataset)
# Initialize AdaBoost
try:
pickle.load( open( "./Models/AdaBoostClassifier.p", "rb" ) )
except IOError:
self.AdaBoost.training(CharRecognitionGUI_support.training_dataset)
# Initialize GBRT
try:
pickle.load( open( "./Models/GradientBoostingClassifier.p", "rb" ) )
except IOError:
self.GBRT.training(CharRecognitionGUI_support.training_dataset)
def __init__(self):
'''
Constructor
'''
self.dsr = DatasetReader()
self.fenc = FreemanEncoder()
states = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
symbols = ['0', '1', '2', '3', '4', '5', '6', '7']
self.learning_model = HiddenMarkovModelTrainer(states=states,
symbols=symbols)
self.model = None
