def __init__(self, complexity, numInputs, outputClassifier, inputClassifiers, appFieldsDict): ContextEngineBase.__init__(self,complexity, numInputs, outputClassifier, inputClassifiers, appFieldsDict) self.fea_det = cv2.xfeatures2d.SURF_create(); self.numObservations = 0; self.classes_names = os.listdir("../python/VLR/dataset/") if True in appFieldsDict: self.clf, self.classes_names, self.stdSlr, self.k, self.voc = joblib.load("../python/VLR/bof.pkl")
def __init__(self, complexity, numInputs, outputClassifier, inputClassifiers, appFieldsDict): ContextEngineBase.__init__(self,complexity, numInputs, outputClassifier, inputClassifiers, appFieldsDict) #self.discreteOutputs = discreteOutputs; #3sself.discreteInputs = discreteInputs; self.x_Obs = np.empty([0,numInputs]); self.x_Test = np.empty([0,numInputs]); self.knnRegressor = KNeighborsRegressor(n_neighbors=self.complexity.value, weights='uniform');
def __init__(self, complexity, numInputs, outputClassifier, inputClassifiers, appFieldsDict): ContextEngineBase.__init__(self,complexity, numInputs, outputClassifier, inputClassifiers, appFieldsDict) self.alpr = Alpr("us", "/etc/openalpr/openalpr.conf", "/home/pi/openalpr/runtime_data") if not self.alpr.is_loaded(): print("Error loading OpenALPR") sys.exit(1) self.alpr.set_top_n(self.n) self.alpr.set_default_region("va")
def __init__(self, numInputs, outputClassifier, inputClassifiers, appFieldsDict): ContextEngineBase.__init__(self, numInputs, outputClassifier, inputClassifiers, appFieldsDict) self.numInputs = numInputs self.outputClassifier = outputClassifier self.inputClassifiersList = inputClassifiers self.customFieldsDict = appFieldsDict self.svmObj = svm.LinearSVC()
def __init__(self, numInputs, outputClassifier, inputClassifiers,appFieldsDict): ContextEngineBase.__init__(self, numInputs, outputClassifier, inputClassifiers, appFieldsDict) # Parameters that are passed self.numInputs = numInputs self.outputClassifier = outputClassifier self.inputClassifiersList = inputClassifiers self.customFieldsDict = appFieldsDict # Linear SVM object self.svmObj = svm.LinearSVC()
def __init__(self, complexity, numInputs, outputClassifier, inputClassifiers, appFieldsDict): ContextEngineBase.__init__(self, complexity, numInputs, outputClassifier, inputClassifiers, appFieldsDict) self.alpr = Alpr("us", "/etc/openalpr/openalpr.conf", "/home/pi/openalpr/runtime_data") if not self.alpr.is_loaded(): print("Error loading OpenALPR") sys.exit(1) self.alpr.set_top_n(self.n) self.alpr.set_default_region("va")
def __init__(self, numInputs, outputClassifier, inputClassifiers, appFieldsDict): ContextEngineBase.__init__(self, numInputs, outputClassifier, inputClassifiers, appFieldsDict) self.decTreeAB = AdaBoostClassifier(DecisionTreeClassifier(max_depth=2)) def addBatchObservations(self, newInputObsMatrix, newOutputVector): if(len(newInputObsMatrix.shape) == 2 and newInputObsMatrix.shape[1] == self.numInputs and newOutputVector.shape[0] == newInputObsMatrix.shape[0]): #print("All good!"); newOutputVector = newOutputVector.ravel(); i = 0; for newInputVector in newInputObsMatrix: newOutputValue = newOutputVector[i]; self.addSingleObservation(newInputVector, newOutputValue); i += 1; else: print("Wrong dimensions!");
def __init__(self, numInputs, outputClassifier, inputClassifiers, appFieldsDict): ContextEngineBase.__init__(self, numInputs, outputClassifier, inputClassifiers, appFieldsDict) self.numInputs = numInputs self.outputClassifier = outputClassifier self.inputClassifiersList = inputClassifiers self.customFieldsDict = appFieldsDict if 'kernel' in appFieldsDict: svrKernel = appFieldsDict['kernel'] else: svrKernel = 'linear' if 'degree' in appFieldsDict: svrDegree = appFieldsDict['degree'] else: svrDegree = 1 self.svrLinear = svm.SVR(kernel='rbf', degree=svrDegree)
def __init__(self, numInputs, outputClassifier, inputClassifiers, appFieldsDict): ContextEngineBase.__init__(self, numInputs, outputClassifier, inputClassifiers, appFieldsDict) self.numInputs = numInputs; self.outputClassifier = outputClassifier; self.inputClassifiersList = inputClassifiers; self.customFieldsDict = appFieldsDict; if 'kernel' in appFieldsDict: svrKernel = appFieldsDict['kernel'] else: svrKernel = 'linear' if 'degree' in appFieldsDict: svrDegree = appFieldsDict['degree'] else: svrDegree = 1 # SVR regressor object self.svrLinear = svm.SVR(kernel='rbf', degree = svrDegree)
def __init__(self, complexity, numInputs, outputClassifier, inputClassifiers, appFieldsDict): ContextEngineBase.__init__(self,complexity, numInputs, outputClassifier, inputClassifiers, appFieldsDict) self.x_Obs = np.empty([0, numInputs]) self.x_Test = np.empty([0, numInputs]) self.svrLinear = svm.SVR(kernel='rbf')
def __init__(self, complexity, numInputs, outputClassifier, inputClassifiers, appFieldsDict): ContextEngineBase.__init__(self,complexity, numInputs, outputClassifier, inputClassifiers, appFieldsDict) self.x_Obs = np.empty([0, numInputs]) self.x_Test = np.empty([0, numInputs]) self.decTreeAB = AdaBoostClassifier(DecisionTreeClassifier(max_depth=2))
def __init__(self, complexity, numInputs, outputClassifier, inputClassifiers, appFieldsDict): ContextEngineBase.__init__(self,complexity, numInputs, outputClassifier, inputClassifiers, appFieldsDict) #self.x_Test = np.empty([0,numInputs]); self.fea_det = cv2.xfeatures2d.SURF_create(); self.numObservations = 0; self.classes_names = os.listdir("../python/VLR/dataset/")