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_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/")
