def __init__(self, A=None, H=None, Q=None, R=None, G=None, X_0=None, threshold=100): KalmanFilter.__init__(self, A, H, Q, R, G, X_0) self.threshold = threshold self.sphereVolume = self.__getSphereVolume__(H.shape[0]) self.__initializeInnovationCovariance__() self.age = 1 self.totalVisibleCount = 0 self.consecutiveInvisibleCount = 0 self.detectionProbability = 0.90
def __init__(self, respond = None, regressors = None, intercept = False, Sigma = None, sigma = None, initBeta = None, initVariance = None, Phi = None, **args): """ :param respond: Dependent time series :type respond: TimeSeriesFrame<double> :param regressors: Independent time serieses :type regressors: TimeSeriesFrame<double> :param intercept: include/exclude intercept in the regression :type intercept: boolean """ KalmanFilter.__init__(self, respond, regressors, intercept, Sigma, sigma, initBeta, initVariance, Phi, **args)
def __init__(self, respond=None, regressors=None, intercept=False, Sigma=None, sigma=None, initBeta=None, initVariance=None, Phi=None, **args): """ :param respond: Dependent time series :type respond: TimeSeriesFrame<double> :param regressors: Independent time serieses :type regressors: TimeSeriesFrame<double> :param intercept: include/exclude intercept in the regression :type intercept: boolean """ KalmanFilter.__init__(self, respond, regressors, intercept, Sigma, sigma, initBeta, initVariance, Phi, **args)