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)
