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This operator uses a kalman filter to estimate the distribution of one or more attribute values.

Parameter

Attributes: The attributes to perform the filter on
Transition: The transition matrix
ProcessNoise: The process noise matrix
Measurement: The measurement matrix
MeasurementNoise: The measurement noise matrix
InitialState: The initial state vector (optional)
InitialError: The initial error matrix (optional)
Control: The control matrix (optional)

Example

out = KALMAN({MEASUREMENT = '[1.0]', TRANSITION = '[1.0]', ProcessNoise = '[2.0]', ATTRIBUTES = ['x'], MEASUREMENTNOISE = '[4.0]'}, in)

in = KALMAN({INITIALSTATE = '[0.0, 0.0, 0.0, 0.0]', INITIALERROR = '[1.0,0.0,0.0,0.0;0.0,1.0,0.0,0.0;0.0,0.0,1.0,0.0;0.0,0.0,0.0,1.0]', MEASUREMENT = '[0.0,0.0,1.0,0.0;0.0,0.0,0.0,1.0]', TRANSITION = '[1.0,0.0,1.0,0.0;0.0,1.0,0.0,1.0;0.0,0.0,1.0,0.0;0.0,0.0,0.0,1.0]', PROCESSNOISE = '[1/4, 1/4, 1/2, 1/2;1/4, 1/4, 1/2, 1/2; 1/2, 1/2, 1, 1; 1/2, 1/2, 1, 1]', ATTRIBUTES = ['x1','x2'], MEASUREMENTNOISE = '[10.0,0.0;0.0,10.0]'}, out)