cecile-supercool-tracker 0.0.1

Machine learning framework for building object trackers and similarity search engines
Documentation 
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from similari import Point2DKalmanFilter

if __name__ == '__main__':
    f = Point2DKalmanFilter()
    state = f.initiate(1.0, 2.0)

    for i in range(1, 21):
        state = f.predict(state)
        print("Predicted", state.x(), state.y())

        pt = (1.0 + i * 0.1, 2.0 + i * 0.1)
        print("Observation:", pt)
        state = f.update(state, pt[0], pt[1])