AIMA.Core.Probability.Reasoning.TransitionModel.setTransitionProbability C# (CSharp) Method

        public void setTransitionProbability(String startState, String endState,
                Double probability)
        {
            String start_state_plus_action = String.Concat(startState
                    ,HmmConstants.DO_NOTHING);
            table.set(start_state_plus_action, endState, probability);
        }

        public static HiddenMarkovModel createRainmanHMM()
        {
            List <String> states = new List <String> {
                HmmConstants.RAINING, HmmConstants.NOT_RAINING
            };
            // no actions because the observer has no way of changing the hidden
            // state and i spassive
            List <String> perceptions = new List <String> {
                HmmConstants.SEE_UMBRELLA, HmmConstants.SEE_NO_UMBRELLA
            };

            RandomVariable prior = new RandomVariable(states);

            TransitionModel tm = new TransitionModel(states);

            // tm.setTransitionModelValue(start_state, action, end_state,
            // probability);
            // given a start state and an action the probability of the end state is
            // probability
            tm.setTransitionProbability(HmmConstants.RAINING, HmmConstants.RAINING,
                                        0.7);
            tm.setTransitionProbability(HmmConstants.RAINING,
                                        HmmConstants.NOT_RAINING, 0.3);
            tm.setTransitionProbability(HmmConstants.NOT_RAINING,
                                        HmmConstants.RAINING, 0.3);
            tm.setTransitionProbability(HmmConstants.NOT_RAINING,
                                        HmmConstants.NOT_RAINING, 0.7);

            SensorModel sm = new SensorModel(states, perceptions);

            // sm.setSensingProbaility(state,perception,p); given a state the
            // probability of a perception is p
            sm.setSensingProbability(HmmConstants.RAINING,
                                     HmmConstants.SEE_UMBRELLA, 0.9);
            sm.setSensingProbability(HmmConstants.RAINING,
                                     HmmConstants.SEE_NO_UMBRELLA, 0.1);
            sm.setSensingProbability(HmmConstants.NOT_RAINING,
                                     HmmConstants.SEE_UMBRELLA, 0.2);
            sm.setSensingProbability(HmmConstants.NOT_RAINING,
                                     HmmConstants.SEE_NO_UMBRELLA, 0.8);

            HiddenMarkovModel hmm = new HiddenMarkovModel(prior, tm, sm);

            // hmm.setSensorModelValue(state,perception,p); given a state the
            // probability of a perception is p

            return(hmm);
        }