| public setSensingProbability ( String state, String perception, double probability ) : void | ||
| state | String | |
| perception | String | |
| probability | double | |
| 리턴 | void | |
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);
}
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;
}
public static HiddenMarkovModel createRobotHMM()
{
// Example adopted from Sebastian Thrun's "Probabilistic Robotics"
// Chapter 2
// A robot faces a door. The state of the door (open or closed)
// constitutes the "hidden state"
// The robot's sensor detects a "closed" or "open" state.
// (Perception may be out of synch with reality because sensors are
// probabilistic).
// The robot can either "do nothing" or "push" the door. These are the
// possible actions.
List<String> states = new List<String>{
HmmConstants.DOOR_OPEN, HmmConstants.DOOR_CLOSED };
List<String> actions = new List<String> {
HmmConstants.DO_NOTHING, HmmConstants.PUSH_DOOR };
List<String> perceptions = new List<String> {
HmmConstants.SEE_DOOR_OPEN, HmmConstants.SEE_DOOR_CLOSED };
RandomVariable prior = new RandomVariable(states);
TransitionModel tm = new TransitionModel(states, actions);
// tm.setTransitionProbability(start_state, action, end_state,
// probability)
// given a start state and an action the probability of the end state is
// probability
//tm.setTransitionProbability(HmmConstants.DOOR_OPEN,
// HmmConstants.DO_NOTHING, HmmConstants.DOOR_OPEN, 1.0);
tm.setTransitionProbability(HmmConstants.DOOR_OPEN,
HmmConstants.DO_NOTHING, HmmConstants.DOOR_OPEN, 1.0);
tm.setTransitionProbability(HmmConstants.DOOR_OPEN,
HmmConstants.DO_NOTHING, HmmConstants.DOOR_CLOSED, 0.0);
tm.setTransitionProbability(HmmConstants.DOOR_CLOSED,
HmmConstants.DO_NOTHING, HmmConstants.DOOR_CLOSED, 1.0);
tm.setTransitionProbability(HmmConstants.DOOR_CLOSED,
HmmConstants.DO_NOTHING, HmmConstants.DOOR_OPEN, 0.0);
tm.setTransitionProbability(HmmConstants.DOOR_OPEN,
HmmConstants.PUSH_DOOR, HmmConstants.DOOR_OPEN, 1.0);
tm.setTransitionProbability(HmmConstants.DOOR_OPEN,
HmmConstants.PUSH_DOOR, HmmConstants.DOOR_CLOSED, 0.0);
tm.setTransitionProbability(HmmConstants.DOOR_CLOSED,
HmmConstants.PUSH_DOOR, HmmConstants.DOOR_CLOSED, 0.2);
tm.setTransitionProbability(HmmConstants.DOOR_CLOSED,
HmmConstants.PUSH_DOOR, HmmConstants.DOOR_OPEN, 0.8);
SensorModel sm = new SensorModel(states, perceptions);
// sm.setSensingProbaility(state,perception,p); given a state the
// probability of a perception is p
sm.setSensingProbability(HmmConstants.DOOR_OPEN,
HmmConstants.SEE_DOOR_CLOSED, 0.4);
sm.setSensingProbability(HmmConstants.DOOR_OPEN,
HmmConstants.SEE_DOOR_OPEN, 0.6);
sm.setSensingProbability(HmmConstants.DOOR_CLOSED,
HmmConstants.SEE_DOOR_OPEN, 0.2);
sm.setSensingProbability(HmmConstants.DOOR_CLOSED,
HmmConstants.SEE_DOOR_CLOSED, 0.8);
HiddenMarkovModel hmm = new HiddenMarkovModel(prior, tm, sm);
return hmm;
}
public static HiddenMarkovModel createRobotHMM()
{
// Example adopted from Sebastian Thrun's "Probabilistic Robotics"
// Chapter 2
// A robot faces a door. The state of the door (open or closed)
// constitutes the "hidden state"
// The robot's sensor detects a "closed" or "open" state.
// (Perception may be out of synch with reality because sensors are
// probabilistic).
// The robot can either "do nothing" or "push" the door. These are the
// possible actions.
List <String> states = new List <String> {
HmmConstants.DOOR_OPEN, HmmConstants.DOOR_CLOSED
};
List <String> actions = new List <String> {
HmmConstants.DO_NOTHING, HmmConstants.PUSH_DOOR
};
List <String> perceptions = new List <String> {
HmmConstants.SEE_DOOR_OPEN, HmmConstants.SEE_DOOR_CLOSED
};
RandomVariable prior = new RandomVariable(states);
TransitionModel tm = new TransitionModel(states, actions);
// tm.setTransitionProbability(start_state, action, end_state,
// probability)
// given a start state and an action the probability of the end state is
// probability
//tm.setTransitionProbability(HmmConstants.DOOR_OPEN,
// HmmConstants.DO_NOTHING, HmmConstants.DOOR_OPEN, 1.0);
tm.setTransitionProbability(HmmConstants.DOOR_OPEN,
HmmConstants.DO_NOTHING, HmmConstants.DOOR_OPEN, 1.0);
tm.setTransitionProbability(HmmConstants.DOOR_OPEN,
HmmConstants.DO_NOTHING, HmmConstants.DOOR_CLOSED, 0.0);
tm.setTransitionProbability(HmmConstants.DOOR_CLOSED,
HmmConstants.DO_NOTHING, HmmConstants.DOOR_CLOSED, 1.0);
tm.setTransitionProbability(HmmConstants.DOOR_CLOSED,
HmmConstants.DO_NOTHING, HmmConstants.DOOR_OPEN, 0.0);
tm.setTransitionProbability(HmmConstants.DOOR_OPEN,
HmmConstants.PUSH_DOOR, HmmConstants.DOOR_OPEN, 1.0);
tm.setTransitionProbability(HmmConstants.DOOR_OPEN,
HmmConstants.PUSH_DOOR, HmmConstants.DOOR_CLOSED, 0.0);
tm.setTransitionProbability(HmmConstants.DOOR_CLOSED,
HmmConstants.PUSH_DOOR, HmmConstants.DOOR_CLOSED, 0.2);
tm.setTransitionProbability(HmmConstants.DOOR_CLOSED,
HmmConstants.PUSH_DOOR, HmmConstants.DOOR_OPEN, 0.8);
SensorModel sm = new SensorModel(states, perceptions);
// sm.setSensingProbaility(state,perception,p); given a state the
// probability of a perception is p
sm.setSensingProbability(HmmConstants.DOOR_OPEN,
HmmConstants.SEE_DOOR_CLOSED, 0.4);
sm.setSensingProbability(HmmConstants.DOOR_OPEN,
HmmConstants.SEE_DOOR_OPEN, 0.6);
sm.setSensingProbability(HmmConstants.DOOR_CLOSED,
HmmConstants.SEE_DOOR_OPEN, 0.2);
sm.setSensingProbability(HmmConstants.DOOR_CLOSED,
HmmConstants.SEE_DOOR_CLOSED, 0.8);
HiddenMarkovModel hmm = new HiddenMarkovModel(prior, tm, sm);
return(hmm);
}