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How to use set_states method in lisa

Best Python code snippet using lisa_python

search.py

Source:search.py

1# search.py2# ---------3# Licensing Information:  You are free to use or extend these projects for4# educational purposes provided that (1) you do not distribute or publish5# solutions, (2) you retain this notice, and (3) you provide clear6# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.7# 8# Attribution Information: The Pacman AI projects were developed at UC Berkeley.9# The core projects and autograders were primarily created by John DeNero10# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).11# Student side autograding was added by Brad Miller, Nick Hay, and12# Pieter Abbeel (pabbeel@cs.berkeley.edu).13"""14In search.py, you will implement generic search algorithms which are called by15Pacman agents (in searchAgents.py).16"""17import util18class SearchProblem:19    """20    This class outlines the structure of a search problem, but doesn't implement21    any of the methods (in object-oriented terminology: an abstract class).22    You do not need to change anything in this class, ever.23    """24    def getStartState(self):25        """26        Returns the start state for the search problem.27        """28        util.raiseNotDefined()29    def isGoalState(self, state):30        """31          state: Search state32        Returns True if and only if the state is a valid goal state.33        """34        util.raiseNotDefined()35    def getSuccessors(self, state):36        """37          state: Search state38        For a given state, this should return a list of triples, (successor,39        action, stepCost), where 'successor' is a successor to the current40        state, 'action' is the action required to get there, and 'stepCost' is41        the incremental cost of expanding to that successor.42        """43        util.raiseNotDefined()44    def getCostOfActions(self, actions):45        """46         actions: A list of actions to take47        This method returns the total cost of a particular sequence of actions.48        The sequence must be composed of legal moves.49        """50        util.raiseNotDefined()51def tinyMazeSearch(problem):52    """53    Returns a sequence of moves that solves tinyMaze.  For any other maze, the54    sequence of moves will be incorrect, so only use this for tinyMaze.55    """56    from game import Directions57    s = Directions.SOUTH58    w = Directions.WEST59    return [s, s, w, s, w, w, s, w]60def depthFirstSearch(problem):61    """62    Search the deepest nodes in the search tree first.63    Your search algorithm needs to return a list of actions that reaches the64    goal. Make sure to implement a graph search algorithm.65    To get started, you might want to try some of these simple commands to66    understand the search problem that is being passed in:67    print "Start:", problem.getStartState()68    print "Is the start a goal?", problem.isGoalState(problem.getStartState())69    print "Start's successors:", problem.getSuccessors(problem.getStartState())70    """71    "*** YOUR CODE HERE ***"72    state_stack = util.Stack()73    set_states = set()74    state_stack.push((problem.getStartState(), []))75    while not state_stack.isEmpty():76        popped_state, popped_moves = state_stack.pop()77        if (popped_state in set_states):78            continue79        elif problem.isGoalState(popped_state):80            return popped_moves81        set_states.add(popped_state)82        for state, direction, cost in problem.getSuccessors(popped_state):83            if (state in set_states):84                continue85            state_stack.push((state, popped_moves + [direction]))86    return []87def breadthFirstSearch(problem):88    """Search the shallowest nodes in the search tree first."""89    "*** YOUR CODE HERE ***"90    state_queue = util.Queue()91    set_states = set()92    state_queue.push((problem.getStartState(), []))93    while not state_queue.isEmpty():94        popped_state, popped_moves = state_queue.pop()95        if (popped_state in set_states):96            continue97        elif problem.isGoalState(popped_state):98            return popped_moves99        set_states.add(popped_state)100        for state, direction, cost in problem.getSuccessors(popped_state):101            if (state in set_states):102                continue103            state_queue.push((state, popped_moves + [direction]))104    return []105def uniformCostSearch(problem):106    """Search the node of least total cost first."""107    "*** YOUR CODE HERE ***"108    state_pq = util.PriorityQueue()109    set_states = set()110    state_pq.push((problem.getStartState(), [], 0), 0)111    while not state_pq.isEmpty():112        popped_state, popped_moves, popped_cost = state_pq.pop()113        if (popped_state in set_states):114            continue115        elif problem.isGoalState(popped_state):116            return popped_moves117        set_states.add(popped_state)118        for state, direction, cost in problem.getSuccessors(popped_state):119            if (state in set_states):120                continue121            state_pq.push((state, popped_moves + [direction], popped_cost + cost), popped_cost + cost)122    return []123def nullHeuristic(state, problem=None):124    """125    A heuristic function estimates the cost from the current state to the nearest126    goal in the provided SearchProblem.  This heuristic is trivial.127    """128    return 0129def aStarSearch(problem, heuristic=nullHeuristic):130    """Search the node that has the lowest combined cost and heuristic first."""131    "*** YOUR CODE HERE ***"132    state_pq = util.PriorityQueue()133    set_states = set()134    state_pq.push((problem.getStartState(), [], 0), 0)135    while not state_pq.isEmpty():136        popped_state, popped_moves, popped_cost = state_pq.pop()137        if (popped_state in set_states):138            continue139        if problem.isGoalState(popped_state):140            return popped_moves141        set_states.add(popped_state)142        for state, direction, cost in problem.getSuccessors(popped_state):143            if (state in set_states):144                continue145            hvalue = heuristic(state, problem)146            state_pq.push((state, popped_moves + [direction], popped_cost + cost), popped_cost + cost + hvalue)147    return []148# Abbreviations149bfs = breadthFirstSearch150dfs = depthFirstSearch151astar = aStarSearch...

main.py

Source:main.py

...34		d_ndfa['Transitions'][state] = {} 35	for each in lines_trans:36		d_ndfa['Transitions'][each[0]][each[1]] = each[2:] 37	print(d_ndfa)38def print_set_states(set_states):39	print("\nTransition of Set of States\n".center(20))40	for sets in set_states:41		for transition in set_states[sets]:42			print(sets,",",transition, "=>", set_states[sets][transition], "\n")43def equivalence(set_states):44	i = 045	d_dfa={}46	d_eq={}47	for sets in set_states:48		d_eq[sets] = "q"+str(i) 49		d_dfa['q'+str(i)] = set_states[sets] 50		i+=151	for key in d_dfa: 52		for letter, trans in d_dfa[key].items():53			key_name = ",".join(trans)54			if key_name in d_eq.keys(): 55				d_dfa[key][letter] = d_eq[key_name] 56	return d_dfa57def print_table(set_states, alphabet):58	print("Dfa transition table\n".center(25))59	table = PrettyTable()60	header = ['States']+alphabet61	table.field_names = header62	for s in set_states:63		row_data = []64		row_data.append(s)65		for t in alphabet:66			if t in set_states[s]:67				row_data.append(set_states[s][t])68			else:69				row_data.append("--")70			if len(row_data) == len(header):71				table.add_row(row_data)72	print(table)73def main():74	d_ndfa = read_data()75	result = to_dfa(d_ndfa['Initial_state'], d_ndfa, set_states = {})76	print_set_states(result)77	print_table(result, d_ndfa['Alphabet'])78	print_table(equivalence(result), d_ndfa['Alphabet'])79if __name__ == "__main__":...

utils.py

Source:utils.py

1"""2MIT License, Kemal Ficici, 20183github.com/kemfic4"""5import numpy as np6import matplotlib7import matplotlib.pyplot as plt8class process(object):9    process_response = 010    def __init__(self, const_shift=0, noise=False):11        self.constant_shift = const_shift/512.012        self.noise = noise13    def update(self, controller_response, current_state):14        self.process_response = self.process_response*0.96 - 0.01*controller_response + self.constant_shift +self.noise*((np.random.rand()-0.5)/100)15        self.process_response = min(1, self.process_response)16        self.process_response = max(-1, self.process_response)17        return current_state + self.process_response18def error(current_state, set_state):19    return current_state - set_state20    21class Plant(object):22    23    def __init__(self, control, d_t=0.1, t_max=60, set_steady=False, set_shift=False, set_sin=False, noise=False):24        self.delta_t = d_t25        self.t_max = t_max26        self.t = np.arange(0, self.t_max, self.delta_t)27        if set_sin:28            self.set_states = np.sin(self.t*4*np.pi/self.t_max)29        elif set_steady:30            self.set_states = np.zeros_like(self.t)31        else:32            self.set_states = np.zeros_like(self.t)33            self.set_states[200:400] = 134        35        self.cur_process = process(set_shift, noise)        36        self.controller = control37    def simulate(self):38        states = np.zeros_like(self.t)39        states[0] = -140        errors = np.zeros_like(self.t)41        errors[0] = error(states[0], self.set_states[0])42        for i in range(1,len(self.t)):43            controller_response = self.controller.update(errors[i-1], delta_t = self.delta_t)44            states[i] = self.cur_process.update(controller_response, states[i-1])45            errors[i] = error(states[i], self.set_states[i])46        47        errortotal = np.sum(abs(errors))48        plt.plot(states, color='blue', label='environment state')49        plt.plot(self.set_states, color='red', label = 'set (desired) state')50        plt.plot([], color='none', label = 'total error: ' + str(errortotal)[0:9])51        plt.xlabel('Time')52        plt.ylabel('Error')53        plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)...

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