).satisfiability_report() print Proposition( lambda a, b, c: (implies(a, b) or implies(b, c)) == implies(a, c), "((a => b) and (b => c)) == (a => c)" ).satisfiability_report() print "\n=== Problem 14 ===" from MDP.grid import GridWorld GRID = [[0, 0, None, 100], [0, 0, 0, 0]] PROB = { 'S':(('S', 1.0), ), 'N':(('N', 1.0), ), 'E':(('E', 1.0), ), 'W':(('W', 1.0), ), } STATES = ((1,3),(1,2),(1,1),(0,1),(1,0),(0,0)) g = GridWorld(GRID, PROB, STATES, 1, -5) i = g.value_iteration(0.1) print "Values after %d iterations:" % i print g print "\n=== Problem 15 ===" from Markov.markov import TransProb TRANSITIONS = [("A","A","A","A","B")] t = TransProb(TRANSITIONS) t.report(k=1)
from Markov.markov import TransProb, MarkovChain, MarkovModel print "\n=== Homework 6.1 ===" OBSERVATIONS = (("A", "B", "C", "A", "B", "C"), ("A", "A", "B", "B", "C", "C"), ("A", "A", "A", "C", "C", "C")) t = TransProb(OBSERVATIONS) t.report() print "\n=== Homework 6.2 ===" CHAIN = ("A", 1.0, {True: 0.9, False: 0.5}) c = MarkovChain(CHAIN) print "stationary distribution: P(A) = %.4f" % c.stationary_distribution(True) print "stationary distribution: P(B) = %.4f" % c.stationary_distribution(False) print "\n=== Homework 6.3 ===" MODEL = ("A", 0.5, {True: 0.5, False: 0.5}, "X", {True: 0.1, False: 0.8}) m = MarkovModel(MODEL) m.p({"A0": True}, {"X0": True}) m.p({"A1": True}, {"X0": True}) m.p({"A1": True}, {"X0": True, "X1": True}) print "\n=== Homework 6.11 ===" from Game.game import Game HW6_11 = { "players": (("B", "d", "e", "f"), ("A", "a", "b", "c")), "matrix": ( ((3, 3), (5, 0), (2, 1)), ((2, 4), (7, 8), (4, 6)), ((7, 5), (8, 5), (5, 3)), ) }
c.p({"R1":True}) c.p({"R2":True}) c.p({"R3":True}) print "stationary distribution: P(R) = %.4f" % c.stationary_distribution() print "\nA Example" A_CHAIN = ("A", 1.0, {True: 0.5, False: 1.0}) c = MarkovChain(A_CHAIN, 3) c.p({"A1":True}) c.p({"A2":True}) c.p({"A3":True}) print "stationary distribution: P(A) = %.4f" % c.stationary_distribution() print "\nTransition Probabilities 1" TRANSITIONS = [("R","S","S","S","R","S","R")] t = TransProb(TRANSITIONS) t.report() print "\nTransition Probabilities 2" TRANSITIONS = [("S","S","S","S","S","R","S","S","S","R","R")] t = TransProb(TRANSITIONS) t.report() print "\nTransition Probabilities 3" TRANSITIONS = [("R","S","S","S","S")] t = TransProb(TRANSITIONS) t.report(k=1) print "\nMarkov Model 1" MODEL = ("R", 0.5, {True: 0.6, False: 0.2}, "H", {True: 0.4, False: 0.9}) m = MarkovModel(MODEL)
from Markov.markov import TransProb, MarkovChain, MarkovModel print "\n=== Homework 6.1 ===" OBSERVATIONS = ( ("A", "B", "C", "A", "B", "C"), ("A", "A", "B", "B", "C", "C"), ("A", "A", "A", "C", "C", "C") ) t = TransProb(OBSERVATIONS) t.report() print "\n=== Homework 6.2 ===" CHAIN = ("A", 1.0, {True: 0.9, False: 0.5}) c = MarkovChain(CHAIN) print "stationary distribution: P(A) = %.4f" % c.stationary_distribution(True) print "stationary distribution: P(B) = %.4f" % c.stationary_distribution(False) print "\n=== Homework 6.3 ===" MODEL = ("A", 0.5, {True: 0.5, False: 0.5}, "X", {True: 0.1, False: 0.8}) m = MarkovModel(MODEL) m.p({"A0":True}, {"X0":True}) m.p({"A1":True}, {"X0":True}) m.p({"A1":True}, {"X0":True, "X1":True}) print "\n=== Homework 6.11 ===" from Game.game import Game HW6_11 = { "players": ( ("B", "d", "e", "f"), ("A", "a", "b", "c") ),