def learn(self):
args = {}
args['S'] = self.mdp.getStates()
args['A'] = self.mdp.getPossibleActions(self.mdp.state) # assume state actions are available for all states
def transition(s, a, sp):
trans = self.mdp.getTransitionStatesAndProbs(s, a)
trans = filter(lambda (state, prob): state == sp, trans)
if len(trans) > 0: return trans[0][1]
else: return 0
args['T'] = transition
args['r'] = self.mdp.getReward
args['s0'] = self.mdp.state
self.v = lp(**args)
def specific(G, best_val, k, s_max):
n = G.order()
happiness = {}
stress = {}
for i in range(n):
happiness[i] = {}
stress[i] = {}
for i in range(n):
for j in range(i + 1, n):
happiness[i][j] = G.get_edge_data(i, j)['happiness']
stress[i][j] = G.get_edge_data(i, j)['stress']
answer = best_val
best_k = -1
rooms = {}
if (n == 20 and k in [1, 2, 17, 18, 19]) or (n == 50 and k in [1, 48, 49]):
print("(", end='', flush=True)
val, arr = bruteforce.bruteforce_k(happiness, stress, n, s_max, k)
if val > answer:
print("*", end=",", flush=True)
answer = val
rooms = arr
best_k = k
else:
print("X", end=",", flush=True)
print(")", end="", flush=True)
return answer, rooms, best_k
#prune
pruned = {}
for u in range(n):
for v in range(u + 1, n):
if stress[u][v] > s_max / k:
pruned[(u, v)] = stress[u][v] #add pair to pruned
# print(pruned)
print("(", end="", flush=True)
#auto optimize
val, arr, not_optimal = lp.lp(happiness, stress, s_max, n, k, answer,
pruned)
if val > answer:
print("*", end="", flush=True)
answer = val
rooms = arr
best_k = k
else:
print("X", end="", flush=True)
print(")", end=" ", flush=True)
return answer, rooms, best_k
def lp_cutoff(happiness, stress, s_max, n, cutoff):
answer = cutoff
best_k = -1
rooms = {}
#if n = 20, brute force 1,2,17,18,19
#if n = 50, brute force 1,48,49
#if n <= 10, don't brute force anything (for testing purposes)
#always brute force k = 1, n
bruteforce_nums = []
suboptimal = []
if n == 20:
bruteforce_nums = [1, 2, 17, 18, 19]
elif n == 50:
bruteforce_nums = [1, 48, 49]
nonbruteforce_nums = [i for i in range(1, n) if i not in bruteforce_nums]
print("Bruteforce...", end=" ", flush=True)
for k in bruteforce_nums:
val, arr = bruteforce.bruteforce_k(happiness, stress, n, s_max, k)
if val > answer:
print("*", end="", flush=True)
answer = val
rooms = arr
best_k = k
print("", end=" ", flush=True)
print()
print("Gurobi...", end=" ", flush=True)
for k in nonbruteforce_nums:
#prune
pruned = {}
for u in range(n):
for v in range(u + 1, n):
if stress[u][v] > s_max / k:
pruned[(u, v)] = stress[u][v] #add pair to pruned
print("(", end="", flush=True)
val, arr, not_optimal = lp.lp(happiness, stress, s_max, n, k, answer,
pruned)
if not_optimal:
suboptimal.append(k)
if val > answer:
#print(val, arr)
print("*", end="", flush=True)
answer = val
rooms = arr
best_k = k
print(")", end=" ", flush=True)
print()
print("SUBOPTIMAL K:", suboptimal)
return answer, rooms, best_k
def learn(self):
args = {}
args['S'] = self.mdp.getStates()
args['A'] = self.mdp.getPossibleActions(
self.mdp.state
) # assume state actions are available for all states
def transition(s, a, sp):
trans = self.mdp.getTransitionStatesAndProbs(s, a)
trans = filter(lambda (state, prob): state == sp, trans)
if len(trans) > 0: return trans[0][1]
else: return 0
args['T'] = transition
args['r'] = self.mdp.getReward
args['s0'] = self.mdp.state
self.v = lp(**args)
"--algorithm",
help=
"Algorithm - vi, hpi or lp - to calculate optimal value and policy.",
required=True)
args = parser.parse_args()
# Read the mdp file
numStates, numActions, startState, endStates, transitions, mdpType, discount = helper.readMdpFile(
args.mdp)
# print(f'Number of states: {numStates}')
# print(f'Number of actions: {numActions}')
V, policy = None, None
# Call to the respective function
if args.algorithm == 'vi':
V, policy = vi(numStates, numActions, startState, endStates,
transitions, mdpType, discount)
elif args.algorithm == 'hpi':
V, policy = hpi(numStates, numActions, startState, endStates,
transitions, mdpType, discount)
elif args.algorithm == 'lp':
V, policy = lp(numStates, numActions, startState, endStates,
transitions, mdpType, discount)
else:
raise ValueError(
f'Illegal arguments specified for algorithm - {args.algorithm}')
helper.printValuePolicy(V, policy)
import numpy as np import lp A = np.array([[1., 1.], [16., 8.], [9000., 5000.]]) b = np.array([44., 512., 300000.]) c = np.array([30000., 20000.]) optx, zmin, is_bounded, sol, basis = lp.lp(c, A, b) print basis print zmin print optx
import numpy as np import lp A = np.array([[1., 1.],[16., 8.],[9000., 5000.]]) b = np.array([44., 512., 300000.]) c = np.array([30000., 20000.]) optx,zmin,is_bounded,sol,basis = lp.lp(c,A,b) print zmin print optx