def evaluteFrontier(self,parent,utilities,tfam,pca,cmodels):
from SongData import SongData, findevent
from generate_random_policy import updateNodeAndFeature
terminus = None
candidates = []
prospects = copy.deepcopy(parent.transformedprospects)
for action in prospects.keys():
candidate = copy.deepcopy(parent)
candidate.generatedbyaction = action
event = findevent(action, candidate.context.songPitchMap.getPitchMax(SongData.REST))
updateNodeAndFeature(candidate,event,tfam,pca,cmodels)
if action == SongData.END:
terminus = candidate
elif candidate.transformedfeature not in utilities[candidate.feature.vector[0]]:
utilities[candidate.feature.vector[0]][candidate.transformedfeature] = 0
candidates.append(candidate)
reward = max([utilities[c.feature.vector[0]][c.transformedfeature] for c in candidates if c.feature != SongData.END])
maxlist = [(c,prospects[c.generatedbyaction]) for c in candidates if c.feature!=SongData.END and utilities[c.feature.vector[0]][c.transformedfeature]==reward]
#if tie for best action, draw based on corpus incidence, otherwise list will only have one element anywayz
drawbest= lambda s : random.choice(sum(([v]*wt for v,wt in s),[]))
argcand = drawbest(maxlist)
if terminus is not None:
total_prospects = sum([prospects[pro] for pro in prospects])
if np.random.uniform(0,1) < float(prospects[SongData.END])/total_prospects:
reward, argcand = 0, terminus
return reward, argcand
#def actions(self, feature):
# if state in self.terminals:
# return [None]
# else:
# total = sum([ tfam[state.feature][action] for action in tfam[state.feature].keys() ])
# return [ [float(count) / total, nextstate(state, action)] for action in tfam[state.feature].keys() ]
def value_iteration(mdp, tfam,pca,cmodels,epsilon = 0.001, MIN_INNER_ITERATIONS = 10000):
"""Solving an MDP by value iteration. [Fig. 17.4]"""
from generate_random_policy import updateNodeAndFeature, initializetrajectory
from SongData import SongData
new_utilities = dict()
new_utilities[0]= dict()
new_utilities[1] = dict()
R, gamma = mdp.R, mdp.gamma
s_events = initializetrajectory(mdp.startstates)
while True:
print 'OUTER LOOP OF VALUE ITERATION--'
old_utilities = copy.deepcopy(new_utilities)
start, event, parent = s_events
updateNodeAndFeature(parent,event,tfam,pca,cmodels)
delta = 0
iteration = 0
#walk the space, updating each state by its most lucrative neighbor and following same trajectory
while parent.generatedbyaction != SongData.END:
iteration+=1
maximum_value_action, child = mdp.evaluteFrontier(parent,old_utilities,tfam,pca,cmodels)
originalfeature=parent.feature.vector
parity = originalfeature[0]
tfeat = tuple(pca.transform(originalfeature).reshape(pca.n_components))
featurecluster = np.asscalar(clustermodelheuristic(originalfeature,cmodels).predict(tfeat))
new_utilities[parity][featurecluster] = R(tfeat) + gamma * maximum_value_action
if featurecluster not in old_utilities[parity]:
old_utilities[parity][featurecluster] = 0
delta = max(delta, abs(new_utilities[parity][featurecluster] - old_utilities[parity][featurecluster] ))
parent = child
print('iteration #:' + str(iteration))
print '\tmax delta: ' + str(delta)
if delta < epsilon * (1 - gamma) / gamma:
return old_utilities
