def main(out_path, values_path, neighbors = 8, workers = 0):
"""Run TTT state-clustering experiment(s)."""
# load the value function
with specmine.openz(values_path) as values_file:
values = pickle.load(values_file)
# convert states to their vector representations
states_adict = specmine.tictac.load_adjacency_dict()
states = list(states_adict)
logger.info("converting states to their vector representation")
affinity_index = dict(zip(states, xrange(len(states))))
vectors_ND = numpy.array(map(raw_state_features, states))
# build the affinity graph
affinity_NN = specmine.discovery.affinity_graph(vectors_ND, neighbors)
(gameplay_NN, gameplay_index) = specmine.discovery.adjacency_dict_to_matrix(states_adict)
def yield_jobs():
for B in numpy.r_[0:400:64j].astype(int):
yield (run_random_features, [B, vectors_ND, affinity_index, values])
yield (run_graph_features, ["gameplay", B, vectors_ND, gameplay_NN, gameplay_index, values])
yield (run_graph_features, ["affinity", B, vectors_ND, affinity_NN, affinity_index, values])
with open(out_path, "wb") as out_file:
writer = csv.writer(out_file)
writer.writerow(["map_name", "features", "score_mean", "score_variance"])
condor.do_or_distribute(yield_jobs(), workers, lambda _, r: writer.writerow(r))
def main(workers=20):
print 'creating domain and opponent'
pickle_path = specmine.util.static_path("ttt_states.pickle.gz")
with specmine.util.openz(pickle_path) as pickle_file:
adj_dict = pickle.load(pickle_file)
adj_matrix, index = specmine.discovery.adjacency_dict_to_matrix(adj_dict)
w = csv.writer(file(specmine.util.static_path( \
'feature_number_test_td.csv'),'wb'))
w.writerow(['method','features','reward_mean','reward_variance'])
def yield_jobs():
for k in numpy.array(range(1,11))*50:
yield (run_laplacian_evaluation, [k, adj_matrix, index])
yield (run_random_evaluation, [k, adj_matrix, index])
condor.do_or_distribute(yield_jobs(), workers, lambda _, r: w.writerow(r))
def main(workers=20):
print 'creating domain and opponent'
pickle_path = specmine.util.static_path("ttt_states.pickle.gz")
with specmine.util.openz(pickle_path) as pickle_file:
adj_dict = pickle.load(pickle_file)
adj_matrix, index = specmine.discovery.adjacency_dict_to_matrix(adj_dict)
w = csv.writer(file(specmine.util.static_path( \
'feature_number_test_td.csv'),'wb'))
w.writerow(['method', 'features', 'reward_mean', 'reward_variance'])
def yield_jobs():
for k in numpy.array(range(1, 11)) * 50:
yield (run_laplacian_evaluation, [k, adj_matrix, index])
yield (run_random_evaluation, [k, adj_matrix, index])
condor.do_or_distribute(yield_jobs(), workers, lambda _, r: w.writerow(r))
def main(out_path, values_path, neighbors=8, workers=0):
"""Run TTT state-clustering experiment(s)."""
# load the value function
with specmine.openz(values_path) as values_file:
values = pickle.load(values_file)
# convert states to their vector representations
states_adict = specmine.tictac.load_adjacency_dict()
states = list(states_adict)
logger.info("converting states to their vector representation")
affinity_index = dict(zip(states, xrange(len(states))))
vectors_ND = numpy.array(map(raw_state_features, states))
# build the affinity graph
affinity_NN = specmine.discovery.affinity_graph(vectors_ND, neighbors)
(gameplay_NN, gameplay_index
) = specmine.discovery.adjacency_dict_to_matrix(states_adict)
def yield_jobs():
for B in numpy.r_[0:400:64j].astype(int):
yield (run_random_features,
[B, vectors_ND, affinity_index, values])
yield (run_graph_features, [
"gameplay", B, vectors_ND, gameplay_NN, gameplay_index, values
])
yield (run_graph_features, [
"affinity", B, vectors_ND, affinity_NN, affinity_index, values
])
with open(out_path, "wb") as out_file:
writer = csv.writer(out_file)
writer.writerow(
["map_name", "features", "score_mean", "score_variance"])
condor.do_or_distribute(yield_jobs(), workers,
lambda _, r: writer.writerow(r))