def test_with_twitter_data():
max_tweets = 100
data = read_twitter_data("../tweets.csv",max_tweets) #threshold on maximum tweets, > 500 gets slower due to there is a lot of words.
#That should be improved somehow in order to visualize data in a better way.
print "finish reading data"
adj = build_distance(data)
print "finish building distance"
matrix,labels = build_adjacency_matrix(adj)
k = 10
clusters = single_link_clustering(matrix,100)
colors = build_colors(clusters)
plot_helper.plot(matrix,None, colors)
def train():
plot_scores = []
plot_mean_scores = []
total_score = 0
record = 0
agent = Agent()
game = SnakeGameAI()
while True:
# get old state
state_old = agent.get_state(game)
# get move
final_move = agent.get_action(state_old)
# perform move and get new state
reward, done, score = game.play_step(final_move)
state_new = agent.get_state(game)
# train short memory
agent.train_short_memory(state_old, final_move, reward, state_new,
done)
# remember
agent.remember(state_old, final_move, reward, state_new, done)
if done:
# train long memory, plot result
game.reset()
agent.n_games += 1
agent.train_long_memory()
if score > record:
record = score
agent.model.save()
print('Game', agent.n_games, 'Score', score, 'Record:', record)
plot_scores.append(score)
total_score += score
mean_score = total_score / agent.n_games
plot_mean_scores.append(mean_score)
plot(plot_scores, plot_mean_scores)
print("===Using saved trials")
xstar_firsts_lst = read_saved_data_from_file(
"xstar_firsts_lst_ci_{}e{}.txt".format(scenario_name, inflation))
xstar_totals_lst = read_saved_data_from_file(
"xstar_totals_lst_ci_{}e{}.txt".format(scenario_name, inflation))
eager_xstar_firsts_lst = read_saved_data_from_file(
"eager_xstar_firsts_lst_ci_{}e{}.txt".format(scenario_name, inflation))
eager_xstar_totals_lst = read_saved_data_from_file(
"eager_xstar_totals_lst_ci_{}e{}.txt".format(scenario_name, inflation))
#plot_helper.plot_ci(kNumRobotsList, xstar_firsts_lst, 3, "X* First Search")
#plot_helper.plot_ci(kNumRobotsList, xstar_totals_lst, 3, "X* Full Search")
#plot_helper.plot_ci(kNumRobotsList, mstar_totals_lst, 3, "M* First/Full Search")
plot_helper.plot(
kNumRobotsList,
get_percent_different(eager_xstar_firsts_lst, xstar_firsts_lst), 2,
"First Solution Improvement")
plot_helper.plot(
kNumRobotsList,
get_percent_different(eager_xstar_totals_lst, xstar_totals_lst), 2,
"Opt. Solution Improvement")
def to_percent(y, position):
# Ignore the passed in position. This has the effect of scaling the default
# tick locations.
s = str(100 * y)
# The percent symbol needs escaping in latex
if matplotlib.rcParams['text.usetex'] is True:
return s + r'$\%$'
scenario_name, inflation),
nwastar_totals_lst)
else:
print("===Using saved trials")
xstar_firsts_lst = read_saved_data_from_file(
"xstar_firsts_lst_{}_{}e{}.txt".format(experiment_name, scenario_name,
inflation))
xstar_totals_lst = read_saved_data_from_file(
"xstar_totals_lst_{}_{}e{}.txt".format(experiment_name, scenario_name,
inflation))
nwastar_firsts_lst = read_saved_data_from_file(
"nwastar_firsts_lst_{}_{}e{}.txt".format(experiment_name,
scenario_name, inflation))
nwastar_totals_lst = read_saved_data_from_file(
"nwastar_totals_lst_{}_{}e{}.txt".format(experiment_name,
scenario_name, inflation))
plot_helper.plot(kAgentCount, xstar_firsts_lst, 4, "X* First Search")
plot_helper.plot(kAgentCount, xstar_totals_lst, 4, "X* Full Search")
plot_helper.plot(kAgentCount, nwastar_firsts_lst, 4, "NWA* First Search")
plot_helper.plot(kAgentCount, nwastar_totals_lst, 4, "NWA* Full Search")
plt.ylim(bottom=0)
plt.xticks(kAgentCount)
plt.xlabel("Agent Count")
plt.ylabel("Number of Expansions")
#plt.yscale('log')
plt.legend(loc=2)
plt.savefig("{}_{}_{}.png".format(experiment_name, scenario_name, inflation),
bbox_inches='tight')
plt.show()
xstar_totals_lst = []
if kRunNewTrials:
print("===Running new trials")
for inflation in kInflationValues:
seed = int(inflation * 100 * 53 + 17)
print("Inflation:", inflation)
print("X*")
xstar_first_exps, xstar_total_exps = run_xstar(num_robots, inflation)
xstar_firsts_lst.append(xstar_first_exps)
xstar_totals_lst.append(xstar_total_exps)
save_data_to_file("xstar_firsts_lst_{}_{}e{}.txt".format(experiment_name, scenario_name, inflation), xstar_firsts_lst)
save_data_to_file("xstar_totals_lst_{}_{}e{}.txt".format(experiment_name, scenario_name, inflation), xstar_totals_lst)
else:
inflation = kInflationValues[-1]
print("===Using saved trials")
xstar_firsts_lst = read_saved_data_from_file("xstar_firsts_lst_{}_{}e{}.txt".format(experiment_name, scenario_name, kInflationValues[-1]))
xstar_totals_lst = read_saved_data_from_file("xstar_totals_lst_{}_{}e{}.txt".format(experiment_name, scenario_name, kInflationValues[-1]))
plot_helper.plot(kInflationValues, xstar_firsts_lst, 2, "X* First Solution")
plot_helper.plot(kInflationValues, xstar_totals_lst, 2, "X* Full Solution")
plt.ylim(bottom=0)
plt.xticks(kInflationValues)
plt.xlabel("Heuristic Inflation")
plt.ylabel("State Expansions")
#plt.yscale('log')
plot_helper.legend(loc=1)
plot_helper.save_fig("xstar_heuristic_inflation")
plt.show()
xstar_firsts_lst = []
xstar_totals_lst = []
if kRunNewTrials:
print("===Running new trials")
for num_robots in kAgentCount:
seed = int(num_robots * 100 * 53 + 17)
print("Agent Count:", num_robots)
print("X*")
xstar_first_exps, xstar_total_exps = run_xstar(num_robots)
xstar_firsts_lst.append(xstar_first_exps)
xstar_totals_lst.append(xstar_total_exps)
save_data_to_file("xstar_firsts_lst_{}_{}e{}.txt".format(experiment_name, scenario_name, inflation), xstar_firsts_lst)
save_data_to_file("xstar_totals_lst_{}_{}e{}.txt".format(experiment_name, scenario_name, inflation), xstar_totals_lst)
else:
print("===Using saved trials")
xstar_firsts_lst = read_saved_data_from_file("xstar_firsts_lst_{}_{}e{}.txt".format(experiment_name, scenario_name, inflation))
xstar_totals_lst = read_saved_data_from_file("xstar_totals_lst_{}_{}e{}.txt".format(experiment_name, scenario_name, inflation))
plot_helper.plot(kAgentCount, xstar_firsts_lst, 2, "X* First Solution")
plot_helper.plot(kAgentCount, xstar_totals_lst, 2, "X* Opt. Solution")
plt.ylim(bottom=5)
plt.xticks(kAgentCount)
plt.xlabel("Agent Count")
plt.ylabel("Number of Expansions")
plt.yscale('log')
plot_helper.legend(loc=2)
plot_helper.save_fig("xstar_agent_sweep")
plt.show()
xstar_firsts_lst = []
xstar_totals_lst = []
if kRunNewTrials:
print("===Running new trials")
for radius in kRadiusValues:
seed = int(radius * 100 * 53 + 17)
print("radius:", radius)
print("X*")
xstar_first_exps, xstar_total_exps = run_xstar(num_robots, radius)
xstar_firsts_lst.append(xstar_first_exps)
xstar_totals_lst.append(xstar_total_exps)
save_data_to_file("xstar_firsts_lst_{}_{}e{}.txt".format(experiment_name, scenario_name, inflation), xstar_firsts_lst)
save_data_to_file("xstar_totals_lst_{}_{}e{}.txt".format(experiment_name, scenario_name, inflation), xstar_totals_lst)
else:
print("===Using saved trials")
xstar_firsts_lst = read_saved_data_from_file("xstar_firsts_lst_{}_{}e{}.txt".format(experiment_name, scenario_name, inflation))
xstar_totals_lst = read_saved_data_from_file("xstar_totals_lst_{}_{}e{}.txt".format(experiment_name, scenario_name, inflation))
plot_helper.plot(kRadiusValues, xstar_firsts_lst, 2, "X* First Solution")
plot_helper.plot(kRadiusValues, xstar_totals_lst, 2, "X* Opt. Solution")
plt.ylim(bottom=0)
plt.xticks(kRadiusValues)
plt.xlabel("Radius Values")
plt.ylabel("State Expansions")
#plt.yscale('log')
plot_helper.legend(loc=4)
plot_helper.save_fig("xstar_radius_sweep")
plt.show()
save_data_to_file(
"final_percentage_iterations_{}.txt".format(experiment_name,
scenario_name, inflation),
final_percentage_iterations)
else:
print("===Using saved trials")
final_precentage_solutions = read_saved_data_from_file(
"xstar_soltion_quality_{}.txt".format(experiment_name, scenario_name,
inflation))
final_percentage_iterations = read_saved_data_from_file(
"final_percentage_iterations_{}.txt".format(experiment_name,
scenario_name, inflation))
xs = list(range(1, len(final_precentage_solutions) + 1))
plot_helper.plot(xs, final_percentage_iterations, 2, None)
def to_percent(y, position):
# Ignore the passed in position. This has the effect of scaling the default
# tick locations.
s = str(100 * y)
# The percent symbol needs escaping in latex
if matplotlib.rcParams['text.usetex'] is True:
return s + r'$\%$'
else:
return s + '%'
plt.ylim(bottom=0)
save_data_to_file(
"xstar_totals_lst_{}_{}e{}.txt".format(experiment_name, scenario_name,
inflation), xstar_totals_lst)
save_data_to_file(
"astar_totals_lst_{}_{}e{}.txt".format(experiment_name, scenario_name,
inflation), astar_totals_lst)
else:
print("===Using saved trials")
xstar_firsts_lst = read_saved_data_from_file(
"xstar_firsts_lst_{}_{}e{}.txt".format(experiment_name, scenario_name,
inflation))
xstar_totals_lst = read_saved_data_from_file(
"xstar_totals_lst_{}_{}e{}.txt".format(experiment_name, scenario_name,
inflation))
astar_totals_lst = read_saved_data_from_file(
"astar_totals_lst_{}_{}e{}.txt".format(experiment_name, scenario_name,
inflation))
plot_helper.plot(kAgentCount, xstar_firsts_lst, 3, "X* First Search")
plot_helper.plot(kAgentCount, xstar_totals_lst, 3, "X* Full Search")
plot_helper.plot(kAgentCount, astar_totals_lst, 3, "A* Full Search")
plt.ylim(bottom=0)
plt.xticks(kAgentCount)
plt.xlabel("Agent Count")
plt.ylabel("Number of Expansions")
#plt.yscale('log')
plt.legend(loc=2)
plt.savefig("{}_{}_{}.png".format(experiment_name, scenario_name, inflation),
bbox_inches='tight')
plt.show()
