from explauto.environment import environments

env_cls, env_configs, _ = environments['simple_arm']
print env_configs.keys()

from explauto.experiment import Experiment, make_settings

s = make_settings(environment='simple_arm',
                  babbling_mode='motor', 
                  interest_model='random',
                  sensorimotor_model='nearest_neighbor',
                  environment_config='high_dimensional')

motor_xp = Experiment.from_settings(s)

motor_xp.evaluate_at([1, 10, 20, 30, 100, 200, 300, 400], s.default_testcases)
motor_xp.run()

from explauto.experiment import Experiment

s = make_settings(environment='simple_arm',
                  babbling_mode='goal', 
                  interest_model='random',
                  sensorimotor_model='nearest_neighbor',
                  environment_config='high_dimensional')

goal_xp = Experiment.from_settings(s)

goal_xp.evaluate_at([1, 10, 20, 30, 100, 200, 300, 400], s.default_testcases)
goal_xp.run()

%pylab inline

ax_motor = subplot(121)
ax_motor.axis([0, 1, -1, 1])
data = motor_xp.log.scatter_plot(ax_motor, (('sensori', [0, 1]), ), color='green')
legend(('motor', ))

ax_goal = subplot(122)
ax_goal.axis([0, 1, -1, 1])
data = goal_xp.log.scatter_plot(ax_goal, (('sensori', [0, 1]), ), color='red')
legend(('goal', ))

ax = axes()
motor_xp.log.plot_learning_curve(ax)
goal_xp.log.plot_learning_curve(ax)

legend(('motor', 'goal'))

ax_goal = subplot(122)
ax_goal.axis([0, 1, -1, 1])
data = goal_xp.log.scatter_plot(ax_goal, (('choice', [0, 1]), ), color='green')
data = goal_xp.log.scatter_plot(ax_goal, (('sensori', [0, 1]), ), color='red')