from explauto.experiment import ExperimentPool

xps = ExperimentPool.from_settings_product(environments=[('simple_arm', 'high_dimensional')],
                                           babblings=['motor', 'goal'],
                                           interest_models=[('random', 'default')],
                                           sensorimotor_models=[('nearest_neighbor', 'default')],
                                           evaluate_at=[10, 20, 30, 50, 100, 200, 300, 400],
                                           same_testcases=True)

for i, xp_settings in enumerate(xps.settings):
    
    print """Xp #{}:
        env='{self.environment}' conf='{self.environment_config}'
        babbling mode='{self.babbling_mode}' 
        interest model='{self.interest_model}' conf='{self.interest_model_config}'
        sensorimotor model='{self.sensorimotor_model}' conf='{self.sensorimotor_model_config}'""".format(i, self=xp_settings)

logs = xps.run()

%pylab inline

ax = axes()

for log in xps.logs:
    log.plot_learning_curve(ax)
    
legend(('motor', 'goal'))

colors = ('r', 'g', 'b')

for i, (config, log) in enumerate(zip(xps.settings, xps.logs)):    
    plot_index = 120 + i + 1
    ax_motor = subplot(plot_index)
    ax_motor.axis([0, 1, -1, 1])
    log.scatter_plot(ax_motor, (('sensori', [0, 1]), ), color=colors[i])
    
    legend([config.babbling_mode])

%load exercise_solutions/running_experiment_pool__pool.py