def main():
bots = [MyBot()]
bots.extend(other_bots.get_bots(5, 2))
# Plot a single run. Useful for debugging and visualizing your
# bot's performance. Also prints the bot's final profit, but this
# will be very noisy.
#plot_simulation.run(bots, lmsr_b=250)
# Calculate statistics over many runs. Provides the mean and
# standard deviation of your bot's profit.
run_experiments.run(bots, simulations=1000, lmsr_b=250)
def main():
bots = [MyBot()]
bots.extend(other_bots.get_bots(5, 2))
# Plot a single run. Useful for debugging and visualizing your
# bot's performance. Also prints the bot's final profit, but this
# will be very noisy.
#plot_simulation.run(bots, lmsr_b=150, timesteps=100)
# Calculate statistics over many runs. Provides the mean and
# standard deviation of your bot's profit.
run_experiments.run(bots, simulations=1000, lmsr_b=150, num_processes=2, timesteps=100)
def main():
bots = [MyBot()]
# 5,2 to start
num_fundamentals = 8
num_technical = 2
bots.extend(other_bots.get_bots(num_fundamentals, num_technical))
# Plot a single run. Useful for debugging and visualizing your
# bot's performance. Also prints the bot's final profit, but this
# will be very noisy.
plot_simulation.run(bots, lmsr_b=250)
# Calculate statistics over many runs. Provides the mean and
# standard deviation of your bot's profit.
run_experiments.run(bots, num_processes=4, simulations=1000, lmsr_b=250)
def main():
bots = [MyBot()]
fundamental = 10
technical = 1
bots.extend(other_bots.get_bots(fundamental,technical))
print ('Fundamental: {}, Technical: {}'.format(fundamental,technical))
# Plot a single run. Useful for debugging and visualizing your
# bot's performance. Also prints the bot's final profit, but this
# will be very noisy.
#plot_simulation.run(bots, 200, lmsr_b=250)
# Calculate statistics over many runs. Provides the mean and
# standard deviation of your bot's profit.
run_experiments.run(bots, num_processes=4, simulations=2000, lmsr_b=250)
def run_simulationsHIDDEN(env,f_causal,f_common,f_hidden,methods,n_repeats):
N = 200
D = 10
opts = {}
if methods=='ALL':
methods = ['CV_GPbase_LIN','CV_GPkronprod_LIN','CV_GPkronsum_LIN','CV_GPpool_LIN','CV_GPpool_LIN']
opts['n_c'] = 1
opts['n_sigma'] = 1
opts['nfolds'] = 10
opts['standardizedX'] = False
opts['standardizedY'] = False
opts['min_iter'] = 5
opts['max_iter'] = 200
opts['maf'] = None
out_dir = os.path.join(env['out_dir'],'simulations_hidden')
if not os.path.exists(out_dir):
os.makedirs(out_dir)
fn_out = os.path.join(out_dir,'results_N%d_D%d_causal%02d_common%02d_hidden%02d.hdf5'%(N,D,10*f_causal,10*f_common,10*f_hidden))
f = h5py.File(fn_out,'w')
for rep_id in range(n_repeats):
# load data
data,RV = load_simulationsHIDDEN(env,N,D,f_causal,f_common,f_hidden,rep_id)
# run experiments
SP.random.seed(0)
out = f.create_group('rep%d'%rep_id)
try:
run_experiments.run(methods,data,opts,out)
except:
print 'ouch, something is wrong...'
f.close()