In [11]:
%matplotlib inline
import pylab
import numpy as np
import functools
import adapt_float
import adapt_spike
import adapt_fixed_small
import ctn_benchmark.control as ctrl
In [72]:
def objective(args, adapt_type):
D = 1
dt=0.001
T = 20.0
seed=2
noise=0.1
Kp=args['Kp']
Kd=args['Kd']
Ki=args['Ki']
print 'trying', Kp, Kd, Ki
tau_d=0.001
period=4
amplitude=1
n_neurons=500
learning_rate=1
max_freq=1.0
synapse=0.01
scale_add=2
delay=0.01
filter=0.01
n_neurons=256
signal = ctrl.Signal(D, period, dt=dt, max_freq=max_freq, seed=seed)
system = ctrl.System(D, D, dt=dt, seed=seed,
motor_noise=noise, sense_noise=noise,
scale_add=scale_add,
motor_scale=10,
motor_delay=delay, sensor_delay=delay,
motor_filter=filter, sensor_filter=filter)
pid = ctrl.PID(Kp, Kd, Ki, tau_d=tau_d)
if adapt_type == 'float':
adapt = adapt_float.AdaptiveFloat(n_inputs=D, n_outputs=D, n_neurons=n_neurons, seed=seed,
learning_rate=1e-4)
scale = 1.0
elif adapt_type == 'fixed':
adapt = adapt_fixed_small.AdaptiveFixed(n_inputs=D, n_outputs=D, n_neurons=n_neurons, seed=seed,
input_bits=8,
state_bits=8,
extra_bits=4,
decoder_offset=4,
decoder_bits=8,
smoothing=10,
learning_rate=1e-4)
scale = adapt.input_max
else:
adapt = None
steps = int(T / dt)
time = np.arange(steps)*dt
data_desired = np.zeros((steps, D))
data_actual = np.zeros((steps, D))
data_pid = np.zeros((steps, D))
data_error = np.zeros((steps, D))
for i in range(steps):
desired = signal.value(i*dt)*amplitude
data_desired[i,:] = desired
actual = system.state
data_actual[i,:] = actual
raw_pid = pid.step(actual, desired)
data_pid[i,:] = raw_pid
if adapt is not None:
adjust = adapt.step(actual*scale, -raw_pid*scale)/scale
else:
adjust = 0
system.step(raw_pid + adjust)
#pylab.plot(data_desired)
#pylab.plot(data_actual)
rmse = np.sqrt(np.mean((data_desired - data_actual)**2))
return dict(
status='ok',
loss=rmse
)
In [59]:
objective(dict(Kp=10, Kd=2, Ki=0), adapt_type='none')
Out[59]:
In [73]:
objective(dict(Kp=10, Kd=2, Ki=0), adapt_type='fixed')
Out[73]:
In [61]:
objective(dict(Kp=10, Kd=2, Ki=0), adapt_type='float')
Out[61]:
In [42]:
import hyperopt as hp
space = dict(
Kp=hp.hp.lognormal('Kp', np.log(50), 1),
Kd=hp.hp.lognormal('Kd', np.log(20), 1),
Ki=hp.hp.lognormal('Ki', np.log(10), 1)
)
trials = hp.Trials()
In [54]:
import functools
best = hp.fmin(functools.partial(objective, adapt_type='none'), space=space, algo=hp.tpe.suggest, max_evals=100, trials=trials)
In [55]:
best
Out[55]:
In [99]:
loss = [t['result']['loss'] for t in trials.trials]
pylab.plot(loss)
pylab.xlabel('trial')
pylab.ylabel('rmse')
pylab.ylim(0, 0.15)
pylab.show()
print 'best rmse', np.min(loss)
In [93]:
limits = [1.0, 1.05, 1.1, np.inf]
limit_labels = ['<5%', '5-10%', '10%+']
best_rmse = np.min(loss)
data = []
for t in trials.trials:
d = {}
d['Kp'] = t['misc']['vals']['Kp'][0]
d['Kd'] = t['misc']['vals']['Kd'][0]
d['Ki'] = t['misc']['vals']['Ki'][0]
rate = t['result']['loss'] / best_rmse
index = 0
while limits[index+1] < rate:
index += 1
d['category'] = limit_labels[index]
data.append(d)
In [94]:
import pandas
import seaborn as sns
In [95]:
df = pandas.DataFrame(data)
In [97]:
grid = sns.pairplot(data=df, x_vars=['Kp', 'Kd', 'Ki'], y_vars=['Kp', 'Kd', 'Ki'], diag_kind='kde', hue='category', hue_order=limit_labels)
ax_lim=50
for i, row in enumerate(grid.axes):
for j, ax in enumerate(row):
if i==j:
ax.set_xlim(0, ax_lim)
else:
ax.set_xlim(0, ax_lim)
ax.set_ylim(0, ax_lim)
best_vals = best['Kp'], best['Kd'], best['Ki']
for i, row in enumerate(grid.axes):
for j, ax in enumerate(row):
if i==j:
ax.axvline(best_vals[i], c='k')
else:
ax.axvline(best_vals[j], c='k')
ax.axhline(best_vals[i], c='k')
In [ ]: