def __init__(self, decoding_rate=180):
'''
Constructor for OFCEndpointAssister
Parameters
----------
decoding_rate : int
Rate that the decoder should operate, in Hz. Should be a multiple or divisor of 60 Hz
Returns
-------
OFCEndpointAssister instance
'''
F_dict = pickle.load(
open('/storage/assist_params/assist_20levels_ppf.pkl'))
B = np.mat(
np.vstack([
np.zeros([3, 3]),
np.eye(3) * 1000 * 1. / decoding_rate,
np.zeros(3)
]))
fb_ctrl = feedback_controllers.MultiModalLFC(A=B, B=B, F_dict=F_dict)
super(OFCEndpointAssister, self).__init__(fb_ctrl,
style='additive_cov')
self.n_assist_levels = len(F_dict)
def create_learner(self):
self.learn_flag = True
kwargs = dict()
dt = kwargs.pop('dt', 1. / 180)
use_tau_unNat = self.tau
self.tau = use_tau_unNat
print "learner cost fn param: %g" % use_tau_unNat
tau_scale = 28 * use_tau_unNat / 1000
bin_num_ms = (dt / 0.001)
w_r = 3 * tau_scale**2 / 2 * (bin_num_ms)**2 * 26.61
I = np.eye(3)
zero_col = np.zeros([3, 1])
zero_row = np.zeros([1, 3])
zero = np.zeros([1, 1])
one = np.ones([1, 1])
A = np.bmat([[I, dt * I, zero_col], [0 * I, 0 * I, zero_col],
[zero_row, zero_row, one]])
B = np.bmat([[0 * I], [dt / 1e-3 * I], [zero_row]])
Q = np.mat(np.diag([1., 1, 1, 0, 0, 0, 0]))
R = np.mat(np.diag([w_r, w_r, w_r]))
F = feedback_controllers.LQRController.dlqr(A, B, Q, R)
F_dict = dict(target=F, hold=F)
fb_ctrl = feedback_controllers.MultiModalLFC(A=A, B=B, F_dict=F_dict)
batch_size = 1
self.learner = clda.OFCLearner(batch_size, A, B, F_dict)
# Tell BMISystem that this learner wants the most recent output
# of the decoder rather than the second most recent, to match MATLAB
self.learner.input_state_index = 0