def __init__(self,
n_dmps,
n_bfs,
used,
default,
conf,
sm,
im,
dmp_type='discrete',
ay=None):
Agent.__init__(self, conf, sm, im)
self.n_dmps, self.n_bfs = n_dmps, n_bfs
self.current_m = zeros(self.conf.m_ndims)
if ay is None:
self.dmp = DmpPrimitive(n_dmps,
n_bfs,
used,
default,
type=dmp_type)
else:
self.dmp = DmpPrimitive(n_dmps,
n_bfs,
used,
default,
type=dmp_type,
ay=ones(n_dmps) * 1.)
def init_motor_DMP(self,
optim_initial_position=True,
optim_end_position=True,
default_motor_initial_position=None,
default_motor_end_position=None):
default = np.zeros(self.n_dynamic_motor_dims * (self.n_bfs + 2))
if not optim_initial_position:
default[:self.
n_dynamic_motor_dims] = default_motor_initial_position or [
0.
] * self.n_dynamic_motor_dims
dims_optim = [False] * self.n_dynamic_motor_dims
else:
dims_optim = [True] * self.n_dynamic_motor_dims
dims_optim += [True] * (self.n_dynamic_motor_dims * self.n_bfs)
if not optim_end_position:
default[-self.
n_dynamic_motor_dims:] = default_motor_end_position or [
0.
] * self.n_dynamic_motor_dims
dims_optim += [False] * self.n_dynamic_motor_dims
else:
dims_optim += [True] * self.n_dynamic_motor_dims
self.motor_dmp = DmpPrimitive(self.n_dynamic_motor_dims,
self.n_bfs,
dims_optim,
default,
type='discrete',
timesteps=self.move_steps)
def __init__(self,
synth,
m_mins,
m_maxs,
s_mins,
s_maxs,
m_used,
s_used,
n_dmps,
n_bfs,
dmp_move_steps,
dmp_max_param,
sensory_traj_samples,
audio,
diva_path=None):
self.m_mins = m_mins
self.m_maxs = m_maxs
self.s_mins = s_mins
self.s_maxs = s_maxs
self.m_used = m_used
self.s_used = s_used
self.n_dmps = n_dmps
self.n_bfs = n_bfs
self.dmp_move_steps = dmp_move_steps
self.dmp_max_param = dmp_max_param
self.samples = array(linspace(-1, self.dmp_move_steps - 1,
sensory_traj_samples + 1),
dtype=int)[1:]
self.audio = audio
self.f0 = 1.
self.pressure = 1.
self.voicing = 1.
if self.audio:
import pyaudio
self.pa = pyaudio.PyAudio()
self.stream = self.pa.open(format=pyaudio.paFloat32,
channels=1,
rate=11025,
output=True)
if synth == "octave":
self.synth = DivaOctaveSynth(diva_path)
elif synth == "matlab":
self.synth = DivaMatlabSynth()
else:
raise NotImplementedError
self.art = array(
[0.] * 10 + [self.f0, self.pressure, self.voicing]
) # 13 articulators is a constant from diva_synth.m in the diva source code
self.max_params = array([1.] * self.n_dmps + [self.dmp_max_param] *
self.n_bfs * self.n_dmps + [1.] * self.n_dmps)
self.dmp = DmpPrimitive(dmps=self.n_dmps,
bfs=self.n_bfs,
timesteps=self.dmp_move_steps)
Environment.__init__(self, self.m_mins, self.m_maxs, self.s_mins,
self.s_maxs)
def init_sensori_DMP(self,
optim_initial_position=True,
optim_end_position=True,
default_sensori_initial_position=None,
default_sensori_end_position=None):
default = np.zeros(self.n_dynamic_sensori_dims *
(self.n_sensori_traj_points + 2))
if not optim_initial_position:
default[:self.
n_dynamic_sensori_dims] = default_sensori_initial_position
dims_optim = [False] * self.n_dynamic_sensori_dims
else:
dims_optim = [True] * self.n_dynamic_sensori_dims
dims_optim += [True] * (self.n_dynamic_sensori_dims *
self.n_sensori_traj_points)
if not optim_end_position:
default[-self.
n_dynamic_sensori_dims:] = default_sensori_end_position
dims_optim += [False] * self.n_dynamic_sensori_dims
else:
dims_optim += [True] * self.n_dynamic_sensori_dims
self.sensori_dmp = DmpPrimitive(self.n_dynamic_sensori_dims,
self.n_sensori_traj_points,
dims_optim,
default,
type='discrete',
timesteps=self.move_steps)
