def __init__(
self,
conf): # idim = 1, odim = 1, numepisodes = 1, visualize = False):
"""smpModel.__init__
init
"""
defaults = {
'idim': 1,
'odim': 1,
}
set_attr_from_dict(self, conf)
self.model = None
# FIXME: variables for all models
# X, Y
# e, perf, loss
# dw, |W|
# self.idim = idim
# self.odim = odim
# self.numepisodes = numepisodes
# self.visualize = visualize
if self.visualize:
plt.ion()
self.figs = []
# store data for plotting, another internal model
self.Xhist = []
self.Yhist = []
self.visualize_model_init()
def __init__(self, conf):
# if len(conf['mdls']) > 1:
# set_attr_from_dict(self, conf['mdls'])
k1 = conf['mdls'].keys()[0]
set_attr_from_dict(self, conf['mdls'][k1])
def __init__(self, conf):
set_attr_from_dict(self, conf)
self.isrunning = True
# self.name = "lpzros"
self.mode = HK.modes[self.mode]
self.cnt = 0
############################################################
# model + meta params
self.numsen_raw = self.idim
self.numsen = self.idim
self.nummot = self.odim
# buffer size accomodates causal minimum 1 + lag time steps
self.bufsize = self.maxlag + 1 # 1 + self.robot.lag # 2
# self.creativity = 0.5
# self.epsA = 0.2
# self.epsA = 0.02
# self.epsA = 0.001
# self.epsC = 0.001
# self.epsC = 0.001
# self.epsC = 0.01
# self.epsC = 0.1
# self.epsC = 0.3
# self.epsC = 0.5
# self.epsC = 0.9
# self.epsC = 1.0
# self.epsC = 2.0
############################################################
# forward model
# self.A = np.eye(self.numsen) * 1.
self.A = np.zeros((self.numsen, self.nummot))
# self.A[range(self.nummot),range(self.nummot)] = 1. # diagonal
self.b = np.zeros((self.numsen, 1))
# controller
# self.C = np.eye(self.nummot) * 0.4
self.C = np.zeros((self.nummot, self.numsen))
self.C[list(range(self.nummot)), list(range(self.nummot))] = 1 # * 0.4
# self.C = np.random.uniform(-1e-2, 1e-2, (self.nummot, self.numsen))
# print "models_selforg.py: HK.__init__: self.C", self.C
self.h = np.zeros((self.nummot, 1))
self.g = np.tanh # sigmoidal activation function
self.g_ = dtanh # derivative of sigmoidal activation function
# state
self.x = np.ones((self.numsen, self.bufsize))
self.y = np.zeros((self.nummot, self.bufsize))
self.z = np.zeros((self.numsen, 1))
# auxiliary variables
# self.L = np.zeros((self.numsen, self.nummot))
self.v = np.zeros((self.numsen, 1))
self.v_avg = np.zeros((self.numsen, 1))
self.xsi = np.zeros((self.numsen, 1))
self.imu_vec = np.zeros((3 + 3 + 3, 1))
self.imu_smooth = 0.8 # coef