def __init__(self, c, source):
"""
Initialize the variables of SORN
:param c: Bunch(), the bunch of parameters
:param source: the input source
"""
self.c = c
self.source = source
# Initialize weight matrix
# W_oe: connections from excitatory to output(excitatory) neurons.
self.W_ie = create_matrix((c.N_i, c.N_e), c.W_ie)
self.W_ei = create_matrix((c.N_e, c.N_i), c.W_ei)
self.W_ee = create_matrix((c.N_e, c.N_e), c.W_ee)
self.W_oe = create_matrix((c.N_o, c.N_e), c.W_oe)
self.W_eu = self.source.generate_connection_e(c.N_e)
self.W_iu = self.source.generate_connection_i(c.N_i)
self.x = np.random.rand(c.N_e) < c.h_ip_e
self.y = np.zeros(c.N_i)
self.o = np.zeros(c.N_o)
# Initialize the Modulation-Factors
self.m_r = 1 # reward modulation factor of recurrent layer
self.m_o = 1 # reward modualtion factor of readout layer
self.r = 0 # reward
self.reward = deque(maxlen=c.window_size) # helper list to record
self.output = deque(maxlen=c.N_o) # helper list to record
# Initialize output
self.output.append(0)
# Initialize the pre-threshold variables
self.R_x = np.zeros(c.N_e)
self.R_y = np.zeros(c.N_i)
self.R_o = np.zeros(c.N_o)
if c.ordered_thresholds:
self.T_i = (np.arange(c.N_i) + 0.5) * ((c.T_i_max - c.T_i_min) /
(1. * c.N_i)) + c.T_i_min
self.T_e = (np.arange(c.N_e) + 0.5) * ((c.T_e_max - c.T_e_min) /
(1. * c.N_e)) + c.T_e_min
self.T_o = (np.arange(c.N_o) + 0.5) * ((c.T_o_max - c.T_o_min) /
(1. * c.N_o)) + c.T_o_min
np.random.shuffle(self.T_e)
np.random.shuffle(self.T_o)
else:
self.T_i = c.T_i_min + np.random.rand(
c.N_i) * (c.T_i_max - c.T_i_min)
self.T_e = c.T_e_min + np.random.rand(
c.N_e) * (c.T_e_max - c.T_e_min)
self.T_o = c.T_o_min + np.random.rand(
c.N_o) * (c.T_o_max - c.T_o_min)
# Seperately activate the plasticity mechanism in Recurrent and Output.
self.update = True
self.display = False
def __init__(self, c, source):
"""
Initializes the variables of SORN
:param c: Bunch(), the bunch of parameters
:param source: the input source
"""
self.c = c
self.source = source
# Initialize weight matrix
# W_to_from (W_ie = from excitatory to inhibitory)
self.W_ie = create_matrix((c.N_i, c.N_e), c.W_ie)
self.W_ei = create_matrix((c.N_e, c.N_i), c.W_ei)
self.W_ee = create_matrix((c.N_e, c.N_e), c.W_ee)
self.W_eu = self.source.generate_connection_e(c.N_e)
self.W_iu = self.source.generate_connection_i(c.N_i)
# Initialize the neurons in SORN
self.x = np.random.rand(c.N_e) < c.h_ip
self.y = np.zeros(c.N_i)
self.u = source.next()
# Initialize the pre-threshold variables
self.R_x = np.zeros(c.N_e)
self.R_y = np.zeros(c.N_i)
if c.ordered_thresholds:
self.T_i = (np.arange(c.N_i) + 0.5) * ((c.T_i_max - c.T_i_min) /
(1. * c.N_i)) + c.T_i_min
self.T_e = (np.arange(c.N_e) + 0.5) * ((c.T_e_max - c.T_e_min) /
(1. * c.N_e)) + c.T_e_min
np.random.shuffle(self.T_e)
else:
self.T_i = c.T_i_min + np.random.rand(
c.N_i) * (c.T_i_max - c.T_i_min)
self.T_e = c.T_e_min + np.random.rand(
c.N_e) * (c.T_e_max - c.T_e_min)
# Activate plasticity mechanisms
self.update = True
def generate_connection_i(self, N_i):
c = utils.Bunch(use_sparse=False,
lamb=np.inf,
avoid_self_connection=False)
ans = synapses.create_matrix((N_i, self.N_a), c)
W = np.zeros((N_i, self.N_a))
if N_i > 0:
available = set(range(N_i))
for a in range(self.N_a):
temp = np.random.choice(list(available), self.N_u_i)
W[temp, a] = 1
if '_' in self.lookup:
W[:, self.lookup['_']] = 0
ans.W = W
return ans
def generate_connection_e(self, N_e):
W = np.zeros((N_e, self.N_a))
available = set(range(N_e))
for a in range(self.N_a):
temp = np.random.choice(list(available), self.N_u_e)
W[temp, a] = 1
if self.avoid:
available = available.difference(temp)
if '_' in self.lookup:
W[:, self.lookup['_']] = 0
c = utils.Bunch(use_sparse=False,
lamb=np.inf,
avoid_self_connections=False)
ans = synapses.create_matrix((N_e, self.N_a), c)
ans.W = W
return ans
def generate_connection_i(self, N_i):
c = utils.Bunch(lamb=np.inf, avoid_self_connections=False)
return synapses.create_matrix((N_i, self.N_i), c)
def generate_connection_e(self, N_e):
c = utils.Bunch(lamb=np.inf, avoid_self_connections=False)
tmpsyn = synapses.create_matrix((N_e, self.N_i), c)
tmpsyn.set_synapses(tmpsyn.get_synapses() * 0)
return tmpsyn