def main(seed):
# Setting Seeds
seeds = seedutil.load_seeds("main_seeds.npy", "../../../data/stability")
if seed is not None:
seeds = seeds[:seed]
# sine-wave target
target = lambda t0: np.cos(2 * np.pi * t0 / .5)
#Simulation parameters for FORCE
parameters = {}
parameters['dt'] = dt = .01 # time step
parameters['tmax'] = tmax = 10 # simulation length
parameters['tstop'] = tstop = 5 # learning stop time
parameters['tstart'] = tstart = 0 # learning start
parameters['N'] = N = 300 # size of stochastic pool
parameters['lr'] = lr = 1 # learning rate
parameters['rho'] = rho = 1.25 # spectral radius of J
parameters['pE'] = pE = .8 # excitatory percent
parameters['sparsity'] = sparsity = (.1, 1, 1) # weight sparsity
parameters['t_count'] = t_count = int(tmax / dt +
2) # number of time steps
parameters['noise_int_var'] = noise_int_var = .35
parameters['noise'] = noise = 'pink'
#Noise matrix
if noise == 'normal':
int_noise_mat = np.array(
[np.random.normal(0, noise_int_var, N) for _ in range(t_count)])
elif noise == 'pink':
int_noise_mat = np.array(
[noise_gen.voss(N, noise_int_var) for _ in range(t_count)])
errors_noise = []
derrors_noise = []
zs_noise = []
dzs_noise = []
for seedling in seeds:
J, Wz, _, x0, u, w = init_tools.set_simulation_parameters(seedling,
N,
1,
pE=pE,
p=sparsity,
rho=rho)
# inp & z are dummy variables
def model(t0, x, params):
index = params['index']
tanh_x = params['tanh_x']
z = params['z']
noise = params['noise'][index]
return (-x + np.dot(J, tanh_x) + Wz * z + noise) / dt
x, t, z, _, wu, _ = jedi.force(target,
model,
lr,
dt,
tmax,
tstart,
tstop,
x0,
w,
noise=int_noise_mat)
zs_noise.append(z)
error = z - target(t)
errors_noise.append(error)
x, t, z, _, wu, _ = jedi.dforce(jedi.step_decode,
target,
model,
lr,
dt,
tmax,
tstart,
tstop,
x0,
w,
noise=int_noise_mat,
pE=pE)
dzs_noise.append(z)
derror = z - target(t)
derrors_noise.append(derror)
try:
parameters['t'] = t
except NameError:
print "t was not defined; check seed args and script for errors"
noise_errors = {}
noise_errors['parameters'] = parameters
noise_errors['force'] = (errors_noise, zs_noise)
noise_errors['dforce'] = (derrors_noise, dzs_noise)
cPickle.dump(
noise_errors,
open(
"../../../data/stability/sin/internal_noise/noise_" +
str(noise_int_var) + ".p", "wb"))
def main(seed):
# Setting Seeds
seeds = seedutil.load_seeds("main_seeds.npy", "../../../data/stability")
if seed is not None:
seeds = seeds[:seed]
targets = np.load("../../../data/stability/flipflop/targets_tmax10sec.npy")
inputs = np.load("../../../data/stability/flipflop/inputs_tmax10sec.npy")
#Simulation parameters for FORCE
parameters = {}
parameters['dt'] = dt = .01 # time step
parameters['tmax'] = tmax = 10 # simulation length
parameters['tstop'] = tstop = 5 # learning stop time
parameters['tstart'] = tstart = 0 # learning start
parameters['N'] = N = 300 # size of stochastic pool
parameters['lr'] = lr = 1 # learning rate
parameters['rho'] = rho = 1.02 # spectral radius of J
parameters['sparsity'] = sparsity = (.1, 1, 1) # weight sparsity
parameters['t_count'] = t_count = int(tmax / dt + 2)
parameters['noise_var'] = noise_var = .35
parameters['noise'] = noise = 'normal'
#Noise matrix
if noise == 'normal':
noise_mat = np.array(
[np.random.normal(0, noise_var, N) for _ in range(t_count)])
elif noise == 'pink':
noise_mat = np.array(
[noise_gen.voss(N, noise_var) for _ in range(t_count)])
else:
raise ValueError("Noise '%s' invalid" % noise)
errors_noise = []
derrors_noise = []
zs_noise = []
dzs_noise = []
for seedling in seeds:
J, Wz, Wi, x0, u, w = init_tools.set_simulation_parameters(seedling,
N,
1,
p=sparsity,
rho=rho)
def model(t0, x, params):
index = params['index']
z = params['z']
tanh_x = params['tanh_x']
inp = params['inputs'][index]
noise = params['noise'][index]
return (-x + np.dot(J, tanh_x) + np.dot(Wi, inp) + Wz * z +
noise) / dt
x, t, z, _, wu, _ = jedi.force(targets,
model,
lr,
dt,
tmax,
tstart,
tstop,
x0,
w,
inputs=inputs,
noise=noise_mat)
zs_noise.append(z)
error = z - np.array(targets)
errors_noise.append(error)
x, t, z, _, wu, _ = jedi.dforce(jedi.step_decode,
targets,
model,
lr,
dt,
tmax,
tstart,
tstop,
x0,
w,
inputs=inputs,
noise=noise_mat)
dzs_noise.append(z)
derror = z - np.array(targets)
derrors_noise.append(derror)
try:
parameters['t'] = t
except NameError:
print "t was not defined; check seed args and script for errors"
noise_errors = {}
noise_errors['parameters'] = parameters
noise_errors['force'] = (errors_noise, zs_noise)
noise_errors['dforce'] = (derrors_noise, dzs_noise)
cPickle.dump(
noise_errors,
open(
"../../../data/stability/flipflop/internal_noise/noise_" +
str(noise_var) + ".p", "wb"))