def ljapexp(q=150, iterations=40, gamma0=10**-8, sigma=0.1):
# input, hidden and output layer size
p = 1
#q = 150
r = 1 #300
# strength of input activations
INPUT_STRENGTH = 10**0
def input_dist():
return INPUT_STRENGTH*uniform(-1,1)
def normgen(mu, sigma):
def n():
return normalvariate(mu, sigma)
return n
def unigen(a,b):
def u():
return uniform(a,b)
return u
def zerogen():
def zero():
return 0
return zero
# matrix and initial activation randomization
r = Reservoir(p, q, r)
r.randomize_matrices(normgen(0,1))
r.WI = random_matrix(q,p+1, unigen(-0.1,0.1))
r.W = random_matrix(q,q+1, normgen(0,sigma))
#r.randomize_vectors(normgen(0,1))
# make a copy of the reservoir
r2 = copy.deepcopy(r)
#perturbation = random_vector(q)
#perturbation = times(perturbation, gamma0 / vector_len(perturbation))
priemersum = 0
for perturbed_node in range(q):
r.randomize_vectors(normgen(0,1))
perturbation = [0 for _ in range(q)]
perturbation[perturbed_node] = gamma0
r2.X = plus(r2.X, perturbation)
difvec = plus(times(r.X, -1), r2.X)
exponentsum = 0
lambdy = [0]*iterations
for it in range(0, iterations):
# introduce a random input
r.I = random_vector(r.p, input_dist)
r2.I = r.I
# run one setp
r.fire()
r2.fire()
# calculate the difference
difvec = plus(times(r.X, -1), r2.X)
gammaK = vector_len(difvec)
# renormalize
r2.X = plus(r.X, times(difvec, gamma0 / gammaK))
# record lambda
mylog = math.log(gammaK / gamma0)
lambdy[it] = mylog
priemer = sum(lambdy) / iterations
disperzia = sum([ (x-priemer)*(x-priemer) for x in lambdy]) / iterations
priemersum += priemer
print("\rnodeindex=%d" % perturbed_node, end="")
return priemersum / q
def ljapexp(q=150, iterations=40, gamma0=10**-8, sigma=0.1):
# input, hidden and output layer size
p = 1
#q = 150
r = 1 #300
# strength of input activations
INPUT_STRENGTH = 10**0
def input_dist():
return INPUT_STRENGTH * uniform(-1, 1)
def normgen(mu, sigma):
def n():
return normalvariate(mu, sigma)
return n
def unigen(a, b):
def u():
return uniform(a, b)
return u
def zerogen():
def zero():
return 0
return zero
# matrix and initial activation randomization
r = Reservoir(p, q, r)
r.randomize_matrices(normgen(0, 1))
r.WI = random_matrix(q, p + 1, unigen(-0.1, 0.1))
r.W = random_matrix(q, q + 1, normgen(0, sigma))
#r.randomize_vectors(normgen(0,1))
# make a copy of the reservoir
r2 = copy.deepcopy(r)
#perturbation = random_vector(q)
#perturbation = times(perturbation, gamma0 / vector_len(perturbation))
priemersum = 0
for perturbed_node in range(q):
r.randomize_vectors(normgen(0, 1))
perturbation = [0 for _ in range(q)]
perturbation[perturbed_node] = gamma0
r2.X = plus(r2.X, perturbation)
difvec = plus(times(r.X, -1), r2.X)
exponentsum = 0
lambdy = [0] * iterations
for it in range(0, iterations):
# introduce a random input
r.I = random_vector(r.p, input_dist)
r2.I = r.I
# run one setp
r.fire()
r2.fire()
# calculate the difference
difvec = plus(times(r.X, -1), r2.X)
gammaK = vector_len(difvec)
# renormalize
r2.X = plus(r.X, times(difvec, gamma0 / gammaK))
# record lambda
mylog = math.log(gammaK / gamma0)
lambdy[it] = mylog
priemer = sum(lambdy) / iterations
disperzia = sum([(x - priemer) * (x - priemer)
for x in lambdy]) / iterations
priemersum += priemer
print("\rnodeindex=%d" % perturbed_node, end="")
return priemersum / q
def zerogen():
def zero():
return 0
return zero
# perturbation size = initial separation
gamma0 = 10**-8
ITERATIONS = 400
# matrix and initial activation randomization
r = Reservoir(p, q, r)
r.randomize_matrices(norm)
r.WI = random_matrix(q, p + 1, unigen(-0.1, 0.1))
r.W = random_matrix(q, q + 1, normgen(0, 10**(-0.8)))
r.randomize_vectors(normgen(0, 1))
# make a copy of the reservoir
r2 = copy.deepcopy(r)
#perturbation = random_vector(q)
#perturbation = times(perturbation, gamma0 / vector_len(perturbation))
perturbation = [0] * q
perturbation[randrange(q)] = gamma0
#print(perturbation)
r2.X = plus(r2.X, perturbation)
def u(): return uniform(a,b) return u def zerogen(): def zero(): return 0 return zero # perturbation size = initial separation gamma0 = 10**-8 ITERATIONS = 400 # matrix and initial activation randomization r = Reservoir(p, q, r) r.randomize_matrices(norm) r.WI = random_matrix(q,p+1, unigen(-0.1,0.1)) r.W = random_matrix(q,q+1, normgen(0,10**(-0.8))) r.randomize_vectors(normgen(0,1)) # make a copy of the reservoir r2 = copy.deepcopy(r) #perturbation = random_vector(q) #perturbation = times(perturbation, gamma0 / vector_len(perturbation)) perturbation = [0]*q perturbation[randrange(q)] = gamma0 #print(perturbation) r2.X = plus(r2.X, perturbation)