def fire(self):
# X = αX + (1-α)[WI*I + W*X]
X = plus(times(X, self.alpha),
times(plus(dot(WI, I), dot(W, X)), 1 - self.alpha))
X = tanh(X) #sigmoid(X)
# Y = WO*X
Y = dot(WO, X)
def fire(self):
# X = αX + (1-α)[WI*I + W*X]
self.X = plus(
times(self.X, self.alpha),
times(plus(dot(self.WI, [1] + self.I), dot(self.W, [1] + self.X)),
1 - self.alpha))
self.X = tanh(self.X)
# Y = WO*X
self.Y = dot(self.WO, [1] + self.X)
def iterate(self, measurement, control ):
if measurement:
self.lost = False
velocity = control
# known are speeds, convert to absolute movements
nao_movement_x = velocity[0]
nao_movement_y = velocity[1]
nao_movement_t = velocity[2]
# step forward using control vector u
self.u[0][0] = nao_movement_x
self.u[1][0] = nao_movement_y
muBelief = self.mu
# ________ PREDICTION ________
# rotate using nao_movements theta
t = nao_movement_t
rotationmatrix = [[ math.cos( t ), -math.sin(t) ],[ math.sin(t), math.cos(t) ]]
# Predict new measurement based on nao movement.
# Nao moves x,y towards the ball
muBelief = matrix.subtract( muBelief , self.u )
#print muBelief
# Nao rotates theta
muBelief = matrix.mult( rotationmatrix, muBelief )
# add noise to motion
muBelief = sample( muBelief, self.Sigma, 2)
# covariance matrix update
SigmaBelief = matrix.plus( self.Sigma , self.R)
# ________ CORRECTION _________
if measurement:
self.z[0][0] = measurement[0]
self.z[1][0] = measurement[1]
# Since C = [1,0;0,1], drop it
s = matrix.inverse2D( matrix.plus( SigmaBelief, self.Q) )
K = matrix.mult(SigmaBelief, s )
self.mu = matrix.plus( muBelief, matrix.mult(K, matrix.subtract(self.z , muBelief)) )
self.Sigma = matrix.mult(matrix.subtract( self.I, K ), SigmaBelief)
else:
# if no ball is found, use the predicted state!
self.mu = muBelief
self.Sigma = SigmaBelief
self.ballPos = self.mu[0][0], self.mu[1][0]
def sample(mean, cov , dimensions):
r = matrix.transpose( matrix.Cholesky(cov) )
randoms = matrix.zero(dimensions, 1)
for i in range(len(randoms)):
randoms[i][0] = random.gauss(0,0.025)
return matrix.plus( mean , matrix.mult(r, randoms))
def sample(mean, cov , dimensions):
r = matrix.transpose( matrix.Cholesky(cov) )
randoms = matrix.zero(dimensions, 1)
for i in range(len(randoms)):
randoms[i][0] = random.gauss(0,0.05)
return matrix.plus( mean , matrix.mult(r, randoms))
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 iterate(self, measurement, control):
now = time.time()
# timestamps matter. IMPORTANT: Do not use if first iteration has not been set.
if self.firstCall:
self.firstCall = False
timeTaken = time.time() - self.timeStamp
# major screwup if this happens
if timeTaken > 1.0:
print 'Interval was way too long: ', timeTaken, 'seconds.'
timeTaken = 0.0
self.timeStamp = time.time()
#velocity = motProxy.getRobotVelocity()
velocity = control
# known are speeds, convert to absolute movements
nao_movement_x = velocity[0] * timeTaken
nao_movement_y = velocity[1] * timeTaken
nao_movement_t = velocity[2] * timeTaken
#print timeTaken
# step forward using control vector u
self.u[0][0] = nao_movement_x
self.u[1][0] = nao_movement_y
print 'Increment control ', self.u
muBelief = self.mu
# ________ PREDICTION ________
# rotate using nao_movements theta
t = nao_movement_t
rotationmatrix = [[ math.cos( t ), -math.sin(t) ],[ math.sin(t), math.cos(t) ]]
# Predict new measurement based on nao movement.
# Nao moves x,y towards the ball
muBelief = matrix.subtract( muBelief , self.u )
#print muBelief
# Nao rotates theta
muBelief = matrix.mult( rotationmatrix, muBelief )
# add noise to motion
muBelief = sample( muBelief, self.Sigma, 2)
# covariance matrix update
SigmaBelief = matrix.plus( self.Sigma , self.R)
# ________ CORRECTION _________
if measurement:
self.z[0][0] = measurement[0]
self.z[1][0] = measurement[1]
# Since C = [1,0;0,1], drop it
s = matrix.inverse2D( matrix.plus( SigmaBelief, self.Q) )
K = matrix.mult(SigmaBelief, s )
self.mu = matrix.plus( muBelief, matrix.mult(K, matrix.subtract(self.z , muBelief)) )
self.Sigma = matrix.mult(matrix.subtract( self.I, K ), SigmaBelief)
else:
# if no ball is found, use the predicted state!
self.mu = muBelief
self.Sigma = SigmaBelief
#print 'Mu:',self.mu
#print 'Sigma: '
#matrix.show(self.Sigma)
#print ''
return (self.mu[0][0], self.mu[1][0])
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)
difvec = plus(times(r.X, -1), r2.X)
#print("difvec = %s" % difvec)
f = open('output.dat', 'w')
exponentsum = 0
lambdy = [0] * ITERATIONS
for it in range(0, ITERATIONS):
r.I = random_vector(r.p, input_dist)
r2.I = r.I
r.fire()
r2.fire()
difvec = plus(times(r.X, -1), r2.X)
gammaK = vector_len(difvec)
def fire(self): # X = αX + (1-α)[WI*I + W*X] self.X = plus(times(self.X, self.alpha), times(plus( dot(self.WI, [1]+self.I), dot(self.W,[1]+self.X)), 1 - self.alpha)) self.X = tanh(self.X) # Y = WO*X self.Y = dot(self.WO, [1]+self.X)
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
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)
difvec = plus(times(r.X, -1), r2.X)
#print("difvec = %s" % difvec)
f = open('output.dat','w')
exponentsum = 0
lambdy = [0]*ITERATIONS
for it in range(0, ITERATIONS):
r.I = random_vector(r.p, input_dist)
r2.I = r.I
r.fire()
r2.fire()
difvec = plus(times(r.X, -1), r2.X)
gammaK = vector_len(difvec)
