(int(0.5 / phasta.dt))), numpy.tile(phaseTarget, 20)))
#negatively bias transition towards states 2-4 to block transition from state 1:
#phasta.updateTransitionTriggerInput(bias)
#evolve the system for some time
for i in range(int(t1 / phasta.dt)):
phasta.step()
#set one of the gains to nonzero in order to activate the enslavement
gains = [[0, 0., 0.], [80, 0., 0.], [0., 0., 0.]]
phasta.updateVelocityEnslavementGain(gains)
for i in range(int(t2 / phasta.dt)):
phasta.updatePhasesInput(phaseTarget[i])
phasta.step()
from matplotlib import pylab as plt
plt.figure(figsize=(4, 2))
n = int((t2) / phasta.dt)
plt.plot(numpy.linspace(t1, t1 + t2, n),
phaseTarget[:n],
linestyle=":",
color="#AAAAAA")
#plt.plot(numpy.linspace(t1, t1+t2, n), phasta.errorHistory[:n])
visualize(phasta,
t1 + t2,
sectionsAt=[t1],
name=os.path.splitext(os.path.basename(__file__))[0],
newFigure=False)
phasta.updateBiases([0.0, 0.0, -1e-7])
#alternative: specify the state to block in and project a blocking bias on all successors with the phasta.stateConnectivityMap
phasta.updateBiases(
numpy.dot(phasta.stateConnectivity, numpy.array([
0.0,
-1e-7,
0.0,
])))
#evolve the system for some time
for i in range(int(t1 / phasta.dt)):
phasta.step()
#un-freeze the state by setting the bias back to non-negative values.
# positive values will reduce the dwell-time considerably:
phasta.updateBiases([0.0, 0.0, 1e-1]) #
t2a = 0.1
for i in range(int(t2a / phasta.dt)):
phasta.step()
phasta.updateBiases([0.0, 0.0, 0.0]) #
for i in range(int((t2 - t2a) / phasta.dt)):
phasta.step()
visualize(phasta,
t1 + t2,
sectionsAt=[t1, t1 + t2a],
name=os.path.splitext(os.path.basename(__file__))[0])
predecessors = [
[17], #restart cycle from last state
[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0], #states that branch out from state 0
[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16] #last state aggregates all branches
]
phasta = phasestatemachine.Kernel(
numStates = 18,
predecessors = predecessors,
recordSteps=100000,
)
#set up biases so that we favor one out the 16 possible branches:
import collections
bias = collections.deque([0.0]*16)
bias[0] = 1e-4
phasta.updateBiases([1e-4] + list(bias) + [0.0]) #
endtime = 34.0
#run the kernel for some time:
triggered=False
for i in range(int(endtime/phasta.dt)):
phasta.step()
if phasta.statevector[-1] > 0.9 and not triggered:
triggered = True
bias.rotate(1)
phasta.updateBiases([1e-4] + list(bias) + [0.0])
elif phasta.statevector[0] > 0.9:
triggered = False
visualize(phasta, endtime, name=os.path.splitext(os.path.basename(__file__))[0])
phasta.updateBiases([bias, bias, bias, bias])
phaseVelocityExponentsMatrix = [[0., 0., 0., 0.], [0., 0., -3., 0.],
[0., 0., 0., 0.], [0., 0., -3., 0.]]
phasta.updateTransitionPhaseVelocityExponentInput(phaseVelocityExponentsMatrix)
timesToMark = []
#evolve the system for some time
phasta.step(t1)
timesToMark.append(phasta.t)
phasta.updateBiases([bias, -100 * bias, bias, 100 * bias])
phasta.step(tspike)
phasta.updateBiases([bias, bias, bias, bias])
timesToMark.append(phasta.t)
phasta.step(t2)
timesToMark.append(phasta.t)
phasta.updateBiases([bias, -100 * bias, bias, 100 * bias])
phasta.step(tspike)
phasta.updateBiases([bias, bias, bias, bias])
timesToMark.append(phasta.t)
phasta.step(t3)
visualize(phasta,
phasta.t,
sectionsAt=timesToMark,
name=os.path.splitext(os.path.basename(__file__))[0])
epsilon=epsilon,
recordSteps=100000,
)
endtime = 50.0
#Variation: negatively bias transition towards states 2-4 to block transition from state 1:
#phasta.updateTransitionTriggerInput([0, 1*epsilon, 0, 0])
Gamma = numpy.array([
[0, 1e-9, 0, 1e-9],
[0, 0, 0, 0],
[1e-4, 0, 0, 0],
[0, 0, 0, 0],
])
noiseScale = 1e-4 * numpy.sqrt(dt) / dt
#evolve the system for some time
for i in range(int(endtime / phasta.dt)):
Gamma[1, 2] = 3 * numpy.random.normal(scale=noiseScale)
Gamma[3, 2] = 1 * numpy.random.normal(scale=noiseScale)
bias = numpy.dot(Gamma, phasta.statevector)
phasta.updateBiases(Gamma)
phasta.step()
visualize(phasta,
endtime,
name=os.path.splitext(os.path.basename(__file__))[0],
clipActivations=0.05)