def train(self, func, x0):
"""Optimize parameters to minimze loss.
Arguments:
- func: A function of the parameters that returns a tuple with the gradient and the loss respectively.
- x0: Parameters to use as starting point.
Returns the parameters that minimize the loss.
"""
if self._uW is None:
# TODO: should we not refcast here?
self._uW = numx.zeros_like(x0)
self.deltaW = numx.ones_like(x0) * self.deltainit
updated_params = x0.copy()
for _ in range(self.epochs):
# TODO: properly name variables
uW = func(updated_params)[0]
WW = self._uW * uW
self.deltaW *= self.etaplus * (WW > 0) + self.etamin * (
WW < 0) + 1 * (WW == 0)
self.deltaW = numx.maximum(self.deltaW, self.deltamin)
self.deltaW = numx.minimum(self.deltaW, self.deltamax)
updated_params -= self.deltaW * numx.sign(uW)
self._uW = uW
return updated_params
def train(self, func, x0):
"""Optimize parameters to minimze loss.
Arguments:
- func: A function of the parameters that returns a tuple with the gradient and the loss respectively.
- x0: Parameters to use as starting point.
Returns the parameters that minimize the loss.
"""
if self._uW is None:
# TODO: should we not refcast here?
self._uW = numx.zeros_like(x0)
self.deltaW = numx.ones_like(x0) * self.deltainit
updated_params = x0.copy()
for _ in range(self.epochs):
# TODO: properly name variables
uW = func(updated_params)[0]
WW = self._uW * uW;
self.deltaW *= self.etaplus * (WW > 0) + self.etamin * (WW < 0) + 1 * (WW == 0);
self.deltaW = numx.maximum(self.deltaW, self.deltamin)
self.deltaW = numx.minimum(self.deltaW, self.deltamax)
updated_params -= self.deltaW * numx.sign(uW)
self._uW = uW
return updated_params
def switching_signals(f1, f2, T, n_switches, n_samples=1):
samples = []
# seconds per simulation timestep
t = numx.arange(T)
proto_1 = numx.atleast_2d(numx.sin(2 * numx.pi * t * f1)).T
proto_2 = numx.atleast_2d(numx.sin(2 * numx.pi * t * f2)).T
for _ in range(n_samples):
n_periods1 = numx.random.randint(4, 8, size=(n_switches))
n_periods2 = numx.random.randint(4, 8, size=(n_switches))
#n_periods1, n_periods2 = [1], [0]
switch = []
signal = []
for p1, p2 in zip(n_periods1, n_periods2):
switch.extend([numx.ones_like(proto_1)] * p1)
switch.extend([-1 * numx.ones_like(proto_2)] * p2)
signal.extend([proto_1] * p1)
signal.extend([proto_2] * p2)
samples.append([numx.concatenate((numx.concatenate(switch), numx.concatenate(signal)), 1)])
return samples
def switching_signals(f1, f2, T, n_switches, n_samples=1):
samples = []
# seconds per simulation timestep
t = numx.arange(T)
proto_1 = numx.atleast_2d(numx.sin(2 * numx.pi * t * f1)).T
proto_2 = numx.atleast_2d(numx.sin(2 * numx.pi * t * f2)).T
for _ in range(n_samples):
n_periods1 = numx.random.randint(4, 8, size=(n_switches))
n_periods2 = numx.random.randint(4, 8, size=(n_switches))
# n_periods1, n_periods2 = [1], [0]
switch = []
signal = []
for p1, p2 in zip(n_periods1, n_periods2):
switch.extend([numx.ones_like(proto_1)] * p1)
switch.extend([-1 * numx.ones_like(proto_2)] * p2)
signal.extend([proto_1] * p1)
signal.extend([proto_2] * p2)
samples.append([numx.concatenate((numx.concatenate(switch), numx.concatenate(signal)), 1)])
return samples
