def __init__(self,
input_dim,
num_partials,
lengthscales=None,
variances=None,
frequencies=None):
gpflow.kernels.Kern.__init__(self, input_dim, active_dims=None)
len_l = []
var_l = []
freq_l = []
self.ARD = False
self.num_partials = num_partials
if lengthscales.all() == None:
lengthscales = 1. * np.ones((num_partials, 1))
variances = 0.125 * np.ones((num_partials, 1))
frequencies = 1. * (1. + np.arange(num_partials))
for i in range(self.num_partials):
len_l.append(Param(lengthscales[i], transforms.Logistic(0., 2.)))
var_l.append(Param(variances[i], transforms.Logistic(0., 1.)))
freq_l.append(Param(frequencies[i], transforms.positive))
self.lengthscales = ParamList(len_l)
self.variance = ParamList(var_l)
self.frequency = ParamList(freq_l)
def __init__(self,
input_dim,
numc,
lengthscales=None,
variances=None,
frequencies=None):
gpflow.kernels.Kern.__init__(self, input_dim, active_dims=None)
self.ARD = False
self.numc = numc
if lengthscales == None:
lengthscales = 1.
variances = 0.125 * np.ones((numc, 1))
frequencies = 1. * np.arange(1, numc + 1)
self.lengthscales = Param(lengthscales, transforms.Logistic(0., 10.))
for i in range(
self.numc
): # generate a param object for each var, and freq, they must be (numc,) arrays.
setattr(self, 'variance_' + str(i + 1),
Param(variances[i], transforms.Logistic(0., 0.25)))
setattr(self, 'frequency_' + str(i + 1),
Param(frequencies[i], transforms.positive))
for i in range(self.numc):
exec('self.variance_' + str(i + 1) + '.fixed = ' + str(True))
def __init__(self,
input_dim,
energy=np.asarray([1.]),
frequency=np.asarray([2 * np.pi]),
variance=1.0,
features_as_params=False):
"""
- input_dim is the dimension of the input to the kernel
- variance is the (initial) value for the variance parameter(s)
if ARD=True, there is one variance per input
- active_dims is a list of length input_dim which controls
which columns of X are used.
"""
gpflow.kernels.Kern.__init__(self, input_dim, active_dims=None)
self.num_features = len(frequency)
self.variance = Param(variance, transforms.Logistic(0., 0.25))
if features_as_params:
energy_list = []
frequency_list = []
for i in range(energy.size):
energy_list.append(Param(energy[i], transforms.positive))
frequency_list.append(Param(frequency[i], transforms.positive))
self.energy = ParamList(energy_list)
self.frequency = ParamList(frequency_list)
else:
self.energy = energy
self.frequency = frequency
def __init__(self, input_dim, variance=1., lengthscales=1., gamma=1.):
gpflow.kernels.Stationary.__init__(self,
input_dim=input_dim,
variance=variance,
lengthscales=lengthscales)
self.gamma = Param(gamma, transforms.Logistic(0.00001, 2.))