def __init__(self, length_scale=1.0, sigma_f=1.0, numerator=3.0, **kwargs):
"""Initialize a Squared Exponential kernel instance.
Parameters
----------
length_scale : float or numpy.ndarray, optional
the characteristic length-scale (or length-scales) of the
phenomenon under investigation.
(Defaults to 1.0)
sigma_f : float, optional
Signal standard deviation.
(Defaults to 1.0)
numerator : float, optional
the numerator of parameter ni of Matern covariance functions.
Currently only numerator=3.0 and numerator=5.0 are implemented.
(Defaults to 3.0)
"""
# init base class first
NumpyKernel.__init__(self, **kwargs)
self.length_scale = length_scale
self.sigma_f = sigma_f
if numerator == 3.0 or numerator == 5.0:
self.numerator = numerator
else:
raise NotImplementedError
def __init__(self, length_scale=1.0, sigma_f=1.0, numerator=3.0, **kwargs):
"""Initialize a Squared Exponential kernel instance.
Parameters
----------
length_scale : float or numpy.ndarray, optional
the characteristic length-scale (or length-scales) of the
phenomenon under investigation.
(Defaults to 1.0)
sigma_f : float, optional
Signal standard deviation.
(Defaults to 1.0)
numerator : float, optional
the numerator of parameter ni of Matern covariance functions.
Currently only numerator=3.0 and numerator=5.0 are implemented.
(Defaults to 3.0)
"""
# init base class first
NumpyKernel.__init__(self, **kwargs)
self.length_scale = length_scale
self.sigma_f = sigma_f
if numerator == 3.0 or numerator == 5.0:
self.numerator = numerator
else:
raise NotImplementedError
def __init__(self, length_scale=1.0, sigma_f=1.0, **kwargs):
"""Initialize a Squared Exponential kernel instance.
Parameters
----------
length_scale : float or numpy.ndarray, optional
the characteristic length-scale (or length-scales) of the
phenomenon under investigation.
(Defaults to 1.0)
sigma_f : float, optional
Signal standard deviation.
(Defaults to 1.0)
"""
# init base class first
NumpyKernel.__init__(self, **kwargs)
self.length_scale = length_scale
self.sigma_f = sigma_f
def __init__(self, length_scale=1.0, sigma_f=1.0, **kwargs):
"""Initialize a Squared Exponential kernel instance.
Parameters
----------
length_scale : float or numpy.ndarray, optional
the characteristic length-scale (or length-scales) of the
phenomenon under investigation.
(Defaults to 1.0)
sigma_f : float, optional
Signal standard deviation.
(Defaults to 1.0)
"""
# init base class first
NumpyKernel.__init__(self, **kwargs)
self.length_scale = length_scale
self.sigma_f = sigma_f
def __init__(self, length_scale=1.0, sigma_f=1.0, alpha=0.5, **kwargs):
"""Initialize a Squared Exponential kernel instance.
Parameters
----------
length_scale : float or numpy.ndarray
the characteristic length-scale (or length-scales) of the
phenomenon under investigation.
(Defaults to 1.0)
sigma_f : float
Signal standard deviation.
(Defaults to 1.0)
alpha : float
The parameter of the RQ functions family.
(Defaults to 2.0)
"""
# init base class first
NumpyKernel.__init__(self, **kwargs)
self.length_scale = length_scale
self.sigma_f = sigma_f
self.alpha = alpha
def __init__(self, length_scale=1.0, sigma_f=1.0, alpha=0.5, **kwargs):
"""Initialize a Squared Exponential kernel instance.
Parameters
----------
length_scale : float or numpy.ndarray
the characteristic length-scale (or length-scales) of the
phenomenon under investigation.
(Defaults to 1.0)
sigma_f : float
Signal standard deviation.
(Defaults to 1.0)
alpha : float
The parameter of the RQ functions family.
(Defaults to 2.0)
"""
# init base class first
NumpyKernel.__init__(self, **kwargs)
self.length_scale = length_scale
self.sigma_f = sigma_f
self.alpha = alpha
def __init__(self, *args, **kwargs):
# for docstring holder
NumpyKernel.__init__(self, *args, **kwargs)
def __init__(self, *args, **kwargs):
# for docstring holder
NumpyKernel.__init__(self, *args, **kwargs)