Beispiel #1
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    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
Beispiel #2
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    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
Beispiel #3
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    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
Beispiel #4
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    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
Beispiel #5
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    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
Beispiel #6
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    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
Beispiel #7
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 def __init__(self, *args, **kwargs):
     # for docstring holder
     NumpyKernel.__init__(self, *args, **kwargs)
Beispiel #8
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 def __init__(self, *args, **kwargs):
     # for docstring holder
     NumpyKernel.__init__(self, *args, **kwargs)