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small changes in the way covariance functions handle lengthscale as input

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Nicolas 2013-01-31 17:19:15 +00:00
parent 3a052cd5eb
commit a510524620
5 changed files with 39 additions and 40 deletions
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14
GPy/kern/Matern52.py
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@ -13,14 +13,14 @@ class Matern52(kernpart):
.. math::
k(r) = \sigma^2 (1 + \sqrt{5} r + \\frac53 r^2) \exp(- \sqrt{5} r) \qquad \qquad \\text{ where } r = \sqrt{\sum_{i=1}^D \\frac{(x_i-y_i)^2}{\ell_i^2} }
k(r) = \sigma^2 (1 + \sqrt{5} r + \\frac53 r^2) \exp(- \sqrt{5} r) \\qquad \\qquad \\text{ where } r = \sqrt{\sum_{i=1}^D \\frac{(x_i-y_i)^2}{\ell_i^2} }
:param D: the number of input dimensions
:type D: int
:param variance: the variance :math:`\sigma^2`
:type variance: float
:param lengthscale: the vector of lengthscale :math:`\ell_i`
:type lengthscale: np.ndarray of size (1,) or (D,) depending on ARD
:type lengthscale: array or list of the appropriate size (or float if there is only one lengthscale parameter)
:param ARD: Auto Relevance Determination. If equal to "False", the kernel is isotropic (ie. one single lengthscale parameter \ell), otherwise there is one lengthscale parameter per dimension.
:type ARD: Boolean
:rtype: kernel object
@ -33,17 +33,19 @@ class Matern52(kernpart):
self.Nparam = 2
self.name = 'Mat52'
if lengthscale is not None:
assert lengthscale.shape == (1,)
lengthscale = np.asarray(lengthscale)
assert lengthscale.size == 1, "Only one lengthscale needed for non-ARD kernel"
else:
lengthscale = np.ones(1)
else:
self.Nparam = self.D + 1
self.name = 'Mat52_ARD'
self.name = 'Mat52'
if lengthscale is not None:
assert lengthscale.shape == (self.D,)
lengthscale = np.asarray(lengthscale)
assert lengthscale.size == self.D, "bad number of lengthscales"
else:
lengthscale = np.ones(self.D)
self._set_params(np.hstack((variance,lengthscale)))
self._set_params(np.hstack((variance,lengthscale.flatten())))
def _get_params(self):
"""return the value of the parameters."""