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active_dims as extra parameter for kernels, it tells which input dimensions to work on

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This commit is contained in:
Max Zwiessele 2014-03-14 09:18:08 +00:00
parent 328e0124c7
commit efcce6d0af
12 changed files with 73 additions and 59 deletions
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28
GPy/kern/_src/stationary.py
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@ -41,8 +41,8 @@ class Stationary(Kern):
"""
def __init__(self, input_dim, variance, lengthscale, ARD, name):
super(Stationary, self).__init__(input_dim, name)
def __init__(self, input_dim, variance, lengthscale, ARD, active_dims, name):
super(Stationary, self).__init__(input_dim, active_dims, name)
self.ARD = ARD
if not ARD:
if lengthscale is None:
@ -186,8 +186,8 @@ class Stationary(Kern):
return np.ones(self.input_dim)/self.lengthscale
class Exponential(Stationary):
def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, name='Exponential'):
super(Exponential, self).__init__(input_dim, variance, lengthscale, ARD, name)
def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, active_dims=None, name='Exponential'):
super(Exponential, self).__init__(input_dim, variance, lengthscale, ARD, active_dims, name)
def K_of_r(self, r):
return self.variance * np.exp(-0.5 * r)
@ -205,8 +205,8 @@ class Matern32(Stationary):
"""
def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, name='Mat32'):
super(Matern32, self).__init__(input_dim, variance, lengthscale, ARD, name)
def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, active_dims=None, name='Mat32'):
super(Matern32, self).__init__(input_dim, variance, lengthscale, ARD, active_dims, name)
def K_of_r(self, r):
return self.variance * (1. + np.sqrt(3.) * r) * np.exp(-np.sqrt(3.) * r)
@ -249,8 +249,8 @@ class Matern52(Stationary):
k(r) = \sigma^2 (1 + \sqrt{5} r + \\frac53 r^2) \exp(- \sqrt{5} r)
"""
def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, name='Mat52'):
super(Matern52, self).__init__(input_dim, variance, lengthscale, ARD, name)
def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, active_dims=None, name='Mat52'):
super(Matern52, self).__init__(input_dim, variance, lengthscale, ARD, active_dims, name)
def K_of_r(self, r):
return self.variance*(1+np.sqrt(5.)*r+5./3*r**2)*np.exp(-np.sqrt(5.)*r)
@ -291,8 +291,8 @@ class Matern52(Stationary):
class ExpQuad(Stationary):
def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, name='ExpQuad'):
super(ExpQuad, self).__init__(input_dim, variance, lengthscale, ARD, name)
def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, active_dims=None, name='ExpQuad'):
super(ExpQuad, self).__init__(input_dim, variance, lengthscale, ARD, active_dims, name)
def K_of_r(self, r):
return self.variance * np.exp(-0.5 * r**2)
@ -301,8 +301,8 @@ class ExpQuad(Stationary):
return -r*self.K_of_r(r)
class Cosine(Stationary):
def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, name='Cosine'):
super(Cosine, self).__init__(input_dim, variance, lengthscale, ARD, name)
def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, active_dims=None, name='Cosine'):
super(Cosine, self).__init__(input_dim, variance, lengthscale, ARD, active_dims, name)
def K_of_r(self, r):
return self.variance * np.cos(r)
@ -322,8 +322,8 @@ class RatQuad(Stationary):
"""
def __init__(self, input_dim, variance=1., lengthscale=None, power=2., ARD=False, name='ExpQuad'):
super(RatQuad, self).__init__(input_dim, variance, lengthscale, ARD, name)
def __init__(self, input_dim, variance=1., lengthscale=None, power=2., ARD=False, active_dims=None, name='ExpQuad'):
super(RatQuad, self).__init__(input_dim, variance, lengthscale, ARD, active_dims, name)
self.power = Param('power', power, Logexp())
self.add_parameters(self.power)