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Merge branch 'params' of github.com:SheffieldML/GPy into params

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This commit is contained in:
Alan Saul 2014-02-07 15:16:55 +00:00
commit 625943ef27
1 changed files with 16 additions and 16 deletions
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32
GPy/kern/parts/periodic_exponential.py
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@ -6,6 +6,7 @@ from kernpart import Kernpart
import numpy as np
from GPy.util.linalg import mdot
from GPy.util.decorators import silence_errors
from GPy.core.parameterization.param import Param
class PeriodicExponential(Kernpart):
"""
@ -25,9 +26,9 @@ class PeriodicExponential(Kernpart):
"""
def __init__(self, input_dim=1, variance=1., lengthscale=None, period=2 * np.pi, n_freq=10, lower=0., upper=4 * np.pi):
def __init__(self, input_dim=1, variance=1., lengthscale=None, period=2 * np.pi, n_freq=10, lower=0., upper=4 * np.pi, name='periodic_exp'):
super(PeriodicExponential, self).__init__(input_dim, name)
assert input_dim==1, "Periodic kernels are only defined for input_dim=1"
self.name = 'periodic_exp'
self.input_dim = input_dim
if lengthscale is not None:
lengthscale = np.asarray(lengthscale)
@ -38,7 +39,11 @@ class PeriodicExponential(Kernpart):
self.num_params = 3
self.n_freq = n_freq
self.n_basis = 2*n_freq
self._set_params(np.hstack((variance,lengthscale,period)))
self.variance = Param('variance', variance)
self.lengthscale = Param('lengthscale', lengthscale)
self.period = Param('period', period)
self.parameters_changed()
#self._set_params(np.hstack((variance,lengthscale,period)))
def _cos(self,alpha,omega,phase):
def f(x):
@ -61,30 +66,25 @@ class PeriodicExponential(Kernpart):
Gint = np.dot(r1,r2.T)/2 * np.where(np.isnan(Gint1),Gint2,Gint1)
return Gint
def _get_params(self):
"""return the value of the parameters."""
return np.hstack((self.variance,self.lengthscale,self.period))
#def _get_params(self):
# """return the value of the parameters."""
# return np.hstack((self.variance,self.lengthscale,self.period))
def _set_params(self,x):
def parameters_changed(self):
"""set the value of the parameters."""
assert x.size==3
self.variance = x[0]
self.lengthscale = x[1]
self.period = x[2]
self.a = [1./self.lengthscale, 1.]
self.b = [1]
self.basis_alpha = np.ones((self.n_basis,))
self.basis_omega = np.array(sum([[i*2*np.pi/self.period]*2 for i in range(1,self.n_freq+1)],[]))
self.basis_omega = np.array(sum([[i*2*np.pi/self.period]*2 for i in range(1,self.n_freq+1)],[]))[:,0]
self.basis_phi = np.array(sum([[-np.pi/2, 0.] for i in range(1,self.n_freq+1)],[]))
self.G = self.Gram_matrix()
self.Gi = np.linalg.inv(self.G)
def _get_param_names(self):
"""return parameter names."""
return ['variance','lengthscale','period']
#def _get_param_names(self):
# """return parameter names."""
# return ['variance','lengthscale','period']
def Gram_matrix(self):
La = np.column_stack((self.a[0]*np.ones((self.n_basis,1)),self.a[1]*self.basis_omega))