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periodic kernel gradients and parameterized updates

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Max Zwiessele 2014-03-13 12:27:59 +00:00
parent a18b54ed73
commit 1102387a76
1 changed files with 11 additions and 9 deletions
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20
GPy/kern/_src/periodic.py
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@ -85,8 +85,9 @@ class PeriodicExponential(Periodic):
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)],[]))[:,0]
self.basis_phi = np.array(sum([[-np.pi/2, 0.] for i in range(1,self.n_freq+1)],[]))
self.basis_omega = (2*np.pi*np.arange(1,self.n_freq+1)/self.period).repeat(2)
self.basis_phi = np.zeros(self.n_freq * 2)
self.basis_phi[::2] = -np.pi/2
self.G = self.Gram_matrix()
self.Gi = np.linalg.inv(self.G)
@ -100,7 +101,6 @@ class PeriodicExponential(Periodic):
Flower = np.array(self._cos(self.basis_alpha,self.basis_omega,self.basis_phi)(self.lower))[:,None]
return(self.lengthscale/(2*self.variance) * Gint + 1./self.variance*np.dot(Flower,Flower.T))
#@silence_errors
def update_gradients_full(self, dL_dK, X, X2=None):
"""derivative of the covariance matrix with respect to the parameters (shape is N x num_inducing x num_params)"""
if X2 is None: X2 = X
@ -194,8 +194,9 @@ class PeriodicMatern32(Periodic):
self.b = [1,self.lengthscale**2/3]
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_phi = np.array(sum([[-np.pi/2, 0.] for i in range(1,self.n_freq+1)],[]))
self.basis_omega = (2*np.pi*np.arange(1,self.n_freq+1)/self.period).repeat(2)
self.basis_phi = np.zeros(self.n_freq * 2)
self.basis_phi[::2] = -np.pi/2
self.G = self.Gram_matrix()
self.Gi = np.linalg.inv(self.G)
@ -212,8 +213,8 @@ class PeriodicMatern32(Periodic):
return(self.lengthscale**3/(12*np.sqrt(3)*self.variance) * Gint + 1./self.variance*np.dot(Flower,Flower.T) + self.lengthscale**2/(3.*self.variance)*np.dot(F1lower,F1lower.T))
@silence_errors
def update_gradients_full(self,dL_dK,X,X2,target):
#@silence_errors
def update_gradients_full(self,dL_dK,X,X2):
"""derivative of the covariance matrix with respect to the parameters (shape is num_data x num_inducing x num_params)"""
if X2 is None: X2 = X
FX = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)
@ -307,8 +308,9 @@ class PeriodicMatern52(Periodic):
self.b = [9./8, 9*self.lengthscale**4/200., 3*self.lengthscale**2/5., 3*self.lengthscale**2/(5*8.), 3*self.lengthscale**2/(5*8.)]
self.basis_alpha = np.ones((2*self.n_freq,))
self.basis_omega = np.array(sum([[i*2*np.pi/self.period]*2 for i in range(1,self.n_freq+1)],[]))
self.basis_phi = np.array(sum([[-np.pi/2, 0.] for i in range(1,self.n_freq+1)],[]))
self.basis_omega = (2*np.pi*np.arange(1,self.n_freq+1)/self.period).repeat(2)
self.basis_phi = np.zeros(self.n_freq * 2)
self.basis_phi[::2] = -np.pi/2
self.G = self.Gram_matrix()
self.Gi = np.linalg.inv(self.G)