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yak shaving

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This commit is contained in:
James Hensman 2013-04-05 12:08:21 +01:00
parent 97704d9928
commit 8e4d839b5d
1 changed files with 11 additions and 11 deletions
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22
GPy/kern/rbfcos.py
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@ -5,7 +5,6 @@
from kernpart import kernpart from kernpart import kernpart
import numpy as np import numpy as np
import hashlib
class rbfcos(kernpart): class rbfcos(kernpart):
def __init__(self,D,variance=1.,frequencies=None,bandwidths=None,ARD=False): def __init__(self,D,variance=1.,frequencies=None,bandwidths=None,ARD=False):
@ -32,13 +31,13 @@ class rbfcos(kernpart):
self.Nparam = 3 self.Nparam = 3
if frequencies is not None: if frequencies is not None:
frequencies = np.asarray(frequencies) frequencies = np.asarray(frequencies)
assert frequencies.size == 1, "Only one frequency needed for non-ARD kernel" assert frequencies.size == 1, "Exactly one frequency needed for non-ARD kernel"
else: else:
frequencies = np.ones(1) frequencies = np.ones(1)
if bandwidths is not None: if bandwidths is not None:
bandwidths = np.asarray(bandwidths) bandwidths = np.asarray(bandwidths)
assert bandwidths.size == 1, "Only one bandwidth needed for non-ARD kernel" assert bandwidths.size == 1, "Exactly one bandwidth needed for non-ARD kernel"
else: else:
bandwidths = np.ones(1) bandwidths = np.ones(1)
@ -74,14 +73,15 @@ class rbfcos(kernpart):
np.add(target,self.variance,target) np.add(target,self.variance,target)
def dK_dtheta(self,dL_dK,X,X2,target): def dK_dtheta(self,dL_dK,X,X2,target):
self._K_computations(X,X2)
target[0] += np.sum(dL_dK*self._dvar) target[0] += np.sum(dL_dK*self._dvar)
if self.ARD: if self.ARD:
for q in xrange(self.D): for q in xrange(self.D):
target[q+1] += -2.*np.pi*self.variance*np.sum(dL_dK*self._dvar*np.tan(2.*np.pi*self.dist[:,:,q]*self.frequencies[q])*self.dist[:,:,q]) target[q+1] += -2.*np.pi*self.variance*np.sum(dL_dK*self._dvar*np.tan(2.*np.pi*self._dist[:,:,q]*self.frequencies[q])*self._dist[:,:,q])
target[q+1+self.D] += -2.*np.pi**2*self.variance*np.sum(dL_dK*self._dvar*self.dist2[:,:,q]) target[q+1+self.D] += -2.*np.pi**2*self.variance*np.sum(dL_dK*self._dvar*self._dist2[:,:,q])
else: else:
target[1] += -2.*np.pi*self.variance*np.sum(dL_dK*self._dvar*np.sum(np.tan(2.*np.pi*self.dist*self.frequencies)*self.dist,-1)) target[1] += -2.*np.pi*self.variance*np.sum(dL_dK*self._dvar*np.sum(np.tan(2.*np.pi*self._dist*self.frequencies)*self._dist,-1))
target[2] += -2.*np.pi**2*self.variance*np.sum(dL_dK*self._dvar*self.dist2.sum(-1)) target[2] += -2.*np.pi**2*self.variance*np.sum(dL_dK*self._dvar*self._dist2.sum(-1))
def dKdiag_dtheta(self,dL_dKdiag,X,target): def dKdiag_dtheta(self,dL_dKdiag,X,target):
@ -102,8 +102,8 @@ class rbfcos(kernpart):
#do the distances: this will be high memory for large D #do the distances: this will be high memory for large D
#NB: we don't take the abs of the dist because cos is symmetric #NB: we don't take the abs of the dist because cos is symmetric
self.dist = X[:,None,:] - X2[None,:,:] self._dist = X[:,None,:] - X2[None,:,:]
self.dist2 = np.square(self.dist) self._dist2 = np.square(self._dist)
#ensure the next section is computed: #ensure the next section is computed:
self._params = np.empty(self.Nparam) self._params = np.empty(self.Nparam)
@ -111,7 +111,7 @@ class rbfcos(kernpart):
if not np.all(self._params == self._get_params()): if not np.all(self._params == self._get_params()):
self._params == self._get_params().copy() self._params == self._get_params().copy()
self._rbf_part = np.exp(-2.*np.pi**2*np.sum(self.dist2*self.bandwidths,-1)) self._rbf_part = np.exp(-2.*np.pi**2*np.sum(self._dist2*self.bandwidths,-1))
self._cos_part = np.prod(np.cos(2.*np.pi*self.dist*self.frequencies),-1) self._cos_part = np.prod(np.cos(2.*np.pi*self._dist*self.frequencies),-1)
self._dvar = self._rbf_part*self._cos_part self._dvar = self._rbf_part*self._cos_part