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[paramnonparam] adding cdf like

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mzwiessele 2015-04-24 08:59:02 +02:00
parent 9b54f831b3
commit 1946514593
1 changed files with 45 additions and 0 deletions
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45
GPy/kern/_src/basis_funcs.py
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@ -136,3 +136,48 @@ class DomainKernel(LinearSlopeBasisFuncKernel):
def _phi(self, X):
phi = np.where((X>self.start)*(X<self.stop), 1, 0)
return phi#((phi-self.start)/(self.stop-self.start))-.5
class LogisticBasisFuncKernel(BasisFuncKernel):
def __init__(self, input_dim, centers, variance=1., slope=1., active_dims=None, ARD=False, ARD_slope=True, name='logistic'):
self.centers = np.atleast_2d(centers)
self.ARD_slope = ARD_slope
if self.ARD_slope:
self.slope = Param('slope', slope * np.ones(self.centers.size), Logexp())
else:
self.slope = Param('slope', slope, Logexp())
super(LogisticBasisFuncKernel, self).__init__(input_dim, variance, active_dims, ARD, name)
self.link_parameter(self.slope)
@Cache_this(limit=3, ignore_args=())
def _phi(self, X):
import scipy as sp
phi = 1/(1+np.exp(-((X-self.centers)*self.slope)))
return np.where(np.isnan(phi), 0, phi)#((phi-self.start)/(self.stop-self.start))-.5
def parameters_changed(self):
BasisFuncKernel.parameters_changed(self)
def update_gradients_full(self, dL_dK, X, X2=None):
super(LogisticBasisFuncKernel, self).update_gradients_full(dL_dK, X, X2)
if X2 is None or X is X2:
phi1 = self.phi(X)
if phi1.ndim != 2:
phi1 = phi1[:, None]
dphi1_dl = (phi1**2) * (np.exp(-((X-self.centers)*self.slope)) * (X-self.centers))
if self.ARD_slope:
self.slope.gradient = self.variance * 2 * np.einsum('ij,iq,jq->q', dL_dK, phi1, dphi1_dl)
else:
self.slope.gradient = self.variance * 2 * (dL_dK * phi1.dot(dphi1_dl.T)).sum()
else:
phi1 = self.phi(X)
phi2 = self.phi(X2)
if phi1.ndim != 2:
phi1 = phi1[:, None]
phi2 = phi2[:, None]
dphi1_dl = (phi1**2) * (np.exp(-((X-self.centers)*self.slope)) * (X-self.centers))
dphi2_dl = (phi2**2) * (np.exp(-((X2-self.centers)*self.slope)) * (X2-self.centers))
if self.ARD_slope:
self.slope.gradient = (self.variance * np.einsum('ij,iq,jq->q', dL_dK, phi1, dphi2_dl) + np.einsum('ij,iq,jq->q', dL_dK, phi2, dphi1_dl))
else:
self.slope.gradient = self.variance * (dL_dK * phi1.dot(dphi2_dl.T)).sum() + (dL_dK * phi2.dot(dphi1_dl.T)).sum()
self.slope.gradient = np.where(np.isnan(self.slope.gradient), 0, self.slope.gradient)