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added BGPLVM in parameterized

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This commit is contained in:
Max Zwiessele 2013-11-06 15:15:13 +00:00
parent 3316d29341
commit 8c02e4af36
6 changed files with 103 additions and 72 deletions
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40
GPy/kern/parts/rbf_inv.py
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@ -7,6 +7,7 @@ import numpy as np
import hashlib
from scipy import weave
from ...util.linalg import tdot
from GPy.core.parameter import Param
class RBFInv(RBF):
"""
@ -33,8 +34,9 @@ class RBFInv(RBF):
"""
def __init__(self, input_dim, variance=1., inv_lengthscale=None, ARD=False):
self.input_dim = input_dim
self.name = 'rbf_inv'
#self.input_dim = input_dim
#self.name = 'rbf_inv'
super(RBFInv, self).__init__(input_dim, variance=variance, lengthscale=1./np.array(inv_lengthscale), ARD=ARD, name='inverse rbf')
self.ARD = ARD
if not ARD:
self.num_params = 2
@ -50,8 +52,13 @@ class RBFInv(RBF):
assert inv_lengthscale.size == self.input_dim, "bad number of lengthscales"
else:
inv_lengthscale = np.ones(self.input_dim)
self._set_params(np.hstack((variance, inv_lengthscale.flatten())))
self.variance = Param('variance', variance)
self.inv_lengthscale = Param('sensitivity', inv_lengthscale)
self.inv_lengthscale.add_observer(self, self.update_inv_lengthscale)
self.remove_parameter(self.lengthscale)
self.add_parameters(self.variance, self.inv_lengthscale)
#self._set_params(np.hstack((variance, inv_lengthscale.flatten())))
# initialize cache
self._Z, self._mu, self._S = np.empty(shape=(3, 1))
@ -64,26 +71,29 @@ class RBFInv(RBF):
def _get_params(self):
return np.hstack((self.variance, self.inv_lengthscale))
# def _get_params(self):
# return np.hstack((self.variance, self.inv_lengthscale))
def _set_params(self, x):
assert x.size == (self.num_params)
self.variance = x[0]
self.inv_lengthscale = x[1:]
def update_inv_lengthscale(self, il):
self.inv_lengthscale2 = np.square(self.inv_lengthscale)
# TODO: We can rewrite everything with inv_lengthscale and never need to do the below
self.lengthscale = 1. / self.inv_lengthscale
self.lengthscale2 = np.square(self.lengthscale)
#def _set_params(self, x):
def parameters_changed(self):
#assert x.size == (self.num_params)
#self.variance = x[0]
#self.inv_lengthscale = x[1:]
# reset cached results
self._X, self._X2, self._params = np.empty(shape=(3, 1))
self._Z, self._mu, self._S = np.empty(shape=(3, 1)) # cached versions of Z,mu,S
def _get_param_names(self):
if self.num_params == 2:
return ['variance', 'inv_lengthscale']
else:
return ['variance'] + ['inv_lengthscale%i' % i for i in range(self.inv_lengthscale.size)]
# def _get_param_names(self):
# if self.num_params == 2:
# return ['variance', 'inv_lengthscale']
# else:
# return ['variance'] + ['inv_lengthscale%i' % i for i in range(self.inv_lengthscale.size)]
# TODO: Rewrite computations so that lengthscale is not needed (but only inv. lengthscale)
def dK_dtheta(self, dL_dK, X, X2, target):