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[link|unlink_parameter] renaming add_parameter to link_parameter

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Max Zwiessele 2014-09-08 08:57:28 +01:00
parent b9e897c50d
commit 4543fc3480
33 changed files with 90 additions and 83 deletions
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2
GPy/models/bayesian_gplvm.py
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@ -78,7 +78,7 @@ class BayesianGPLVM(SparseGP):
SparseGP.__init__(self, X, Y, Z, kernel, likelihood, inference_method, name, normalizer=normalizer)
self.logger.info("Adding X as parameter")
self.add_parameter(self.X, index=0)
self.link_parameter(self.X, index=0)
if mpi_comm != None:
from ..util.mpi import divide_data

6
GPy/models/gp_kronecker_gaussian_regression.py
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@ -35,12 +35,12 @@ class GPKroneckerGaussianRegression(Model):
self.X2 = ObsAr(X2)
self.Y = Y
self.kern1, self.kern2 = kern1, kern2
self.add_parameter(self.kern1)
self.add_parameter(self.kern2)
self.link_parameter(self.kern1)
self.link_parameter(self.kern2)
self.likelihood = likelihoods.Gaussian()
self.likelihood.variance = noise_var
self.add_parameter(self.likelihood)
self.link_parameter(self.likelihood)
self.num_data1, self.input_dim1 = self.X1.shape
self.num_data2, self.input_dim2 = self.X2.shape

6
GPy/models/gp_var_gauss.py
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@ -32,13 +32,13 @@ class GPVariationalGaussianApproximation(Model):
if kernel is None:
kernel = kern.RBF(X.shape[1]) + kern.White(X.shape[1], 0.01)
self.kern = kernel
self.add_parameter(self.kern)
self.link_parameter(self.kern)
self.num_data, self.input_dim = self.X.shape
self.alpha = Param('alpha', np.zeros(self.num_data))
self.beta = Param('beta', np.ones(self.num_data))
self.add_parameter(self.alpha)
self.add_parameter(self.beta)
self.link_parameter(self.alpha)
self.link_parameter(self.beta)
self.gh_x, self.gh_w = np.polynomial.hermite.hermgauss(20)
self.Ysign = np.where(Y==1, 1, -1).flatten()

2
GPy/models/gplvm.py
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@ -38,7 +38,7 @@ class GPLVM(GP):
super(GPLVM, self).__init__(X, Y, kernel, likelihood, name='GPLVM')
self.X = Param('latent_mean', X)
self.add_parameter(self.X, index=0)
self.link_parameter(self.X, index=0)
def parameters_changed(self):
super(GPLVM, self).parameters_changed()

2
GPy/models/gradient_checker.py
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@ -76,7 +76,7 @@ class GradientChecker(Model):
for name, xi in zip(self.names, at_least_one_element(x0)):
self.__setattr__(name, Param(name, xi))
self.add_parameter(self.__getattribute__(name))
self.link_parameter(self.__getattribute__(name))
# self._param_names = []
# for name, shape in zip(self.names, self.shapes):
# self._param_names.extend(map(lambda nameshape: ('_'.join(nameshape)).strip('_'), itertools.izip(itertools.repeat(name), itertools.imap(lambda t: '_'.join(map(str, t)), itertools.product(*map(lambda xi: range(xi), shape))))))

8
GPy/models/mrd.py
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@ -129,7 +129,7 @@ class MRD(SparseGP):
else: likelihoods = likelihoods
self.logger.info("adding X and Z")
self.add_parameters(self.X, self.Z)
self.link_parameters(self.X, self.Z)
self.bgplvms = []
self.num_data = Ylist[0].shape[0]
@ -137,11 +137,11 @@ class MRD(SparseGP):
for i, n, k, l, Y in itertools.izip(itertools.count(), Ynames, kernels, likelihoods, Ylist):
assert Y.shape[0] == self.num_data, "All datasets need to share the number of datapoints, and those have to correspond to one another"
p = Parameterized(name=n)
p.add_parameter(k)
p.link_parameter(k)
p.kern = k
p.add_parameter(l)
p.link_parameter(l)
p.likelihood = l
self.add_parameter(p)
self.link_parameter(p)
self.bgplvms.append(p)
self.posterior = None