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Merge branch 'devel' of https://github.com/SheffieldML/GPy into devel

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This commit is contained in:
Neil Lawrence 2013-10-09 11:14:47 +01:00
commit b2cea56c11
14 changed files with 38 additions and 30 deletions
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2
GPy/core/fitc.py
View file

@ -126,7 +126,7 @@ class FITC(SparseGP):
self._dpsi1_dX += self.kern.dK_dX(_dpsi1.T,self.Z,self.X[i:i+1,:])
# the partial derivative vector for the likelihood
if self.likelihood.Nparams == 0:
if self.likelihood.num_params == 0:
# save computation here.
self.partial_for_likelihood = None
elif self.likelihood.is_heteroscedastic:

2
GPy/core/sparse_gp.py
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@ -156,7 +156,7 @@ class SparseGP(GPBase):
# the partial derivative vector for the likelihood
if self.likelihood.Nparams == 0:
if self.likelihood.num_params == 0:
# save computation here.
self.partial_for_likelihood = None
elif self.likelihood.is_heteroscedastic:

12
GPy/core/svigp.py
View file

@ -350,8 +350,8 @@ class SVIGP(GPBase):
#callback
if i and not i%callback_interval:
callback()
time.sleep(0.1)
callback(self) # Change this to callback()
time.sleep(0.01)
if self.epochs > 10:
self._adapt_steplength()
@ -367,13 +367,13 @@ class SVIGP(GPBase):
assert self.vb_steplength > 0
if self.adapt_param_steplength:
# self._adaptive_param_steplength()
self._adaptive_param_steplength()
# self._adaptive_param_steplength_log()
self._adaptive_param_steplength_from_vb()
# self._adaptive_param_steplength_from_vb()
self._param_steplength_trace.append(self.param_steplength)
def _adaptive_param_steplength(self):
decr_factor = 0.1
decr_factor = 0.02
g_tp = self._transform_gradients(self._log_likelihood_gradients())
self.gbar_tp = (1-1/self.tau_tp)*self.gbar_tp + 1/self.tau_tp * g_tp
self.hbar_tp = (1-1/self.tau_tp)*self.hbar_tp + 1/self.tau_tp * np.dot(g_tp.T, g_tp)
@ -407,7 +407,7 @@ class SVIGP(GPBase):
self.tau_t = self.tau_t*(1-self.vb_steplength) + 1
def _adaptive_vb_steplength_KL(self):
decr_factor = 1 #0.1
decr_factor = 0.1
natgrad = self.vb_grad_natgrad()
g_t1 = natgrad[0]
g_t2 = natgrad[1]

12
GPy/kern/kern.py
View file

@ -31,7 +31,7 @@ class kern(Parameterized):
"""
self.parts = parts
self.Nparts = len(parts)
self.num_parts = len(parts)
self.num_params = sum([p.num_params for p in self.parts])
self.input_dim = input_dim
@ -61,7 +61,7 @@ class kern(Parameterized):
here just all the indices, rest can get recomputed
"""
return Parameterized.getstate(self) + [self.parts,
self.Nparts,
self.num_parts,
self.num_params,
self.input_dim,
self.input_slices,
@ -73,7 +73,7 @@ class kern(Parameterized):
self.input_slices = state.pop()
self.input_dim = state.pop()
self.num_params = state.pop()
self.Nparts = state.pop()
self.num_parts = state.pop()
self.parts = state.pop()
Parameterized.setstate(self, state)
@ -308,7 +308,7 @@ class kern(Parameterized):
def K(self, X, X2=None, which_parts='all'):
if which_parts == 'all':
which_parts = [True] * self.Nparts
which_parts = [True] * self.num_parts
assert X.shape[1] == self.input_dim
if X2 is None:
target = np.zeros((X.shape[0], X.shape[0]))
@ -359,7 +359,7 @@ class kern(Parameterized):
def Kdiag(self, X, which_parts='all'):
"""Compute the diagonal of the covariance function for inputs X."""
if which_parts == 'all':
which_parts = [True] * self.Nparts
which_parts = [True] * self.num_parts
assert X.shape[1] == self.input_dim
target = np.zeros(X.shape[0])
[p.Kdiag(X[:, i_s], target=target) for p, i_s, part_on in zip(self.parts, self.input_slices, which_parts) if part_on]
@ -497,7 +497,7 @@ class kern(Parameterized):
def plot(self, x=None, plot_limits=None, which_parts='all', resolution=None, *args, **kwargs):
if which_parts == 'all':
which_parts = [True] * self.Nparts
which_parts = [True] * self.num_parts
if self.input_dim == 1:
if x is None:
x = np.zeros((1, 1))

2
GPy/kern/parts/periodic_Matern32.py
View file

@ -113,7 +113,7 @@ class PeriodicMatern32(Kernpart):
@silence_errors
def dK_dtheta(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to the parameters (shape is Nxnum_inducingxNparam)"""
"""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)
FX2 = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X2)

2
GPy/kern/parts/periodic_Matern52.py
View file

@ -115,7 +115,7 @@ class PeriodicMatern52(Kernpart):
@silence_errors
def dK_dtheta(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to the parameters (shape is Nxnum_inducingxNparam)"""
"""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)
FX2 = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X2)

2
GPy/kern/parts/periodic_exponential.py
View file

@ -111,7 +111,7 @@ class PeriodicExponential(Kernpart):
@silence_errors
def dK_dtheta(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to the parameters (shape is Nxnum_inducingxNparam)"""
"""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
FX = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)
FX2 = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X2)

2
GPy/likelihoods/ep.py
View file

@ -18,7 +18,7 @@ class EP(likelihood):
self.data = data
self.num_data, self.output_dim = self.data.shape
self.is_heteroscedastic = True
self.Nparams = 0
self.num_params = 0
self._transf_data = self.noise_model._preprocess_values(data)
#Initial values - Likelihood approximation parameters:

2
GPy/likelihoods/ep_mixed_noise.py
View file

@ -31,7 +31,7 @@ class EP_Mixed_Noise(likelihood):
self.data = np.vstack(data_list)
self.N, self.output_dim = self.data.shape
self.is_heteroscedastic = True
self.Nparams = 0#FIXME
self.num_params = 0#FIXME
self._transf_data = np.vstack([noise_model._preprocess_values(data) for noise_model,data in zip(noise_model_list,data_list)])
#TODO non-gaussian index

2
GPy/likelihoods/gaussian.py
View file

@ -15,7 +15,7 @@ class Gaussian(likelihood):
"""
def __init__(self, data, variance=1., normalize=False):
self.is_heteroscedastic = False
self.Nparams = 1
self.num_params = 1
self.Z = 0. # a correction factor which accounts for the approximation made
N, self.output_dim = data.shape

8
GPy/likelihoods/gaussian_mixed_noise.py
View file

@ -23,14 +23,14 @@ class Gaussian_Mixed_Noise(likelihood):
:type normalize: False|True
"""
def __init__(self, data_list, noise_params=None, normalize=True):
self.Nparams = len(data_list)
self.num_params = len(data_list)
self.n_list = [data.size for data in data_list]
self.index = np.vstack([np.repeat(i,n)[:,None] for i,n in zip(range(self.Nparams),self.n_list)])
self.index = np.vstack([np.repeat(i,n)[:,None] for i,n in zip(range(self.num_params),self.n_list)])
if noise_params is None:
noise_params = [1.] * self.Nparams
noise_params = [1.] * self.num_params
else:
assert self.Nparams == len(noise_params), 'Number of noise parameters does not match the number of noise models.'
assert self.num_params == len(noise_params), 'Number of noise parameters does not match the number of noise models.'
self.noise_model_list = [Gaussian(Y,variance=v,normalize = normalize) for Y,v in zip(data_list,noise_params)]
self.n_params = [noise_model._get_params().size for noise_model in self.noise_model_list]

4
GPy/models/mrd.py
View file

@ -211,8 +211,8 @@ class MRD(Model):
# g.Z = Z.reshape(self.num_inducing, self.input_dim)
#
# def _set_kern_params(self, g, p):
# g.kern._set_params(p[:g.kern.Nparam])
# g.likelihood._set_params(p[g.kern.Nparam:])
# g.kern._set_params(p[:g.kern.num_params])
# g.likelihood._set_params(p[g.kern.num_params:])
def _set_params(self, x):
start = 0; end = self.NQ

4
GPy/models/svigp_regression.py
View file

@ -25,7 +25,7 @@ class SVIGPRegression(SVIGP):
"""
def __init__(self, X, Y, kernel=None, Z=None, num_inducing=10, q_u=None, batchsize=10):
def __init__(self, X, Y, kernel=None, Z=None, num_inducing=10, q_u=None, batchsize=10, normalize_Y=False):
# kern defaults to rbf (plus white for stability)
if kernel is None:
kernel = kern.rbf(X.shape[1], variance=1., lengthscale=4.) + kern.white(X.shape[1], 1e-3)
@ -38,7 +38,7 @@ class SVIGPRegression(SVIGP):
assert Z.shape[1] == X.shape[1]
# likelihood defaults to Gaussian
likelihood = likelihoods.Gaussian(Y, normalize=False)
likelihood = likelihoods.Gaussian(Y, normalize=normalize_Y)
SVIGP.__init__(self, X, likelihood, kernel, Z, q_u=q_u, batchsize=batchsize)
self.load_batch()

12
GPy/testing/kernel_tests.py
View file

@ -7,6 +7,13 @@ import GPy
verbose = False
try:
import sympy
SYMPY_AVAILABLE=True
except ImportError:
SYMPY_AVAILABLE=False
class KernelTests(unittest.TestCase):
def test_kerneltie(self):
K = GPy.kern.rbf(5, ARD=True)
@ -22,8 +29,9 @@ class KernelTests(unittest.TestCase):
self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
def test_rbf_sympykernel(self):
kern = GPy.kern.rbf_sympy(5)
self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
if SYMPY_AVAILABLE:
kern = GPy.kern.rbf_sympy(5)
self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
def test_eq_sympykernel(self):
kern = GPy.kern.eq_sympy(5, 3)