From 39eb0368d8880b9a0afe058bbbacee981c4af8a9 Mon Sep 17 00:00:00 2001 From: James Hensman Date: Tue, 8 Oct 2013 12:30:14 +0100 Subject: [PATCH 1/2] changes Nparts for num_parts in kern --- GPy/kern/kern.py | 12 ++++++------ GPy/testing/kernel_tests.py | 12 ++++++++++-- 2 files changed, 16 insertions(+), 8 deletions(-) diff --git a/GPy/kern/kern.py b/GPy/kern/kern.py index 5a8882dd..d6611a51 100644 --- a/GPy/kern/kern.py +++ b/GPy/kern/kern.py @@ -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)) diff --git a/GPy/testing/kernel_tests.py b/GPy/testing/kernel_tests.py index 87d4a20e..71daf0e8 100644 --- a/GPy/testing/kernel_tests.py +++ b/GPy/testing/kernel_tests.py @@ -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_rbf_invkernel(self): kern = GPy.kern.rbf_inv(5) From a59d980327c5c583264b168b0ff7c7290cae790c Mon Sep 17 00:00:00 2001 From: James Hensman Date: Tue, 8 Oct 2013 14:49:18 +0100 Subject: [PATCH 2/2] Nparam changes to num_params --- GPy/core/fitc.py | 2 +- GPy/core/sparse_gp.py | 2 +- GPy/kern/parts/periodic_Matern32.py | 2 +- GPy/kern/parts/periodic_Matern52.py | 2 +- GPy/kern/parts/periodic_exponential.py | 2 +- GPy/likelihoods/ep.py | 2 +- GPy/likelihoods/ep_mixed_noise.py | 2 +- GPy/likelihoods/gaussian.py | 2 +- GPy/likelihoods/gaussian_mixed_noise.py | 8 ++++---- GPy/models/mrd.py | 4 ++-- 10 files changed, 14 insertions(+), 14 deletions(-) diff --git a/GPy/core/fitc.py b/GPy/core/fitc.py index c9cf6eb2..0d294d07 100644 --- a/GPy/core/fitc.py +++ b/GPy/core/fitc.py @@ -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: diff --git a/GPy/core/sparse_gp.py b/GPy/core/sparse_gp.py index d4b33ed2..9251fcd6 100644 --- a/GPy/core/sparse_gp.py +++ b/GPy/core/sparse_gp.py @@ -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: diff --git a/GPy/kern/parts/periodic_Matern32.py b/GPy/kern/parts/periodic_Matern32.py index 5693085d..0de57f82 100644 --- a/GPy/kern/parts/periodic_Matern32.py +++ b/GPy/kern/parts/periodic_Matern32.py @@ -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) diff --git a/GPy/kern/parts/periodic_Matern52.py b/GPy/kern/parts/periodic_Matern52.py index 7b5ae846..882084fd 100644 --- a/GPy/kern/parts/periodic_Matern52.py +++ b/GPy/kern/parts/periodic_Matern52.py @@ -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) diff --git a/GPy/kern/parts/periodic_exponential.py b/GPy/kern/parts/periodic_exponential.py index 36b7b9ac..201def6d 100644 --- a/GPy/kern/parts/periodic_exponential.py +++ b/GPy/kern/parts/periodic_exponential.py @@ -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) diff --git a/GPy/likelihoods/ep.py b/GPy/likelihoods/ep.py index d242e583..4fedd66b 100644 --- a/GPy/likelihoods/ep.py +++ b/GPy/likelihoods/ep.py @@ -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: diff --git a/GPy/likelihoods/ep_mixed_noise.py b/GPy/likelihoods/ep_mixed_noise.py index ffc8cb51..f5452512 100644 --- a/GPy/likelihoods/ep_mixed_noise.py +++ b/GPy/likelihoods/ep_mixed_noise.py @@ -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 diff --git a/GPy/likelihoods/gaussian.py b/GPy/likelihoods/gaussian.py index 8f66d074..da13ddb0 100644 --- a/GPy/likelihoods/gaussian.py +++ b/GPy/likelihoods/gaussian.py @@ -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 diff --git a/GPy/likelihoods/gaussian_mixed_noise.py b/GPy/likelihoods/gaussian_mixed_noise.py index 4df01ec2..696867c0 100644 --- a/GPy/likelihoods/gaussian_mixed_noise.py +++ b/GPy/likelihoods/gaussian_mixed_noise.py @@ -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] diff --git a/GPy/models/mrd.py b/GPy/models/mrd.py index be191e9b..1435028f 100644 --- a/GPy/models/mrd.py +++ b/GPy/models/mrd.py @@ -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