From 1fea140df8176c3ee209d23fc7a075e4c7b5e16a Mon Sep 17 00:00:00 2001 From: James Hensman Date: Mon, 16 Sep 2013 09:55:32 +0100 Subject: [PATCH] actually changing coregionalise to coregionalize --- GPy/examples/regression.py | 6 +++--- GPy/kern/constructors.py | 10 +++++----- GPy/kern/parts/__init__.py | 4 ++-- GPy/kern/parts/coregionalize.py | 2 +- GPy/testing/kernel_tests.py | 7 +------ GPy/util/multioutput.py | 8 ++++---- 6 files changed, 16 insertions(+), 21 deletions(-) diff --git a/GPy/examples/regression.py b/GPy/examples/regression.py index df7b92b8..e1727d5f 100644 --- a/GPy/examples/regression.py +++ b/GPy/examples/regression.py @@ -22,7 +22,7 @@ def coregionalisation_toy2(max_iters=100): Y = np.vstack((Y1, Y2)) k1 = GPy.kern.rbf(1) + GPy.kern.bias(1) - k2 = GPy.kern.coregionalise(2,1) + k2 = GPy.kern.coregionalize(2,1) k = k1**k2 #k = k1.prod(k2,tensor=True) m = GPy.models.GPRegression(X, Y, kernel=k) m.constrain_fixed('.*rbf_var', 1.) @@ -82,7 +82,7 @@ def coregionalisation_sparse(max_iters=100): k1 = GPy.kern.rbf(1) m = GPy.models.SparseGPMultioutputRegression(X_list=[X1,X2],Y_list=[Y1,Y2],kernel_list=[k1],num_inducing=20) - #k2 = GPy.kern.coregionalise(2, 2) + #k2 = GPy.kern.coregionalize(2, 2) #k = k1**k2 #.prod(k2, tensor=True) # + GPy.kern.white(2,0.001) #m = GPy.models.SparseGPRegression(X, Y, kernel=k, Z=Z) m.constrain_fixed('.*rbf_var', 1.) @@ -135,7 +135,7 @@ def epomeo_gpx(max_iters=100): np.random.randint(0, 4, num_inducing)[:, None])) k1 = GPy.kern.rbf(1) - k2 = GPy.kern.coregionalise(output_dim=5, rank=5) + k2 = GPy.kern.coregionalize(output_dim=5, rank=5) k = k1**k2 m = GPy.models.SparseGPRegression(t, Y, kernel=k, Z=Z, normalize_Y=True) diff --git a/GPy/kern/constructors.py b/GPy/kern/constructors.py index 943965ed..8a334278 100644 --- a/GPy/kern/constructors.py +++ b/GPy/kern/constructors.py @@ -346,7 +346,7 @@ def symmetric(k): k_.parts = [symmetric.Symmetric(p) for p in k.parts] return k_ -def coregionalise(num_outputs,W_columns=1, W=None, kappa=None): +def coregionalize(num_outputs,W_columns=1, W=None, kappa=None): """ Coregionlization matrix B, of the form: .. math:: @@ -358,7 +358,7 @@ def coregionalise(num_outputs,W_columns=1, W=None, kappa=None): it is obtainded as the tensor product between a kernel k(x,y) and B. - :param num_outputs: the number of outputs to corregionalise + :param num_outputs: the number of outputs to corregionalize :type num_outputs: int :param W_columns: number of columns of the W matrix (this parameter is ignored if parameter W is not None) :type W_colunns: int @@ -369,7 +369,7 @@ def coregionalise(num_outputs,W_columns=1, W=None, kappa=None): :rtype: kernel object """ - p = parts.coregionalise.Coregionalise(num_outputs,W_columns,W,kappa) + p = parts.coregionalize.Coregionalize(num_outputs,W_columns,W,kappa) return kern(1,[p]) @@ -448,11 +448,11 @@ def build_lcm(input_dim, num_outputs, kernel_list = [], W_columns=1,W=None,kappa k.input_dim = input_dim warnings.warn("kernel's input dimension overwritten to fit input_dim parameter.") - k_coreg = coregionalise(num_outputs,W_columns,W,kappa) + k_coreg = coregionalize(num_outputs,W_columns,W,kappa) kernel = kernel_list[0]**k_coreg.copy() for k in kernel_list[1:]: - k_coreg = coregionalise(num_outputs,W_columns,W,kappa) + k_coreg = coregionalize(num_outputs,W_columns,W,kappa) kernel += k**k_coreg.copy() return kernel diff --git a/GPy/kern/parts/__init__.py b/GPy/kern/parts/__init__.py index 4b7c2d9b..643483f5 100644 --- a/GPy/kern/parts/__init__.py +++ b/GPy/kern/parts/__init__.py @@ -1,11 +1,11 @@ import bias import Brownian -import coregionalise +import coregionalize import exponential import finite_dimensional import fixed import gibbs -import hetero +#import hetero #hetero.py is not commited: omitting for now. JH. import hierarchical import independent_outputs import linear diff --git a/GPy/kern/parts/coregionalize.py b/GPy/kern/parts/coregionalize.py index 66e14052..941fb429 100644 --- a/GPy/kern/parts/coregionalize.py +++ b/GPy/kern/parts/coregionalize.py @@ -7,7 +7,7 @@ from GPy.util.linalg import mdot, pdinv import pdb from scipy import weave -class Coregionalise(Kernpart): +class Coregionalize(Kernpart): """ Kernel for intrinsic/linear coregionalization models diff --git a/GPy/testing/kernel_tests.py b/GPy/testing/kernel_tests.py index 9329aba0..2ebddefd 100644 --- a/GPy/testing/kernel_tests.py +++ b/GPy/testing/kernel_tests.py @@ -4,12 +4,7 @@ import unittest import numpy as np import GPy -<<<<<<< HEAD - verbose = False -======= - ->>>>>>> 1bc93747178b0bab1b7177568388ebd4207647e0 class KernelTests(unittest.TestCase): def test_kerneltie(self): @@ -93,7 +88,7 @@ class KernelTests(unittest.TestCase): Y = np.vstack((Y1,Y2)) k1 = GPy.kern.rbf(1) + GPy.kern.bias(1) - k2 = GPy.kern.coregionalise(2,1) + k2 = GPy.kern.coregionalize(2,1) kern = k1**k2 self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose)) diff --git a/GPy/util/multioutput.py b/GPy/util/multioutput.py index 2b06ba95..a57593a7 100644 --- a/GPy/util/multioutput.py +++ b/GPy/util/multioutput.py @@ -9,8 +9,8 @@ def build_lcm(input_dim, num_outputs, CK = [], NC = [], W_columns=1,W=None,kappa :input_dim: Input dimensionality :num_outputs: Number of outputs - :param CK: List of coregionalized kernels (i.e., this will be multiplied by a coregionalise kernel). - :param K: List of kernels that will be added up together with CK, but won't be multiplied by a coregionalise kernel + :param CK: List of coregionalized kernels (i.e., this will be multiplied by a coregionalize kernel). + :param K: List of kernels that will be added up together with CK, but won't be multiplied by a coregionalize kernel :param W_columns: number tuples of the corregionalization parameters 'coregion_W' :type W_columns: integer """ @@ -25,9 +25,9 @@ def build_lcm(input_dim, num_outputs, CK = [], NC = [], W_columns=1,W=None,kappa k.input_dim = input_dim + 1 warnings.warn("kernel's input dimension overwritten to fit input_dim parameter.") - kernel = CK[0].prod(kern.coregionalise(num_outputs,W_columns,W,kappa),tensor=True) + kernel = CK[0].prod(kern.coregionalize(num_outputs,W_columns,W,kappa),tensor=True) for k in CK[1:]: - k_coreg = kern.coregionalise(num_outputs,W_columns,W,kappa) + k_coreg = kern.coregionalize(num_outputs,W_columns,W,kappa) kernel += k.prod(k_coreg,tensor=True) for k in NC: kernel += k