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

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Neil Lawrence 2013-11-18 16:39:47 +00:00
commit 2af48254ce
4 changed files with 35 additions and 16 deletions
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17
GPy/kern/constructors.py
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@ -600,3 +600,20 @@ def ODE_1(input_dim=1, varianceU=1., varianceY=1., lengthscaleU=None, lengthsc
""" """
part = parts.ODE_1.ODE_1(input_dim, varianceU, varianceY, lengthscaleU, lengthscaleY) part = parts.ODE_1.ODE_1(input_dim, varianceU, varianceY, lengthscaleU, lengthscaleY)
return kern(input_dim, [part]) return kern(input_dim, [part])
def ODE_UY(input_dim=2, varianceU=1., varianceY=1., lengthscaleU=None, lengthscaleY=None):
"""
kernel resultiong from a first order ODE with OU driving GP
:param input_dim: the number of input dimension, has to be equal to one
:type input_dim: int
:param input_lengthU: the number of input U length
:param varianceU: variance of the driving GP
:type varianceU: float
:param varianceY: 'variance' of the transfer function
:type varianceY: float
:param lengthscaleY: 'lengthscale' of the transfer function
:type lengthscaleY: float
:rtype: kernel object
"""
part = parts.ODE_UY.ODE_UY(input_dim, varianceU, varianceY, lengthscaleU, lengthscaleY)
return kern(input_dim, [part])

1
GPy/kern/parts/__init__.py
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@ -14,6 +14,7 @@ import Matern32
import Matern52 import Matern52
import mlp import mlp
import ODE_1 import ODE_1
import ODE_UY
import periodic_exponential import periodic_exponential
import periodic_Matern32 import periodic_Matern32
import periodic_Matern52 import periodic_Matern52

31
GPy/models/__init__.py
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@ -1,18 +1,19 @@
# Copyright (c) 2012, GPy authors (see AUTHORS.txt). # Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt) # Licensed under the BSD 3-clause license (see LICENSE.txt)
from gp_regression import GPRegression from gp_regression import GPRegression; _gp_regression = gp_regression ; del gp_regression
from gp_classification import GPClassification from gp_classification import GPClassification; _gp_classification = gp_classification ; del gp_classification
from sparse_gp_regression import SparseGPRegression from sparse_gp_regression import SparseGPRegression; _sparse_gp_regression = sparse_gp_regression ; del sparse_gp_regression
from svigp_regression import SVIGPRegression from svigp_regression import SVIGPRegression; _svigp_regression = svigp_regression ; del svigp_regression
from sparse_gp_classification import SparseGPClassification from sparse_gp_classification import SparseGPClassification; _sparse_gp_classification = sparse_gp_classification ; del sparse_gp_classification
from fitc_classification import FITCClassification from fitc_classification import FITCClassification; _fitc_classification = fitc_classification ; del fitc_classification
from gplvm import GPLVM from gplvm import GPLVM; _gplvm = gplvm ; del gplvm
from bcgplvm import BCGPLVM from bcgplvm import BCGPLVM; _bcgplvm = bcgplvm; del bcgplvm
from sparse_gplvm import SparseGPLVM from sparse_gplvm import SparseGPLVM; _sparse_gplvm = sparse_gplvm ; del sparse_gplvm
from warped_gp import WarpedGP from warped_gp import WarpedGP; _warped_gp = warped_gp ; del warped_gp
from bayesian_gplvm import BayesianGPLVM from bayesian_gplvm import BayesianGPLVM; _bayesian_gplvm = bayesian_gplvm ; del bayesian_gplvm
from mrd import MRD from mrd import MRD; _mrd = mrd ; del mrd
from gradient_checker import GradientChecker from gradient_checker import GradientChecker; _gradient_checker = gradient_checker ; del gradient_checker
from gp_multioutput_regression import GPMultioutputRegression from gp_multioutput_regression import GPMultioutputRegression; _gp_multioutput_regression = gp_multioutput_regression ; del gp_multioutput_regression
from sparse_gp_multioutput_regression import SparseGPMultioutputRegression from sparse_gp_multioutput_regression import SparseGPMultioutputRegression; _sparse_gp_multioutput_regression = sparse_gp_multioutput_regression ; del sparse_gp_multioutput_regression

2
GPy/util/warping_functions.py
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@ -222,7 +222,7 @@ class TanhWarpingFunction_d(WarpingFunction):
""" """
mpsi = psi.coSpy() mpsi = psi.copy()
d = psi[-1] d = psi[-1]
mpsi = mpsi[:self.num_parameters-1].reshape(self.n_terms, 3) mpsi = mpsi[:self.num_parameters-1].reshape(self.n_terms, 3)