Added copyrights and documentation to new models and kernels

GPy/kern/src/diff_kern.py

@@ -1,9 +1,19 @@
+# Copyright (c) 2018, GPy authors (see AUTHORS.txt).
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
 from .kern import CombinationKernel
 import numpy as np
 from paramz.caching import Cache_this
 
-# A thin wrapper around the base kernel to tell that we are dealing with a partial derivative of a Kernel
 class DiffKern(CombinationKernel):
+    """
+    Diff kernel is a thin wrapper for using partial derivatives of kernels as kernels. Eg. in combination with
+    Multioutput kernel this allows the user to train GPs with observations of latent function and latent
+    function derivatives
+    
+    The parameters the kernel needs are:
+    -'base_kern': a member of Kernel class that is used for observations
+    -'dimension': integer that indigates in which dimensions the partial derivative observations are
+    """
     def __init__(self, base_kern, dimension):
         super(DiffKern, self).__init__([base_kern], 'DiffKern')
         self.base_kern = base_kern

GPy/kern/src/multioutput_derivative_kern.py

@@ -1,3 +1,6 @@
+# Copyright (c) 2018, GPy authors (see AUTHORS.txt).
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
 from .kern import Kern, CombinationKernel
 from .multioutput_kern import MultioutputKern, ZeroKern
 import numpy as np

GPy/kern/src/multioutput_kern.py

@@ -1,3 +1,6 @@
+# Copyright (c) 2018, GPy authors (see AUTHORS.txt).
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
 from .kern import Kern, CombinationKernel
 import numpy as np
 from functools import reduce, partial

GPy/models/multioutput_gp.py

@@ -22,19 +22,13 @@ logger = logging.getLogger("GP")
 
 class MultioutputGP(GP):
     """
-    General purpose Gaussian process model
-    :param X: input observations
-    :param Y: output observations
-    :param kernel: a GPy kernel, defaults to rbf+white
-    :param likelihood: a GPy likelihood
+    Gaussian process model for using observations from multiple likelihoods and different kernels
+    :param X_list: input observations in a list for each likelihood
+    :param Y: output observations in a list for each likelihood
+    :param kernel_list: kernels in a list for each likelihood
+    :param likelihood_list: likelihoods in a list
+    :param kernel_cross_covariances: Cross covariances between different likelihoods. See class MultioutputKern for more
     :param inference_method: The :class:`~GPy.inference.latent_function_inference.LatentFunctionInference` inference method to use for this GP
-    :rtype: model object
-    :param Norm normalizer:
-        normalize the outputs Y.
-        Prediction will be un-normalized using this normalizer.
-        If normalizer is None, we will normalize using Standardize.
-        If normalizer is False, no normalization will be done.
-    .. Note:: Multiple independent outputs are allowed using columns of Y
     """
     def __init__(self, X_list, Y_list, kernel_list, likelihood_list, name='multioutputgp', kernel_cross_covariances={}, inference_method=None):
         #Input and Output