Fixed lots of examples

GPy/models/__init__.py

@@ -4,7 +4,6 @@
 from gp_regression import GPRegression
 from gp_classification import GPClassification
 from sparse_gp_regression import SparseGPRegression, SparseGPRegressionUncertainInput
-from svigp_regression import SVIGPRegression
 from sparse_gp_classification import SparseGPClassification
 from gplvm import GPLVM
 from bcgplvm import BCGPLVM

GPy/models/svigp_regression.py

@@ -1,45 +0,0 @@
-# Copyright (c) 2012, James Hensman
-# Licensed under the BSD 3-clause license (see LICENSE.txt)
-
-import numpy as np
-from ..core import SVIGP
-from .. import likelihoods
-from .. import kern
-
-class SVIGPRegression(SVIGP):
-    """
-    Gaussian Process model for regression
-
-    This is a thin wrapper around the SVIGP class, with a set of sensible defalts
-
-    :param X: input observations
-    :param Y: observed values
-    :param kernel: a GPy kernel, defaults to rbf+white
-    :param normalize_X:  whether to normalize the input data before computing (predictions will be in original scales)
-    :type normalize_X: False|True
-    :param normalize_Y:  whether to normalize the input data before computing (predictions will be in original scales)
-    :type normalize_Y: False|True
-    :rtype: model object
-
-    .. Note:: Multiple independent outputs are allowed using columns of Y
-
-    """
-
-    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)
-
-        # Z defaults to a subset of the data
-        if Z is None:
-            i = np.random.permutation(X.shape[0])[:num_inducing]
-            Z = X[i].copy()
-        else:
-            assert Z.shape[1] == X.shape[1]
-
-        # likelihood defaults to Gaussian
-        likelihood = likelihoods.Gaussian(Y, normalize=normalize_Y)
-
-        SVIGP.__init__(self, X, likelihood, kernel, Z, q_u=q_u, batchsize=batchsize)
-        self.load_batch()
-