New FITC model and other stuff

GPy/models/GP_regression.py

@@ -11,7 +11,7 @@ class GP_regression(GP):
     """
     Gaussian Process model for regression
 
-    This is a thin wrapper around the models.GP class, with a set of sensible defalts
+    This is a thin wrapper around the models.GP class, with a set of sensible defaults
 
     :param X: input observations
     :param Y: observed values

GPy/models/__init__.py

@@ -6,10 +6,9 @@ from GP_regression import GP_regression
 from GP_classification import GP_classification
 from sparse_GP_regression import sparse_GP_regression
 from sparse_GP_classification import sparse_GP_classification
+from FITC_classification import FITC_classification
 from GPLVM import GPLVM
 from warped_GP import warpedGP
 from sparse_GPLVM import sparse_GPLVM
 from Bayesian_GPLVM import Bayesian_GPLVM
 from mrd import MRD
-from generalized_FITC import generalized_FITC
-from FITC import FITC

GPy/models/sparse_GP_classification.py

@@ -7,13 +7,12 @@ from ..core import sparse_GP
 from .. import likelihoods
 from .. import kern
 from ..likelihoods import likelihood
-from GP_regression import GP_regression
 
 class sparse_GP_classification(sparse_GP):
     """
     sparse Gaussian Process model for classification
 
-    This is a thin wrapper around the sparse_GP class, with a set of sensible defalts
+    This is a thin wrapper around the sparse_GP class, with a set of sensible defaults
 
     :param X: input observations
     :param Y: observed values
@@ -25,8 +24,6 @@ class sparse_GP_classification(sparse_GP):
     :type normalize_Y: False|True
     :rtype: model object
 
-    .. Note:: Multiple independent outputs are allowed using columns of Y
-
     """
 
     def __init__(self, X, Y=None, likelihood=None, kernel=None, normalize_X=False, normalize_Y=False, Z=None, M=10):