diff --git a/GPy/models/gp_offset_regression.py b/GPy/models/gp_offset_regression.py
new file mode 100644
index 00000000..3699223e
--- /dev/null
+++ b/GPy/models/gp_offset_regression.py
@@ -0,0 +1,92 @@
+# Copyright (c) 2012 - 2014 the GPy Austhors (see AUTHORS.txt)
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+# Written by Mike Smith. michaeltsmith.org.uk
+
+import numpy as np
+from ..core import GP
+from .. import likelihoods
+from .. import kern
+from ..core import Param
+
+class GPOffsetRegression(GP):
+    """
+    Gaussian Process model for offset regression
+
+    :param X: input observations, we assume for this class that this has one dimension of actual inputs and the last dimension should be the index of the cluster (so X should be Nx2)
+    :param Y: observed values (Nx1?)
+    :param kernel: a GPy kernel, defaults to rbf
+    :param Norm normalizer: [False]
+    :param noise_var: the noise variance for Gaussian likelhood, defaults to 1.
+
+        Normalize Y with the norm given.
+        If normalizer is False, no normalization will be done
+        If it is None, we use GaussianNorm(alization)
+
+    .. Note:: Multiple independent outputs are allowed using columns of Y
+
+    """
+
+    def __init__(self, X, Y, kernel=None, Y_metadata=None, normalizer=None, noise_var=1., mean_function=None):
+
+        assert X.shape[1]>1, "Need at least two input dimensions, as last dimension is the label of the cluster"
+        if kernel is None:
+            kernel = kern.RBF(X.shape[1]-1)
+
+        #self._log_marginal_likelihood = np.nan #todo
+        
+        likelihood = likelihoods.Gaussian(variance=noise_var)
+        self.X_fixed = X[:,:-1]
+        self.selected = np.array([int(x) for x in X[:,-1]])
+        
+        
+        super(GPOffsetRegression, self).__init__(X, Y, kernel, likelihood, name='GP offset regression', Y_metadata=Y_metadata, normalizer=normalizer, mean_function=mean_function)
+        maxcluster = np.max(self.selected)
+        self.offset = Param('offset', np.zeros(maxcluster))
+        #self.offset.set_prior(...)
+        self.link_parameter(self.offset)
+        
+    #def dr_doffset(self, X, sel): #how much r changes wrt the offset hyperparameters
+        
+    #def dL_doffset(self, X, sel):
+    #    dL_dr = self.dK_dr_via_X(X, X) * dL_dK
+        
+        
+        
+    def dr_doffset(self,X,sel,delta):
+        #given an input matrix, X and the offsets (delta)
+        #finds dr/dDelta
+        #returns them as a list, one for each offset (delta).        
+        #get the input values
+
+        #a matrix G represents the effect of increasing the offset on the radius passed to the kernel for each input. For example
+        #what effect will increasing offset 4 have on the kernel output of inputs 5 and 8? Answer: Gs[4][5,8]... (positive or negative)
+        Gs = []
+        for i,d in enumerate(delta):
+            #X[sel==(i+1)]-=d
+            G = np.repeat(np.array(sel==(i+1))[:,None]*1,len(X),axis=1) - np.repeat(np.array(sel==(i+1))[None,:]*1,len(X),axis=0)
+            Gs.append(G)
+        #does subtracting the two Xs end up positive or negative (if negative we need to flip the sign in G).
+        w = np.repeat(X,len(X),axis=1) - np.repeat(X.T,len(X),axis=0)
+        dr_doffsets = []
+        for i,d in enumerate(delta):
+            dr_doffset = np.sign(w * Gs[i])
+            #print "dr_doffset %d" % i
+            #print dr_doffset
+            #print Gs[i]
+            #print w
+            dr_doffsets.append(dr_doffset)
+        return dr_doffsets
+        
+    def parameters_changed(self):
+        offsets = np.hstack([0.0,self.offset.values])[:,None]
+        
+        self.X = self.X_fixed - offsets[self.selected]
+        super(GPOffsetRegression, self).parameters_changed()
+        
+        dL_dr = self.kern.dK_dr_via_X(self.X, self.X) * self.grad_dict['dL_dK']
+        
+        dr_doff = self.dr_doffset(self.X,self.selected,self.offset.values)
+        for i in range(len(dr_doff)):
+            dL_doff = dL_dr * dr_doff[i]
+            self.offset.gradient[i] = -np.sum(dL_doff)
+        
diff --git a/GPy/util/cluster_with_offset.py b/GPy/util/cluster_with_offset.py
new file mode 100644
index 00000000..103bdaca
--- /dev/null
+++ b/GPy/util/cluster_with_offset.py
@@ -0,0 +1,180 @@
+# Copyright (c) 2016, Mike Smith
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+import GPy
+import numpy as np
+
+def get_log_likelihood(inputs,data,clust):
+    """Get the LL of a combined set of clusters, ignoring time series offsets.
+    
+    Get the log likelihood of a cluster without worrying about the fact
+    different time series are offset. We're using it here really for those
+    cases in which we only have one cluster to get the loglikelihood of.
+    
+    arguments:
+    inputs -- the 'X's in a list, one item per cluster
+    data -- the 'Y's in a list, one item per cluster
+    clust -- list of clusters to use
+    
+    returns a tuple:
+    log likelihood and the offset (which is always zero for this model)
+    """
+ 
+    S = data[0].shape[0] #number of time series
+    
+    #build a new dataset from the clusters, by combining all clusters together
+    X = np.zeros([0,1])
+    Y = np.zeros([0,S])
+    
+    #for each person in the cluster,
+    #add their inputs and data to the new dataset
+    for p in clust:
+        X = np.vstack([X,inputs[p]])
+        Y = np.vstack([Y,data[p].T])
+        
+    #find the loglikelihood. We just add together the LL for each time series.
+    #ll=0
+    #for s in range(S):
+    #    m = GPy.models.GPRegression(X,Y[:,s][:,None])
+    #    m.optimize()
+    #    ll+=m.log_likelihood()
+
+    m = GPy.models.GPRegression(X,Y)
+    m.optimize()
+    ll=m.log_likelihood()    
+    return ll,0
+
+def get_log_likelihood_offset(inputs,data,clust):
+    """Get the log likelihood of a combined set of clusters, fitting the offsets
+    
+    arguments:
+    inputs -- the 'X's in a list, one item per cluster
+    data -- the 'Y's in a list, one item per cluster
+    clust -- list of clusters to use
+    
+    returns a tuple:
+    log likelihood and the offset
+    """    
+        
+    #if we've only got one cluster, the model has an error, so we want to just
+    #use normal GPRegression.
+    if len(clust)==1: 
+        return get_log_likelihood(inputs,data,clust)
+                
+    S = data[0].shape[0] #number of time series
+        
+    X = np.zeros([0,2]) #notice the extra column, this is for the cluster index
+    Y = np.zeros([0,S])
+    
+    #for each person in the cluster, add their inputs and data to the new
+    #dataset. Note we add an index identifying which person is which data point.
+    #This is for the offset model to use, to allow it to know which data points
+    #to shift.
+    for i,p in enumerate(clust):
+        idx = i*np.ones([inputs[p].shape[0],1])
+        X = np.vstack([X,np.hstack([inputs[p],idx])])
+        Y = np.vstack([Y,data[p].T])
+        
+    m = GPy.models.GPOffsetRegression(X,Y)
+    #TODO: How to select a sensible prior?
+    m.offset.set_prior(GPy.priors.Gaussian(0,20)) 
+    #TODO: Set a sensible start value for the length scale,
+    #make it long to help the offset fit.
+    
+    m.optimize()
+    
+    ll = m.log_likelihood()
+    offset = m.offset.values[0]
+    return ll,offset
+
+def cluster(data,inputs,verbose=False):
+    """Clusters data
+    
+    Using the new offset model, this method uses a greedy algorithm to cluster
+    the data. It starts with all the data points in separate clusters and tests
+    whether combining them increases the overall log-likelihood (LL). It then
+    iteratively joins pairs of clusters which cause the greatest increase in
+    the LL, until no join increases the LL.
+    
+    arguments:
+    inputs -- the 'X's in a list, one item per cluster
+    data -- the 'Y's in a list, one item per cluster
+    
+    returns a list of the clusters.    
+    """
+    N=len(data)
+    
+    
+    #Define a set of N active cluster
+    active = []
+    for p in range(0,N):
+        active.append([p])
+
+    loglikes = np.zeros(len(active))
+    loglikes[:] = None
+
+    pairloglikes = np.zeros([len(active),len(active)])
+    pairloglikes[:] = None
+    pairoffset = np.zeros([len(active),len(active)])
+
+    it = 0
+    while True:
+    
+        if verbose:
+            it +=1
+            print "Iteration %d" % it
+        
+        #Compute the log-likelihood of each cluster (add them together)
+        for clusti in range(len(active)):
+            if np.isnan(loglikes[clusti]):
+                loglikes[clusti], unused_offset = get_log_likelihood_offset(inputs,data,[clusti])
+
+            #try combining with each other cluster...
+            for clustj in range(clusti): #count from 0 to clustj-1
+                temp = [clusti,clustj]
+                if np.isnan(pairloglikes[clusti,clustj]):
+                    pairloglikes[clusti,clustj],pairoffset[clusti,clustj] = get_log_likelihood_offset(inputs,data,temp)
+
+        seploglikes = np.repeat(loglikes[:,None].T,len(loglikes),0)+np.repeat(loglikes[:,None],len(loglikes),1)
+        loglikeimprovement = pairloglikes - seploglikes #how much likelihood improves with clustering
+        top = np.unravel_index(np.nanargmax(pairloglikes-seploglikes), pairloglikes.shape)
+
+        #if loglikeimprovement.shape[0]<3:
+        # #no more clustering to do - this shouldn't happen really unless
+        # #we've set the threshold to apply clustering to less than 0
+        #    break 
+        
+        #if theres further clustering to be done...
+        if loglikeimprovement[top[0],top[1]]>0:
+            active[top[0]].extend(active[top[1]])
+            offset=pairoffset[top[0],top[1]]
+            inputs[top[0]] = np.vstack([inputs[top[0]],inputs[top[1]]-offset])
+            data[top[0]] = np.hstack([data[top[0]],data[top[1]]])
+            del inputs[top[1]]
+            del data[top[1]]
+            del active[top[1]]
+
+            #None = message to say we need to recalculate
+            pairloglikes[:,top[0]] = None 
+            pairloglikes[top[0],:] = None 
+            pairloglikes = np.delete(pairloglikes,top[1],0)
+            pairloglikes = np.delete(pairloglikes,top[1],1)
+            loglikes[top[0]] = None
+            loglikes = np.delete(loglikes,top[1])
+        else:
+            break
+            
+        #if loglikeimprovement[top[0],top[1]]>0:
+        #    print "joined"
+        #    print top
+        #    print offset
+        #    print offsets
+        #    print offsets[top[1]]-offsets[top[0]]
+
+    #TODO Add a way to return the offsets applied to all the time series
+    return active 
+
+#starttime = time.time()
+#active = cluster(data,inputs)
+#endtime = time.time()
+#print "TOTAL TIME %0.4f" % (endtime-starttime)