def get_log_likelihood_offset(inputs,data,clust):
<EOL>if len(clust)==<NUM_LIT:1>: <EOL><INDENT>return get_log_likelihood(inputs,data,clust)<EOL><DEDENT>S = data[<NUM_LIT:0>].shape[<NUM_LIT:0>] <EOL>X = np.zeros([<NUM_LIT:0>,<NUM_LIT:2>]) <EOL>Y = np.zeros([<NUM_LIT:0>,S])<EOL>for i,p in enumerate(clust):<EOL><INDENT>idx = i*np.ones([inputs[p].shape[<NUM_LIT:0>],<NUM_LIT:1>])<EOL>X = np.vstack([X,np.hstack([inputs[p],idx])])<EOL>Y = np.vstack([Y,data[p].T])<EOL><DEDENT>m = GPy.models.GPOffsetRegression(X,Y)<EOL>m.offset.set_prior(GPy.priors.Gaussian(<NUM_LIT:0>,<NUM_LIT:20>)) <EOL>m.optimize()<EOL>ll = m.log_likelihood()<EOL>offset = m.offset.values[<NUM_LIT:0>]<EOL>return ll,offset<EOL>
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