diff --git a/GPy/core/parameterised.py b/GPy/core/parameterised.py
index c8d8ce4d..e091e820 100644
--- a/GPy/core/parameterised.py
+++ b/GPy/core/parameterised.py
@@ -85,7 +85,7 @@ class parameterised(object):
             else:
                 return self._get_params()[matches]
         else:
-            raise AttributeError, "no parameter matches %s" % name
+            raise AttributeError, "no parameter matches %s" % regexp
 
     def __setitem__(self, name, val):
         """
diff --git a/GPy/examples/regression.py b/GPy/examples/regression.py
index 310a0691..7989b3c1 100644
--- a/GPy/examples/regression.py
+++ b/GPy/examples/regression.py
@@ -181,7 +181,7 @@ def coregionalisation_sparse(optim_iters=100):
     return m
 
 
-def multiple_optima(gene_number=937,resolution=80, model_restarts=10, seed=10000, optim_iters=100):
+def multiple_optima(gene_number=937,resolution=80, model_restarts=10, seed=10000, optim_iters=300):
     """Show an example of a multimodal error surface for Gaussian process regression. Gene 939 has bimodal behaviour where the noisey mode is higher."""
 
     # Contour over a range of length scales and signal/noise ratios.
@@ -197,7 +197,7 @@ def multiple_optima(gene_number=937,resolution=80, model_restarts=10, seed=10000
     data['Y'] = data['Y'] - np.mean(data['Y'])
 
     lls = GPy.examples.regression._contour_data(data, length_scales, log_SNRs, GPy.kern.rbf)
-    pb.contour(length_scales, log_SNRs, np.exp(lls), 20)
+    pb.contour(length_scales, log_SNRs, np.exp(lls), 20, cmap=pb.cm.jet)
     ax = pb.gca()
     pb.xlabel('length scale')
     pb.ylabel('log_10 SNR')
@@ -211,18 +211,20 @@ def multiple_optima(gene_number=937,resolution=80, model_restarts=10, seed=10000
     optim_point_y = np.empty(2)
     np.random.seed(seed=seed)
     for i in range(0, model_restarts):
-        kern = GPy.kern.rbf(1, variance=np.random.exponential(1.), lengthscale=np.random.exponential(50.)) + GPy.kern.white(1,variance=np.random.exponential(1.))
+        #kern = GPy.kern.rbf(1, variance=np.random.exponential(1.), lengthscale=np.random.exponential(50.))
+        kern = GPy.kern.rbf(1, variance=np.random.uniform(1e-3,1), lengthscale=np.random.uniform(5,50))
 
         m = GPy.models.GP_regression(data['X'],data['Y'], kernel=kern)
-        optim_point_x[0] = m.get('rbf_lengthscale')
-        optim_point_y[0] = np.log10(m.get('rbf_variance')) - np.log10(m.get('white_variance'));
+        m['noise_variance'] = np.random.uniform(1e-3,1)
+        optim_point_x[0] = m['rbf_lengthscale']
+        optim_point_y[0] = np.log10(m['rbf_variance']) - np.log10(m['noise_variance']);
 
         # optimize
         m.ensure_default_constraints()
-        m.optimize(xtol=1e-6, ftol=1e-6, max_f_eval=optim_iters)
+        m.optimize('scg', xtol=1e-6, ftol=1e-6, max_f_eval=optim_iters)
 
-        optim_point_x[1] = m.get('rbf_lengthscale')
-        optim_point_y[1] = np.log10(m.get('rbf_variance')) - np.log10(m.get('white_variance'));
+        optim_point_x[1] = m['rbf_lengthscale']
+        optim_point_y[1] = np.log10(m['rbf_variance']) - np.log10(m['noise_variance']);
 
         pb.arrow(optim_point_x[0], optim_point_y[0], optim_point_x[1]-optim_point_x[0], optim_point_y[1]-optim_point_y[0], label=str(i), head_length=1, head_width=0.5, fc='k', ec='k')
         models.append(m)
@@ -231,39 +233,32 @@ def multiple_optima(gene_number=937,resolution=80, model_restarts=10, seed=10000
     ax.set_ylim(ylim)
     return (models, lls)
 
-def _contour_data(data, length_scales, log_SNRs, signal_kernel_call=GPy.kern.rbf):
+def _contour_data(data, length_scales, log_SNRs, kernel_call=GPy.kern.rbf):
     """Evaluate the GP objective function for a given data set for a range of signal to noise ratios and a range of lengthscales.
 
     :data_set: A data set from the utils.datasets director.
     :length_scales: a list of length scales to explore for the contour plot.
     :log_SNRs: a list of base 10 logarithm signal to noise ratios to explore for the contour plot.
-    :signal_kernel: a kernel to use for the 'signal' portion of the data."""
+    :kernel: a kernel to use for the 'signal' portion of the data."""
 
     lls = []
     total_var = np.var(data['Y'])
+    kernel = kernel_call(1, variance=1., lengthscale=1.)
+    model = GPy.models.GP_regression(data['X'], data['Y'], kernel=kernel)
     for log_SNR in log_SNRs:
-        SNR = 10**log_SNR
+        SNR = 10.**log_SNR
+        noise_var = total_var/(1.+SNR)
+        signal_var = total_var - noise_var
+        model.kern['.*variance'] = signal_var
+        model['noise_variance'] = noise_var
         length_scale_lls = []
+
         for length_scale in length_scales:
-            noise_var = 1.
-            signal_var = SNR
-            noise_var = noise_var/(noise_var + signal_var)*total_var
-            signal_var = signal_var/(noise_var + signal_var)*total_var
-
-            signal_kernel = signal_kernel_call(1, variance=signal_var, lengthscale=length_scale)
-            noise_kernel = GPy.kern.white(1, variance=noise_var)
-            kernel = signal_kernel + noise_kernel
-            K = kernel.K(data['X'])
-            total_var = (np.dot(np.dot(data['Y'].T,GPy.util.linalg.pdinv(K)[0]), data['Y'])/data['Y'].shape[0])[0,0]
-            noise_var *= total_var
-            signal_var *= total_var
-
-            kernel = signal_kernel_call(1, variance=signal_var, lengthscale=length_scale) + GPy.kern.white(1, variance=noise_var)
-
-            model = GPy.models.GP_regression(data['X'], data['Y'], kernel=kernel)
-            model.constrain_positive('')
+            model['.*lengthscale'] = length_scale
             length_scale_lls.append(model.log_likelihood())
+
         lls.append(length_scale_lls)
+
     return np.array(lls)
 
 def sparse_GP_regression_1D(N = 400, M = 5, optim_iters=100):