biiig changes in tieing, and printing -> hirarchy now always shown

GPy/kern/constructors.py

@@ -228,7 +228,7 @@ def Matern52(input_dim, variance=1., lengthscale=None, ARD=False):
     part = parts.Matern52.Matern52(input_dim, variance, lengthscale, ARD)
     return kern(input_dim, [part])
 
-def bias(input_dim, variance=1.):
+def bias(input_dim, variance=1., name='bias'):
     """
      Construct a bias kernel.
 
@@ -238,7 +238,7 @@ def bias(input_dim, variance=1.):
     :type variance: float
 
     """
-    part = parts.bias.Bias(input_dim, variance)
+    part = parts.bias.Bias(input_dim, variance, name=name)
     return kern(input_dim, [part])
 
 def finite_dimensional(input_dim, F, G, variances=1., weights=None):

GPy/kern/kern.py

@@ -605,7 +605,7 @@ class Kern_check_model(Model):
         self.dL_dK = dL_dK
         #self.constrained_indices=[]
         #self.constraints=[]
-        Model.__init__(self)
+        Model.__init__(self, 'kernel_test_model')
 
     def is_positive_definite(self):
         v = np.linalg.eig(self.kernel.K(self.X))[0]
@@ -614,14 +614,14 @@ class Kern_check_model(Model):
         else:
             return True
         
-#     def _get_params(self):
-#         return self.kernel._get_params()
-# 
-#     def _get_param_names(self):
-#         return self.kernel._get_param_names()
-# 
-#     def _set_params(self, x):
-#         self.kernel._set_params(x)
+    def _get_params(self):
+        return self.kernel._get_params()
+ 
+    def _get_param_names(self):
+        return self.kernel._get_param_names()
+ 
+    def _set_params(self, x):
+        self.kernel._set_params(x)
 
     def log_likelihood(self):
         return (self.dL_dK*self.kernel.K(self.X, self.X2)).sum()

GPy/kern/parts/bias.py

@@ -8,14 +8,14 @@ import hashlib
 from GPy.core.parameter import Param
 
 class Bias(Kernpart):
-    def __init__(self,input_dim,variance=1.):
+    def __init__(self,input_dim,variance=1.,name=None):
         """
         :param input_dim: the number of input dimensions
         :type input_dim: int
         :param variance: the variance of the kernel
         :type variance: float
         """
-        super(Bias, self).__init__(input_dim, 'bias')
+        super(Bias, self).__init__(input_dim, name)
         self.variance = Param("variance", variance)
         self.add_parameter(self.variance)
         #self._set_params(np.array([variance]).flatten())

GPy/kern/parts/rbf.py

@@ -54,7 +54,7 @@ class RBF(Kernpart):
         self.lengthscale = Param('lengthscale', lengthscale)
         self.lengthscale.add_observer(self, self.update_lengthscale)
         self.add_parameters(self.variance, self.lengthscale)
-        self.parameters_changed()
+        self.parameters_changed() # initializes cache
         
         #self.update_inv_lengthscale(self.lengthscale)
         #self.parameters_changed()

GPy/kern/parts/rbf_inv.py

@@ -36,7 +36,9 @@ class RBFInv(RBF):
     def __init__(self, input_dim, variance=1., inv_lengthscale=None, ARD=False, name='inverse rbf'):
         #self.input_dim = input_dim
         #self.name = 'rbf_inv'
-        super(RBFInv, self).__init__(input_dim, variance=variance, lengthscale=1./np.array(inv_lengthscale), ARD=ARD, name=name)
+        if inv_lengthscale is not None: lengthscale = 1./np.array(inv_lengthscale)
+        else: lengthscale = None
+        super(RBFInv, self).__init__(input_dim, variance=variance, lengthscale=lengthscale, ARD=ARD, name=name)
         self.ARD = ARD
         if not ARD:
             self.num_params = 2