first adjustments to the model and gps -> updates and gradient transforms

GPy/core/gp.py

@@ -3,9 +3,7 @@
 
 
 import numpy as np
-import pylab as pb
-from .. import kern
-from ..util.linalg import pdinv, mdot, tdot, dpotrs, dtrtrs
+from ..util.linalg import pdinv, tdot, dpotrs, dtrtrs
 from ..likelihoods import EP
 from gp_base import GPBase
 
@@ -35,6 +33,7 @@ class GP(GPBase):
         #self._set_params(self._get_params())
 
     def parameters_changed(self):
+        super(GP, self).parameters_changed()
         self.K = self.kern.K(self.X)
         self.K += self.likelihood.covariance_matrix
         self.Ki, self.L, self.Li, self.K_logdet = pdinv(self.K)
@@ -51,11 +50,11 @@ class GP(GPBase):
             tmp, _ = dpotrs(self.L, np.asfortranarray(tmp.T), lower=1)
             self.dL_dK = 0.5 * (tmp - self.output_dim * self.Ki)
 
-    def _get_params(self):
-        return np.hstack((self.kern._get_params_transformed(), self.likelihood._get_params()))
+#     def _get_params(self):
+#         return np.hstack((self.kern._get_params_transformed(), self.likelihood._get_params()))
 
-    def _get_param_names(self):
-        return self.kern._get_param_names_transformed() + self.likelihood._get_param_names()
+#     def _get_param_names(self):
+#         return self.kern._get_param_names_transformed() + self.likelihood._get_param_names()
 
     def update_likelihood_approximation(self, **kwargs):
         """
@@ -66,7 +65,7 @@ class GP(GPBase):
         """
         self.likelihood.restart()
         self.likelihood.fit_full(self.kern.K(self.X), **kwargs)
-        self._set_params(self._get_params()) # update the GP
+#         self._set_params(self._get_params()) # update the GP
 
     def _model_fit_term(self):
         """

GPy/core/gp_base.py

@@ -2,7 +2,7 @@ import numpy as np
 from .. import kern
 from ..util.plot import gpplot, Tango, x_frame1D, x_frame2D
 import pylab as pb
-from GPy.core.model import Model
+from model import Model
 import warnings
 from ..likelihoods import Gaussian, Gaussian_Mixed_Noise
 
@@ -12,6 +12,8 @@ class GPBase(Model):
     sparse_GP and GP models.
     """
     def __init__(self, X, likelihood, kernel, normalize_X=False):
+        super(GPBase, self).__init__()
+        
         self.X = X
         assert len(self.X.shape) == 2
         self.num_data, self.input_dim = self.X.shape
@@ -29,10 +31,9 @@ class GPBase(Model):
             self._Xoffset = np.zeros((1, self.input_dim))
             self._Xscale = np.ones((1, self.input_dim))
         
-        self.set_as_parameter(self.kern._parameters_)
-        self.set_as_parameter(self.likelihood._parameters_)
+        self.set_as_parameters(*self.kern._parameters_, highest_parent=self)
+        self.set_as_parameters(*self.likelihood._parameters_, highest_parent=self)
 
-        super(GPBase, self).__init__()
         # Model.__init__(self)
         # All leaf nodes should call self._set_params(self._get_params()) at
         # the end

GPy/core/model.py

@@ -6,7 +6,7 @@ from .. import likelihoods
 from ..inference import optimization
 from ..util.linalg import jitchol
 from ..util.misc import opt_wrapper
-from parameterized import Parameterized
+from parameterized import Parameterized, __fixed__
 import multiprocessing as mp
 import numpy as np
 from domains import _POSITIVE, _REAL
@@ -18,7 +18,8 @@ class Model(Parameterized):
     _fail_count = 0 # Count of failed optimization steps (see objective)
     _allowed_failures = 10 # number of allowed failures
     def __init__(self):
-        Parameterized.__init__(self)
+        super(Model, self).__init__()#Parameterized.__init__(self)
+        self.priors = []
         self._priors = ParameterIndexOperations()
         self.optimization_runs = []
         self.sampling_runs = []
@@ -151,15 +152,19 @@ class Model(Parameterized):
         [np.put(ret, i, p.lnpdf_grad(xx)) for i, (p, xx) in enumerate(zip(self.priors, x)) if not p is None]
         return ret
 
+
     def _transform_gradients(self, g):
         x = self._get_params()
-        for index, constraint in zip(self.constrained_indices, self.constraints):
+        for constraint, index in self._constraints_.iteritems():
+            if constraint != __fixed__:
                 g[index] = g[index] * constraint.gradfactor(x[index])
-        [np.put(g, i, v) for i, v in [(t[0], np.sum(g[t])) for t in self.tied_indices]]
-        if len(self.tied_indices) or len(self.fixed_indices):
-            to_remove = np.hstack((self.fixed_indices + [t[1:] for t in self.tied_indices]))
-            return np.delete(g, to_remove)
-        else:
+        #[np.put(g, i, v) for i, v in [(t[0], np.sum(g[t])) for t in self.tied_indices]]
+        [np.put(g, i, v) for i, v in [[i, t.sum()] for p in self._parameters_ for t,i in p._tied_to_me_.iteritems()]]
+#         if len(self.tied_indices) or len(self.fixed_indices):
+#             to_remove = np.hstack((self.fixed_indices + [t[1:] for t in self.tied_indices]))
+#             return np.delete(g, to_remove)
+#         else:
+        if self._fixes_ is not None: return g[self._fixes_]
         return g
 
     def randomize(self):
@@ -262,14 +267,18 @@ class Model(Parameterized):
         """
         positive_strings = ['variance', 'lengthscale', 'precision', 'kappa']
         # param_names = self._get_param_names()
-        currently_constrained = self.all_constrained_indices()
-        to_make_positive = []
         for s in positive_strings:
-            for i in self.grep_param_names(".*" + s):
-                if not (i in currently_constrained):
-                    to_make_positive.append(i)
-        if len(to_make_positive):
-            self.constrain_positive(np.asarray(to_make_positive), warning=warning)
+            paramlist = self.grep_param_names(".*"+s)
+            if paramlist:
+                self.__getitem__(None, paramlist).constrain_positive(warning=warning)
+#         currently_constrained = self.all_constrained_indices()
+#         to_make_positive = []
+#         for s in positive_strings:
+#             for i in self.grep_param_names(".*" + s):
+#                 if not (i in currently_constrained):
+#                     to_make_positive.append(i)
+#         if len(to_make_positive):
+#             self.constrain_positive(np.asarray(to_make_positive), warning=warning)
 
     def objective_function(self, x):
         """
@@ -277,7 +286,7 @@ class Model(Parameterized):
         the likelihood and the priors.
         
         Failures are handled robustly. The algorithm will try several times to
-        return the objective, and will raise the original exception if it
+        return the objective, and will raise the original exception if
         the objective cannot be computed.
 
         :param x: the parameters of the model.
@@ -298,7 +307,7 @@ class Model(Parameterized):
         Gets the gradients from the likelihood and the priors.
 
         Failures are handled robustly. The algorithm will try several times to
-        return the gradients, and will raise the original exception if it
+        return the gradients, and will raise the original exception if
         the objective cannot be computed.
 
         :param x: the parameters of the model.
@@ -345,8 +354,8 @@ class Model(Parameterized):
         :type max_f_eval: int
         :messages: whether to display during optimisation
         :type messages: bool
-        :param optimzer: which optimizer to use (defaults to self.preferred optimizer)
-        :type optimzer: string TODO: valid strings?
+        :param optimizer: which optimizer to use (defaults to self.preferred optimizer)
+        :type optimizer: string TODO: valid strings?
         """
         if optimizer is None:
             optimizer = self.preferred_optimizer

GPy/models/gp_regression.py

@@ -31,7 +31,7 @@ class GPRegression(GP):
 
         likelihood = likelihoods.Gaussian(Y, normalize=normalize_Y)
 
-        GP.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
+        super(GPRegression, self).__init__(X, likelihood, kernel, normalize_X=normalize_X)
         self.ensure_default_constraints()
 
     def getstate(self):