working mean function examples

GPy/core/gp.py

@@ -82,7 +82,7 @@ class GP(Model):
             assert isinstance(self.mean_function, Mapping)
             assert mean_function.input_dim == self.input_dim
             assert mean_function.output_dim == self.output_dim
-            self.add_parameter(mean_function)
+            self.link_parameter(mean_function)
 
 
         #find a sensible inference method
@@ -166,6 +166,8 @@ class GP(Model):
         self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.likelihood, self.Y_normalized, self.mean_function, self.Y_metadata)
         self.likelihood.update_gradients(self.grad_dict['dL_dthetaL'])
         self.kern.update_gradients_full(self.grad_dict['dL_dK'], self.X)
+        if self.mean_function is not None:
+            self.mean_function.update_gradients(self.grad_dict['dL_dm'], self.X)
 
     def log_likelihood(self):
         """

GPy/examples/regression.py

@@ -505,3 +505,48 @@ def uncertain_inputs_sparse_regression(max_iters=200, optimize=True, plot=True):
 
     print m
     return m
+
+def simple_mean_function(max_iters=100, optimize=True, plot=True):
+    """
+    The simplest possible mean function. No parameters, just a simple Sinusoid.
+    """
+    #create  simple mean function
+    mf = GPy.core.Mapping(1,1)
+    mf.f = np.sin
+    mf.update_gradients = lambda a,b: None
+
+    X = np.linspace(0,10,50).reshape(-1,1)
+    Y = np.sin(X) + 0.5*np.cos(3*X) + 0.1*np.random.randn(*X.shape)
+
+    k =GPy.kern.RBF(1)
+    lik = GPy.likelihoods.Gaussian()
+    m = GPy.core.GP(X, Y, kernel=k, likelihood=lik, mean_function=mf)
+    if optimize:
+        m.optimize(max_iters=max_iters)
+    if plot:
+        m.plot(plot_limits=(-10,15))
+    return m
+
+def parametric_mean_function(max_iters=100, optimize=True, plot=True):
+    """
+    A linear mean function with parameters that we'll learn alongside the kernel
+    """
+    #create  simple mean function
+    mf = GPy.core.Mapping(1,1)
+    mf.f = np.sin
+
+    X = np.linspace(0,10,50).reshape(-1,1)
+    Y = np.sin(X) + 0.5*np.cos(3*X) + 0.1*np.random.randn(*X.shape) + 3*X
+
+    mf = GPy.mappings.Linear(1,1)
+
+    k =GPy.kern.RBF(1)
+    lik = GPy.likelihoods.Gaussian()
+    m = GPy.core.GP(X, Y, kernel=k, likelihood=lik, mean_function=mf)
+    if optimize:
+        m.optimize(max_iters=max_iters)
+    if plot:
+        m.plot()
+    return m
+
+

GPy/inference/latent_function_inference/exact_gaussian_inference.py

@@ -63,4 +63,4 @@ class ExactGaussianInference(LatentFunctionInference):
 
         dL_dthetaL = likelihood.exact_inference_gradients(np.diag(dL_dK),Y_metadata)
 
-        return Posterior(woodbury_chol=LW, woodbury_vector=alpha, K=K), log_marginal, {'dL_dK':dL_dK, 'dL_dthetaL':dL_dthetaL}
+        return Posterior(woodbury_chol=LW, woodbury_vector=alpha, K=K), log_marginal, {'dL_dK':dL_dK, 'dL_dthetaL':dL_dthetaL, 'dL_dm':alpha}

GPy/mappings/linear.py

@@ -26,8 +26,8 @@ class Linear(Mapping):
 
     def __init__(self, input_dim, output_dim, name='linmap'):
         Mapping.__init__(self, input_dim=input_dim, output_dim=output_dim, name=name)
-        self.A = GPy.core.Param('A', np.random.randn(self.input_dim, self.output_dim))
-        self.add_parameter(self.A)
+        self.A = Param('A', np.random.randn(self.input_dim, self.output_dim))
+        self.link_parameter(self.A)
 
     def f(self, X):
         return np.dot(X, self.A)