[classification] sparse gp classification and dtc update

GPy/models/sparse_gp_classification.py

@@ -11,7 +11,7 @@ from ..inference.latent_function_inference import expectation_propagation_dtc
 
 class SparseGPClassification(SparseGP):
     """
-    sparse Gaussian Process model for classification
+    Sparse Gaussian Process model for classification
 
     This is a thin wrapper around the sparse_GP class, with a set of sensible defaults
 
@@ -27,10 +27,7 @@ class SparseGPClassification(SparseGP):
 
     """
 
-    #def __init__(self, X, Y=None, likelihood=None, kernel=None, normalize_X=False, normalize_Y=False, Z=None, num_inducing=10):
     def __init__(self, X, Y=None, likelihood=None, kernel=None, Z=None, num_inducing=10, Y_metadata=None):
-
-
         if kernel is None:
             kernel = kern.RBF(X.shape[1])
 
@@ -43,4 +40,56 @@ class SparseGPClassification(SparseGP):
             assert Z.shape[1] == X.shape[1]
 
         SparseGP.__init__(self, X, Y, Z, kernel, likelihood, inference_method=expectation_propagation_dtc.EPDTC(), name='SparseGPClassification',Y_metadata=Y_metadata)
-    #def __init__(self, X, Y, Z, kernel, likelihood, inference_method=None, name='sparse gp', Y_metadata=None):
+
+class SparseGPClassificationUncertainInput(SparseGP):
+    """
+    Sparse Gaussian Process model for classification with uncertain inputs.
+
+    This is a thin wrapper around the sparse_GP class, with a set of sensible defaults
+
+    :param X: input observations
+    :type X: np.ndarray (num_data x input_dim)
+    :param X_variance: The uncertainty in the measurements of X (Gaussian variance, optional)
+    :type X_variance: np.ndarray (num_data x input_dim)
+    :param Y: observed values
+    :param kernel: a GPy kernel, defaults to rbf+white
+    :param Z: inducing inputs (optional, see note)
+    :type Z: np.ndarray (num_inducing x input_dim) | None
+    :param num_inducing: number of inducing points (ignored if Z is passed, see note)
+    :type num_inducing: int
+    :rtype: model object
+
+    .. Note:: If no Z array is passed, num_inducing (default 10) points are selected from the data. Other wise num_inducing is ignored
+    .. Note:: Multiple independent outputs are allowed using columns of Y
+    """
+    def __init__(self, X, X_variance, Y, kernel=None, Z=None, num_inducing=10, Y_metadata=None, normalizer=None):
+        from ..core.parameterization.variational import NormalPosterior
+        if kernel is None:
+            kernel = kern.RBF(X.shape[1])
+
+        likelihood = likelihoods.Bernoulli()
+
+        if Z is None:
+            i = np.random.permutation(X.shape[0])[:num_inducing]
+            Z = X[i].copy()
+        else:
+            assert Z.shape[1] == X.shape[1]
+
+        X = NormalPosterior(X, X_variance)
+
+        SparseGP.__init__(self, X, Y, Z, kernel, likelihood,
+                          inference_method=expectation_propagation_dtc.EPDTC(),
+                          name='SparseGPClassification', Y_metadata=Y_metadata, normalizer=normalizer)
+
+    def parameters_changed(self):
+        #Compute the psi statistics for N once, but don't sum out N in psi2
+        self.psi0 = self.kern.psi0(self.Z, self.X)
+        self.psi1 = self.kern.psi1(self.Z, self.X)
+        self.psi2 = self.kern.psi2n(self.Z, self.X)
+        self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.Z, self.likelihood, self.Y, self.Y_metadata, psi0=self.psi0, psi1=self.psi1, psi2=self.psi2)
+        self._update_gradients()
+
+
+
+
+