Merge branch 'devel' into pickle

GPy/models/__init__.py

@@ -4,6 +4,7 @@
 from gp_regression import GPRegression
 from gp_classification import GPClassification
 from sparse_gp_regression import SparseGPRegression
+from svigp_regression import SVIGPRegression
 from sparse_gp_classification import SparseGPClassification
 from fitc_classification import FITCClassification
 from gplvm import GPLVM

GPy/models/bayesian_gplvm.py

@@ -46,7 +46,7 @@ class BayesianGPLVM(SparseGP, GPLVM):
             kernel = kern.rbf(input_dim) + kern.white(input_dim)
 
         SparseGP.__init__(self, X, likelihood, kernel, Z=Z, X_variance=X_variance, **kwargs)
-        self._set_params(self._get_params())
+        self.ensure_default_constraints()
 
     def __getstate__(self):
         """

GPy/models/fitc_classification.py

@@ -44,4 +44,4 @@ class FITCClassification(FITC):
             assert Z.shape[1]==X.shape[1]
 
         FITC.__init__(self, X, likelihood, kernel, Z=Z, normalize_X=normalize_X)
-        self._set_params(self._get_params())
+        self.ensure_default_constraints()

GPy/models/gp_classification.py

@@ -38,4 +38,4 @@ class GPClassification(GP):
                 raise Warning, 'likelihood.data and Y are different.'
 
         GP.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
-        self._set_params(self._get_params())
+        self.ensure_default_constraints()

GPy/models/gp_regression.py

@@ -32,4 +32,4 @@ class GPRegression(GP):
         likelihood = likelihoods.Gaussian(Y,normalize=normalize_Y)
 
         GP.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
-        self._set_params(self._get_params())
+        self.ensure_default_constraints()

GPy/models/gplvm.py

@@ -33,7 +33,7 @@ class GPLVM(GP):
             kernel = kern.rbf(input_dim, ARD=input_dim > 1) + kern.bias(input_dim, np.exp(-2)) + kern.white(input_dim, np.exp(-2))
         likelihood = Gaussian(Y, normalize=normalize_Y)
         GP.__init__(self, X, likelihood, kernel, normalize_X=False)
-        self._set_params(self._get_params())
+        self.ensure_default_constraints()
 
     def initialise_latent(self, init, input_dim, Y):
         if init == 'PCA':

GPy/models/mrd.py

@@ -82,7 +82,7 @@ class MRD(Model):
         self.MQ = self.num_inducing * self.input_dim
 
         Model.__init__(self)
-        self._set_params(self._get_params())
+        self.ensure_default_constraints()
 
     def __getstate__(self):
         return [self.names,

GPy/models/sparse_gp_classification.py

@@ -44,4 +44,4 @@ class SparseGPClassification(SparseGP):
             assert Z.shape[1]==X.shape[1]
 
         SparseGP.__init__(self, X, likelihood, kernel, Z=Z, normalize_X=normalize_X)
-        self._set_params(self._get_params())
+        self.ensure_default_constraints()

GPy/models/sparse_gp_regression.py

@@ -42,4 +42,4 @@ class SparseGPRegression(SparseGP):
         likelihood = likelihoods.Gaussian(Y, normalize=normalize_Y)
 
         SparseGP.__init__(self, X, likelihood, kernel, Z=Z, normalize_X=normalize_X, X_variance=X_variance)
-        self._set_params(self._get_params())
+        self.ensure_default_constraints()

GPy/models/sparse_gplvm.py

@@ -26,6 +26,7 @@ class SparseGPLVM(SparseGPRegression, GPLVM):
     def __init__(self, Y, input_dim, kernel=None, init='PCA', num_inducing=10):
         X = self.initialise_latent(init, input_dim, Y)
         SparseGPRegression.__init__(self, X, Y, kernel=kernel, num_inducing=num_inducing)
+        self.ensure_default_constraints()
 
     def _get_param_names(self):
         return (sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])

GPy/models/svigp_regression.py

@@ -0,0 +1,44 @@
+# Copyright (c) 2012, James Hensman
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+import numpy as np
+from ..core import SVIGP
+from .. import likelihoods
+from .. import kern
+
+class SVIGPRegression(SVIGP):
+    """
+    Gaussian Process model for regression
+
+    This is a thin wrapper around the SVIGP class, with a set of sensible defalts
+
+    :param X: input observations
+    :param Y: observed values
+    :param kernel: a GPy kernel, defaults to rbf+white
+    :param normalize_X:  whether to normalize the input data before computing (predictions will be in original scales)
+    :type normalize_X: False|True
+    :param normalize_Y:  whether to normalize the input data before computing (predictions will be in original scales)
+    :type normalize_Y: False|True
+    :rtype: model object
+
+    .. Note:: Multiple independent outputs are allowed using columns of Y
+
+    """
+
+    def __init__(self, X, Y, kernel=None, Z=None, num_inducing=10, q_u=None, batchsize=10):
+        # kern defaults to rbf (plus white for stability)
+        if kernel is None:
+            kernel = kern.rbf(X.shape[1], variance=1., lengthscale=4.) + kern.white(X.shape[1], 1e-3)
+
+        # Z defaults to a subset of the data
+        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]
+
+        # likelihood defaults to Gaussian
+        likelihood = likelihoods.Gaussian(Y, normalize=False)
+
+        SVIGP.__init__(self, X, likelihood, kernel, Z, q_u=q_u, batchsize=batchsize)
+        self.load_batch()