Changes to configuration file set up: now uses defaults.cfg, installation.cfg and searches locally for .gpy_user.cfg in the users home directory.

GPy/mappings/additive.py

@@ -0,0 +1,61 @@
+# Copyright (c) 2013, GPy authors (see AUTHORS.txt).
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+import numpy as np
+from ..core.mapping import Mapping
+import GPy
+
+class Additive(Mapping):
+    """
+    Mapping based on adding two existing mappings together.
+
+    .. math::
+
+       f(\mathbf{x}*) = f_1(\mathbf{x}*) + f_2(\mathbf(x)*)
+
+    :param mapping1: first mapping to add together.
+    :type mapping1: GPy.mappings.Mapping
+    :param mapping2: second mapping to add together.
+    :type mapping2: GPy.mappings.Mapping
+    :param tensor: whether or not to use the tensor product of input spaces
+    :type tensor: bool
+
+    """
+
+    def __init__(self, mapping1, mapping2, tensor=False):
+        if tensor:
+            input_dim = mapping1.input_dim + mapping2.input_dim
+        else:
+            input_dim = mapping1.input_dim
+            assert(mapping1.input_dim==mapping2.input_dim)
+        assert(mapping1.output_dim==mapping2.output_dim)
+        output_dim = mapping1.output_dim
+        Mapping.__init__(self, input_dim=input_dim, output_dim=output_dim)
+        self.mapping1 = mapping1
+        self.mapping2 = mapping2
+        self.num_params = self.mapping1.num_params + self.mapping2.num_params
+        self.name = self.mapping1.name + '+' + self.mapping2.name
+    def _get_param_names(self):
+        return self.mapping1._get_param_names + self.mapping2._get_param_names
+
+    def _get_params(self):
+        return np.hstack((self.mapping1._get_params() self.mapping2._get_params()))
+
+    def _set_params(self, x):
+        self.mapping1._set_params(x[:self.mapping1.num_params])
+        self.mapping2._set_params(x[self.mapping1.num_params:])
+        
+    def randomize(self):
+        self.mapping1._randomize()
+        self.mapping2._randomize()
+
+    def f(self, X):
+        return self.mapping1.f(X) + self.mapping2.f(X)
+
+    def df_dtheta(self, dL_df, X):
+        self._df_dA = (dL_df[:, :, None]*self.kern.K(X, self.X)[:, None, :]).sum(0).T
+        self._df_dbias = (dL_df.sum(0))
+        return np.hstack((self._df_dA.flatten(), self._df_dbias))
+
+    def df_dX(self, dL_df, X):
+        return self.kern.dK_dX((dL_df[:, None, :]*self.A[None, :, :]).sum(2), X, self.X)