First draft of base symbolic object, compiling with symbolic mapping.

GPy/mappings/symbolic.py

@@ -0,0 +1,56 @@
+# Copyright (c) 2014 GPy Authors
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+import sympy as sym
+from ..core.mapping import Mapping, Bijective_mapping
+from ..core.symbolic import Symbolic_core
+import numpy as np
+
+class Symbolic(Mapping, Symbolic_core):
+    """
+    Symbolic mapping
+
+    Mapping where the form of the mapping is provided by a sympy expression.
+
+    """
+    def __init__(self, input_dim, output_dim, f=None, name='symbolic', param=None, func_modules=[]):
+
+
+        if f is None:
+            raise ValueError, "You must provide an argument for the function."
+
+        Mapping.__init__(self, input_dim, output_dim, name=name)
+        Symbolic_core.__init__(self, f, ['X'], derivatives = ['X', 'theta'], param=param, func_modules=func_modules)
+
+        self._initialize_cache()
+        self.parameters_changed()
+
+    def _initialize_cache(self):
+        self.x_0 = np.random.normal(size=(3, self.input_dim))
+
+
+    def parameters_changed(self):
+        self.eval_parameters_changed()
+
+    def update_cache(self, X):
+        self.eval_update_cache(X)
+
+    def update_gradients(self, partial, X):
+        self.eval_update_gradients(partial, X)
+
+    def gradients_X(self, partial, X):
+        return self.eval_gradients_X(partial, X)
+
+    def f(self, X):
+        """
+        """
+        return self.eval_f(X)
+
+
+    def df_dX(self, X):
+        """
+        """
+        pass
+
+    def df_dtheta(self, X):
+        pass