added gradient checker model

GPy/models/gradient_checker.py

@@ -0,0 +1,89 @@
+'''
+Created on 17 Jul 2013
+
+@author: maxz
+'''
+from GPy.core.model import Model
+import itertools
+import numpy
+
+def get_shape(x):
+    if isinstance(x, numpy.ndarray):
+        return x.shape
+    return ()
+
+def at_least_one_element(x):
+    if isinstance(x, (list, tuple)):
+        return x
+    return [x]
+
+def flatten_if_needed(x):
+    return numpy.atleast_1d(x).flatten()
+
+class GradientChecker(Model):
+
+    def __init__(self, f, df, x0, names=None, *args, **kwargs):
+        """
+        :param f: Function to check gradient for
+        :param df: Gradient of function to check
+        :param x0: 
+            Initial guess for inputs x (if it has a shape (a,b) this will be reflected in the parameter names).
+            Can be a list of arrays, if takes a list of arrays. This list will be passed 
+            to f and df in the same order as given here.
+            If only one argument, make sure not to pass a list!!!
+            
+        :type x0: [array-like] | array-like | float | int
+        :param names:
+            Names to print, when performing gradcheck. If a list was passed to x0
+            a list of names with the same length is expected.
+        :param args: Arguments passed as f(x, *args, **kwargs) and df(x, *args, **kwargs)
+        """
+        Model.__init__(self)
+        self.f = f
+        self.df = df
+        if isinstance(x0, (list, tuple)) and names is None:
+            self.shapes = [get_shape(xi) for xi in x0]
+            self.names = ['X{i}'.format(i=i) for i in range(len(x0))]
+        elif isinstance(x0, (list, tuple)) and names is not None:
+            self.shapes = [get_shape(xi) for xi in x0]
+            self.names = names
+        elif names is None:
+            self.names = ['X']
+            self.shapes = [get_shape(x0)]
+        else:
+            self.names = names
+            self.shapes = [get_shape(x0)]
+        for name, xi in zip(self.names, at_least_one_element(x0)):
+            self.__setattr__(name, xi)
+        self._param_names = []
+        for name, shape in zip(self.names, self.shapes):
+            self._param_names.extend(map(lambda nameshape: ('_'.join(nameshape)).strip('_'), itertools.izip(itertools.repeat(name), itertools.imap(lambda t: '_'.join(map(str, t)), itertools.product(*map(lambda xi: range(xi), shape))))))
+        self.args = args
+        self.kwargs = kwargs
+
+    def _get_x(self):
+        if len(self.names) > 1:
+            return [self.__getattribute__(name) for name in self.names]
+        return self.__getattribute__(self.names[0])
+
+    def log_likelihood(self):
+        return numpy.atleast_1d(self.f(self._get_x(), *self.args, **self.kwargs))
+
+    def _log_likelihood_gradients(self):
+        return numpy.atleast_1d(self.df(self._get_x(), *self.args, **self.kwargs))
+
+
+    def _get_params(self):
+        return numpy.atleast_1d(numpy.hstack(map(lambda name: flatten_if_needed(self.__getattribute__(name)), self.names)))
+
+
+    def _set_params(self, x):
+        current_index = 0
+        for name, shape in zip(self.names, self.shapes):
+            current_size = numpy.prod(shape)
+            self.__setattr__(name, x[current_index:current_index + current_size].reshape(shape))
+            current_index += current_size
+
+    def _get_param_names(self):
+        return self._param_names
+