diff --git a/GPy/core/parameterization/transformations.py b/GPy/core/parameterization/transformations.py
index 51194865..36291ca3 100644
--- a/GPy/core/parameterization/transformations.py
+++ b/GPy/core/parameterization/transformations.py
@@ -4,10 +4,10 @@
 
 import numpy as np
 from domains import _POSITIVE,_NEGATIVE, _BOUNDED
-import sys
 import weakref
 
-_lim_val = -np.log(sys.float_info.epsilon)
+_exp_lim_val = np.finfo(np.float64).max
+_lim_val = np.log(_exp_lim_val)#-np.log(sys.float_info.epsilon)
 
 #===============================================================================
 # Fixing constants
@@ -34,6 +34,16 @@ class Transformation(object):
     def initialize(self, f):
         """ produce a sensible initial value for f(x)"""
         raise NotImplementedError
+    def plot(self, xlabel=r'transformed $\theta$', ylabel=r'$\theta$', axes=None, *args,**kw):
+        import sys
+        assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
+        import matplotlib.pyplot as plt
+        from ...plotting.matplot_dep import base_plots
+        x = np.linspace(-8,8)
+        base_plots.meanplot(x, self.f(x),axes=axes*args,**kw)
+        axes = plt.gca()
+        axes.set_xlabel(xlabel)
+        axes.set_ylabel(ylabel)
     def __str__(self):
         raise NotImplementedError
     def __repr__(self):
@@ -54,6 +64,24 @@ class Logexp(Transformation):
         return np.abs(f)
     def __str__(self):
         return '+ve'
+    
+
+class LogexpNeg(Transformation):
+    domain = _POSITIVE
+    def f(self, x):
+        return np.where(x>_lim_val, -x, -np.log(1. + np.exp(np.clip(x, -np.inf, _lim_val))))
+        #raises overflow warning: return np.where(x>_lim_val, x, np.log(1. + np.exp(x)))
+    def finv(self, f):
+        return np.where(f>_lim_val, 0, np.log(np.exp(-f) - 1.))
+    def gradfactor(self, f):
+        return np.where(f>_lim_val, -1, -1 + np.exp(-f))
+    def initialize(self, f):
+        if np.any(f < 0.):
+            print "Warning: changing parameters to satisfy constraints"
+        return np.abs(f)
+    def __str__(self):
+        return '+ve'
+
 
 class NegativeLogexp(Transformation):
     domain = _NEGATIVE