added file:transformations

GPy/core/transformations.py

@@ -0,0 +1,86 @@
+# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+
+import numpy as np
+
+class transformation(object):
+    def __init__(self):
+        #set the domain. Suggest we use 'positive', 'bounded', etc
+        self.domain = 'undefined'
+    def f(self, x):
+        raise NotImplementedError
+
+    def finv(self,x):
+        raise NotImplementedError
+
+    def gradfactor(self,f):
+        """ df_dx evaluated at self.f(x)=f"""
+        raise NotImplementedError
+    def initialize(self,f):
+        """ produce a sensible initial values for f(x)"""
+        raise NotImplementedError
+    def __str__(self):
+        raise NotImplementedError
+
+class logexp(transformation):
+    def __init__(self):
+        self.domain= 'positive'
+    def f(self,x):
+        return np.log(1. + np.exp(x))
+    def finv(self,f):
+        return np.log(np.exp(f) - 1.)
+    def gradfactor(self,f):
+        ef = np.exp(f)
+        return (ef - 1.)/ef
+    def initialize(self,f):
+        return np.abs(f)
+    def __str__(self):
+        return '(+ve)'
+
+class exponent(transformation):
+    def __init__(self):
+        self.domain= 'positive'
+    def f(self,x):
+        return np.exp(x)
+    def finv(self,x):
+        return np.log(x)
+    def gradfactor(self,f):
+        return f
+    def initialize(self,f):
+        return np.abs(f)
+    def __str__(self):
+        return '(+ve)'
+
+class negative_exponent(transformation):
+    def __init__(self):
+        self.domain= 'negative'
+    def f(self,x):
+        return -np.exp(self.x)
+    def finv(self,x):
+        return np.log(-x)
+    def gradfactor(self,f):
+        return f
+    def initialize(self,f):
+        return -np.abs(f)
+    def __str__(self):
+        return '(-ve)'
+
+
+class logistic(transformation):
+    def __init__(self,lower,upper):
+        self.domain= 'bounded'
+        assert lower < upper
+        self.lower, self.upper = float(lower), float(upper)
+        self.difference = self.upper - self.lower
+    def f(self,x):
+        return self.lower +  self.difference/(1.+np.exp(-x))
+    def finv(self,f):
+        return np.log(np.clip(f - self.lower, 1e-10, np.inf) / np.clip(self.upper - f, 1e-10, np.inf))
+    def gradfactor(self,f):
+        return (f-self.lower)*(self.upper-f)/self.difference
+    def initialize(self,f):
+        return self.f(f*0.)
+    def __str__(self):
+        return '({},{})'.format(self.lower,self.upper)
+