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stability enhancing clipping in logexp_clipped and reverse of stability clipping of parameters

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Max Zwiessele 2013-05-16 15:17:54 +01:00
parent 524c6e26bf
commit ba4bd50924
2 changed files with 46 additions and 45 deletions
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26
GPy/core/transformations.py
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@ -39,23 +39,25 @@ class logexp(transformation):
return '(+ve)'
class logexp_clipped(transformation):
max_bound = 1e300
max_bound = 1e10
min_bound = 1e-10
log_max_bound = np.log(max_bound)
def __init__(self, lower=1e-15):
log_min_bound = np.log(min_bound)
def __init__(self, lower=1e-6):
self.domain = 'positive'
self.lower = lower
def f(self, x):
exp = np.exp(np.where(x > self.log_max_bound, self.log_max_bound, x))
exp = np.exp(np.clip(x, self.log_min_bound, self.log_max_bound))
f = np.log(1. + exp)
return f
def finv(self, f):
return np.log(np.exp(f) - 1.)
return np.log(np.exp(np.clip(f, self.min_bound, self.max_bound)) - 1.)
def gradfactor(self, f):
ef = np.exp(f)
gf = (ef - 1.) / ef
return np.where(f < self.lower, 0, gf)
def initialize(self,f):
if np.any(f<0.):
def initialize(self, f):
if np.any(f < 0.):
print "Warning: changing parameters to satisfy constraints"
return np.abs(f)
def __str__(self):
@ -71,7 +73,7 @@ class exponent(transformation):
def gradfactor(self, f):
return f
def initialize(self, f):
if np.any(f<0.):
if np.any(f < 0.):
print "Warning: changing parameters to satisfy constraints"
return np.abs(f)
def __str__(self):
@ -87,7 +89,7 @@ class negative_exponent(transformation):
def gradfactor(self, f):
return f
def initialize(self, f):
if np.any(f>0.):
if np.any(f > 0.):
print "Warning: changing parameters to satisfy constraints"
return -np.abs(f)
def __str__(self):
@ -118,11 +120,11 @@ class logistic(transformation):
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):
if np.any(np.logical_or(f<self.lower,f>self.upper)):
return (f - self.lower) * (self.upper - f) / self.difference
def initialize(self, f):
if np.any(np.logical_or(f < self.lower, f > self.upper)):
print "Warning: changing parameters to satisfy constraints"
return np.where(np.logical_or(f<self.lower,f>self.upper),self.f(f*0.),f)
return np.where(np.logical_or(f < self.lower, f > self.upper), self.f(f * 0.), f)
def __str__(self):
return '({},{})'.format(self.lower, self.upper)