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first attemot at the new constraint framework

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James Hensman 2013-04-30 11:25:57 +01:00
parent 50a68e1a65
commit 83510e7515
5 changed files with 129 additions and 218 deletions
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19
GPy/core/model.py
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@ -8,7 +8,7 @@ import sys, pdb
import multiprocessing as mp
from GPy.util.misc import opt_wrapper
#import numdifftools as ndt
from parameterised import parameterised, truncate_pad
from parameterised import parameterised
import priors
from ..util.linalg import jitchol
from ..inference import optimization
@ -108,22 +108,15 @@ class model(parameterised):
return ret
def _transform_gradients(self, g):
"""
Takes a list of gradients and return an array of transformed gradients (positive/negative/tied/and so on)
"""
x = self._get_params()
g[self.constrained_positive_indices] = g[self.constrained_positive_indices]*x[self.constrained_positive_indices]
g[self.constrained_negative_indices] = g[self.constrained_negative_indices]*x[self.constrained_negative_indices]
[np.put(g,i,g[i]*(x[i]-l)*(h-x[i])/(h-l)) for i,l,h in zip(self.constrained_bounded_indices, self.constrained_bounded_lowers, self.constrained_bounded_uppers)]
[np.put(g,i,v) for i,v in [(t[0],np.sum(g[t])) for t in self.tied_indices]]
if len(self.tied_indices) or len(self.constrained_fixed_indices):
to_remove = np.hstack((self.constrained_fixed_indices+[t[1:] for t in self.tied_indices]))
for index,constraint in zip(self.constrained_indices, self.constraints):
g[index] = g[index] * constraint.gradfactor(x[index])
if len(self.tied_indices) or len(self.fixed_indices):
to_remove = np.hstack((self.fixed_indices+[t[1:] for t in self.tied_indices]))
return np.delete(g,to_remove)
else:
return g
def randomize(self):
"""
Randomize the model.
@ -207,7 +200,7 @@ class model(parameterised):
"""
Ensure that any variables which should clearly be positive have been constrained somehow.
"""
positive_strings = ['variance','lengthscale', 'precision']
positive_strings = ['variance','lengthscale', 'precision', 'kappa']
param_names = self._get_param_names()
currently_constrained = self.all_constrained_indices()
to_make_positive = []