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Merge branch 'devel' of https://github.com/SheffieldML/GPy into devel

Browse source 
Conflicts:
	GPy/examples/dimensionality_reduction.py
This commit is contained in:
Neil Lawrence 2013-05-05 08:01:47 +01:00
commit 7ffcefc511
20 changed files with 981 additions and 409 deletions
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37
GPy/core/model.py
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@ -6,7 +6,7 @@ from .. import likelihoods
from ..inference import optimization
from ..util.linalg import jitchol
from GPy.util.misc import opt_wrapper
from parameterised import parameterised, truncate_pad
from parameterised import parameterised
from scipy import optimize
import multiprocessing as mp
import numpy as np
@ -67,9 +67,14 @@ class model(parameterised):
# check constraints are okay
if isinstance(what, (priors.gamma, priors.log_Gaussian)):
assert not np.any(which[:, None] == self.constrained_negative_indices), "constraint and prior incompatible"
assert not np.any(which[:, None] == self.constrained_bounded_indices), "constraint and prior incompatible"
unconst = np.setdiff1d(which, self.constrained_positive_indices)
constrained_positive_indices = [i for i, t in zip(self.constrained_indices, self.constraints) if t.domain == 'positive']
if len(constrained_positive_indices):
constrained_positive_indices = np.hstack(constrained_positive_indices)
else:
constrained_positive_indices = np.zeros(shape=(0,))
bad_constraints = np.setdiff1d(self.all_constrained_indices(), constrained_positive_indices)
assert not np.any(which[:, None] == bad_constraints), "constraint and prior incompatible"
unconst = np.setdiff1d(which, constrained_positive_indices)
if len(unconst):
print "Warning: constraining parameters to be positive:"
print '\n'.join([n for i, n in enumerate(self._get_param_names()) if i in unconst])
@ -80,7 +85,6 @@ class model(parameterised):
else:
raise ValueError, "prior not recognised"
# store the prior in a local list
for w in which:
self.priors[w] = what
@ -110,22 +114,16 @@ 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)]
for index, constraint in zip(self.constrained_indices, self.constraints):
g[index] = g[index] * constraint.gradfactor(x[index])
[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]))
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.
@ -209,7 +207,7 @@ 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 = []
@ -361,10 +359,7 @@ class model(parameterised):
numerical_gradient = (f1 - f2) / (2 * dx)
global_ratio = (f1 - f2) / (2 * np.dot(dx, gradient))
if (np.abs(1. - global_ratio) < tolerance) and not np.isnan(global_ratio):
return True
else:
return False
return (np.abs(1. - global_ratio) < tolerance) or (np.abs(gradient - numerical_gradient).mean() - 1) < tolerance
else:
# check the gradient of each parameter individually, and do some pretty printing
try:
@ -401,7 +396,7 @@ class model(parameterised):
ratio = (f1 - f2) / (2 * step * gradient)
difference = np.abs((f1 - f2) / 2 / step - gradient)
if (np.abs(ratio - 1) < tolerance):
if (np.abs(1. - ratio) < tolerance) or np.abs(difference) < tolerance:
formatted_name = "\033[92m {0} \033[0m".format(names[i])
else:
formatted_name = "\033[91m {0} \033[0m".format(names[i])