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kern is now parameterized

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Max Zwiessele 2013-10-22 13:38:33 +01:00
parent fba121c826
commit e70203a1b2
1 changed files with 57 additions and 33 deletions
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90
GPy/kern/kern.py
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@ -10,6 +10,7 @@ import itertools
from parts.prod import Prod as prod
from matplotlib.transforms import offset_copy
import GPy
from GPy.core.parameterized import Parameterized_old, __fixed__
class kern(Parameterized):
def __init__(self, input_dim, parts=[], input_slices=None):
@ -56,7 +57,8 @@ class kern(Parameterized):
self._parameters_ = []
for p in self.parts:
self._parameters_.extend(p._parameters_)
Parameterized.__init__(self)
super(kern, self).__init__()
#Parameterized_old.__init__(self)
def parameters_changed(self):
[p.parameters_changed() for p in self.parts]
@ -165,13 +167,27 @@ class kern(Parameterized):
:param g: the gradient vector for the current model, usually created by dK_dtheta
"""
x = self._get_params()
[np.put(x, i, x * t.gradfactor(x[i])) for i, t in zip(self.constrained_indices, self.constraints)]
[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.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
[np.place(g, index, g[index] * constraint.gradfactor(x[index]))
for constraint, index in self._constraints_.iteritems() if constraint is not __fixed__]
# for constraint, index in self._constraints_.iteritems():
# if constraint != __fixed__:
# 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]]
[np.put(g, i, v) for i, v in [[i, t.sum()] for p in self._parameters_ for t,i in p._tied_to_me_.iteritems()]]
# 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:
if self._fixes_ is not None: return g[self._fixes_]
return g
# x = self._get_params()
# [np.put(x, i, x * t.gradfactor(x[i])) for i, t in zip(self.constrained_indices, self.constraints)]
# [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.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 compute_param_slices(self):
"""
@ -211,21 +227,27 @@ class kern(Parameterized):
newkern = kern(D, self.parts + other.parts, self_input_slices + other_input_slices)
# transfer constraints:
newkern.constrained_indices = self.constrained_indices + [x + self.num_params for x in other.constrained_indices]
newkern.constraints = self.constraints + other.constraints
newkern.fixed_indices = self.fixed_indices + [self.num_params + x for x in other.fixed_indices]
newkern.fixed_values = self.fixed_values + other.fixed_values
newkern.constraints = self.constraints + other.constraints
newkern.tied_indices = self.tied_indices + [self.num_params + x for x in other.tied_indices]
# newkern.constrained_indices = self.constrained_indices + [x + self.num_params for x in other.constrained_indices]
# newkern.constraints = self.constraints + other.constraints
# newkern.fixed_indices = self.fixed_indices + [self.num_params + x for x in other.fixed_indices]
# newkern.fixed_values = self.fixed_values + other.fixed_values
# newkern.constraints = self.constraints + other.constraints
# newkern.tied_indices = self.tied_indices + [self.num_params + x for x in other.tied_indices]
else:
assert self.input_dim == other.input_dim
newkern = kern(self.input_dim, self.parts + other.parts, self.input_slices + other.input_slices)
# transfer constraints:
newkern.constrained_indices = self.constrained_indices + [i + self.num_params for i in other.constrained_indices]
newkern.constraints = self.constraints + other.constraints
newkern.fixed_indices = self.fixed_indices + [self.num_params + x for x in other.fixed_indices]
newkern.fixed_values = self.fixed_values + other.fixed_values
newkern.tied_indices = self.tied_indices + [self.num_params + x for x in other.tied_indices]
# newkern.constrained_indices = self.constrained_indices + [i + self.num_params for i in other.constrained_indices]
# newkern.constraints = self.constraints + other.constraints
# newkern.fixed_indices = self.fixed_indices + [self.num_params + x for x in other.fixed_indices]
# newkern.fixed_values = self.fixed_values + other.fixed_values
# newkern.tied_indices = self.tied_indices + [self.num_params + x for x in other.tied_indices]
[newkern._add_constrain(param, transform, warning=False)
for param, transform in itertools.izip(
*itertools.chain(self._constraints_.iteritems(),
other._constraints_.iteritems()))]
newkern._fixes_ = ((self._fixes_ or 0) + (other._fixes_ or 0)) or None
return newkern
def __mul__(self, other):
@ -308,20 +330,22 @@ class kern(Parameterized):
for i, t in zip(prev_constr_ind, prev_constr):
self.constrain(np.where(index_param == i)[0], t)
# def _get_params(self):
# return np.hstack([p._get_params() for p in self.parts])
#
# def _set_params(self, x):
# [p._set_params(x[s]) for p, s in zip(self.parts, self.param_slices)]
#
# def _get_param_names(self):
# # this is a bit nasty: we want to distinguish between parts with the same name by appending a count
# part_names = np.array([k.name for k in self.parts], dtype=np.str)
# counts = [np.sum(part_names == ni) for i, ni in enumerate(part_names)]
# cum_counts = [np.sum(part_names[i:] == ni) for i, ni in enumerate(part_names)]
# names = [name + '_' + str(cum_count) if count > 1 else name for name, count, cum_count in zip(part_names, counts, cum_counts)]
#
# return sum([[name + '_' + n for n in k._get_param_names()] for name, k in zip(names, self.parts)], [])
def _get_params(self):
return np.hstack(self._parameters_)
return np.hstack([p._get_params() for p in self.parts])
def _set_params(self, x):
import ipdb;ipdb.set_trace()
[p._set_params(x[s]) for p, s in zip(self.parts, self.param_slices)]
def _get_param_names(self):
# this is a bit nasty: we want to distinguish between parts with the same name by appending a count
part_names = np.array([k.name for k in self.parts], dtype=np.str)
counts = [np.sum(part_names == ni) for i, ni in enumerate(part_names)]
cum_counts = [np.sum(part_names[i:] == ni) for i, ni in enumerate(part_names)]
names = [name + '_' + str(cum_count) if count > 1 else name for name, count, cum_count in zip(part_names, counts, cum_counts)]
return sum([[name + '_' + n for n in k._get_param_names()] for name, k in zip(names, self.parts)], [])
def K(self, X, X2=None, which_parts='all'):
"""