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changing all parameterized objects to be compatible with the new parameterization

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Max Zwiessele 2013-10-25 15:29:04 +01:00
parent e1bee4536a
commit d3721b76a8
21 changed files with 645 additions and 529 deletions
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46
GPy/core/model.py
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@ -12,6 +12,7 @@ import numpy as np
from domains import _POSITIVE, _REAL
from numpy.linalg.linalg import LinAlgError
from index_operations import ParameterIndexOperations
import itertools
# import numdifftools as ndt
class Model(Parameterized):
@ -28,8 +29,15 @@ class Model(Parameterized):
def log_likelihood(self):
raise NotImplementedError, "this needs to be implemented to use the model class"
def _log_likelihood_gradients(self):
#def dK_d(self, param, dL_dK, X, X2)
g = np.zeros(self.size)
try:
[g.__setitem__(s, self.gradient_mapping[p]().flat) for p, s in itertools.izip(self._parameters_, self._param_slices_) if not p.is_fixed]
except KeyError:
raise KeyError, 'Gradient for {} not defined, please specify gradients for parameters to optimize'.format(p.name)
return g
raise NotImplementedError, "this needs to be implemented to use the model class"
def getstate(self):
"""
Get the current state of the class.
@ -153,19 +161,19 @@ class Model(Parameterized):
return ret
def _transform_gradients(self, g):
x = self._get_params()
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
# def _transform_gradients(self, g):
# x = self._get_params()
# 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.flattened_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
def randomize(self):
"""
@ -173,15 +181,15 @@ class Model(Parameterized):
Make this draw from the prior if one exists, else draw from N(0,1)
"""
# first take care of all parameters (from N(0,1))
x = self._get_params_transformed()
x = np.random.randn(x.size)
#x = self._get_params_transformed()
x = np.random.randn(self.size_transformed)
self._set_params_transformed(x)
# now draw from prior where possible
x = self._get_params()
if self.priors is not None:
[np.put(x, i, p.rvs(1)) for i, p in enumerate(self.priors) if not p is None]
self._set_params(x)
self._set_params_transformed(self._get_params_transformed()) # makes sure all of the tied parameters get the same init (since there's only one prior object...)
#self._set_params_transformed(self._get_params_transformed()) # makes sure all of the tied parameters get the same init (since there's only one prior object...)
def optimize_restarts(self, num_restarts=10, robust=False, verbose=True, parallel=False, num_processes=None, **kwargs):
@ -480,7 +488,7 @@ class Model(Parameterized):
names = self._get_param_names_transformed()
except NotImplementedError:
names = ['Variable %i' % i for i in range(len(x))]
import ipdb;ipdb.set_trace()
# Prepare for pretty-printing
header = ['Name', 'Ratio', 'Difference', 'Analytical', 'Numerical']
max_names = max([len(names[i]) for i in range(len(names))] + [len(header[0])])
@ -522,7 +530,7 @@ class Model(Parameterized):
d = '%.6f' % float(difference)
g = '%.6f' % gradient
ng = '%.6f' % float(numerical_gradient)
grad_string = "{0:^{c0}}|{1:^{c1}}|{2:^{c2}}|{3:^{c3}}|{4:^{c4}}".format(formatted_name, r, d, g, ng, c0=cols[0] + 9, c1=cols[1], c2=cols[2], c3=cols[3], c4=cols[4])
grad_string = "{0:<{c0}}|{1:^{c1}}|{2:^{c2}}|{3:^{c3}}|{4:^{c4}}".format(formatted_name, r, d, g, ng, c0=cols[0] + 9, c1=cols[1], c2=cols[2], c3=cols[3], c4=cols[4])
print grad_string
def input_sensitivity(self):