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REFACTORING: model names, lowercase, classes uppercase

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Max Zwiessele 2013-06-05 13:02:03 +01:00
parent 2a39440619
commit 2e5e8ac026
50 changed files with 436 additions and 3307 deletions
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29
GPy/models/mrd.py
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@ -4,14 +4,13 @@ Created on 10 Apr 2013
@author: Max Zwiessele
'''
from GPy.core import model
from GPy.models.Bayesian_GPLVM import Bayesian_GPLVM
from GPy.core import sparse_GP
from GPy.core import SparseGP
from GPy.util.linalg import PCA
from scipy import linalg
import numpy
import itertools
import pylab
from GPy.kern.kern import kern
from GPy.models.bayesian_gplvm import BayesianGPLVM
class MRD(model):
"""
@ -38,7 +37,7 @@ class MRD(model):
*concat: PCA on concatenated outputs
*single: PCA on each output
*random: random
:param M:
:param num_inducing:
number of inducing inputs to use
:param Z:
initial inducing inputs
@ -62,22 +61,22 @@ class MRD(model):
assert not ('kernel' in kw), "pass kernels through `kernels` argument"
self.input_dim = input_dim
self.M = M
self.num_inducing = M
self._debug = _debug
self._init = True
X = self._init_X(initx, likelihood_or_Y_list)
Z = self._init_Z(initz, X)
self.bgplvms = [Bayesian_GPLVM(l, input_dim=input_dim, kernel=k, X=X, Z=Z, M=self.M, **kw) for l, k in zip(likelihood_or_Y_list, kernels)]
self.bgplvms = [BayesianGPLVM(l, input_dim=input_dim, kernel=k, X=X, Z=Z, M=self.num_inducing, **kw) for l, k in zip(likelihood_or_Y_list, kernels)]
del self._init
self.gref = self.bgplvms[0]
nparams = numpy.array([0] + [sparse_GP._get_params(g).size - g.Z.size for g in self.bgplvms])
nparams = numpy.array([0] + [SparseGP._get_params(g).size - g.Z.size for g in self.bgplvms])
self.nparams = nparams.cumsum()
self.N = self.gref.N
self.NQ = self.N * self.input_dim
self.MQ = self.M * self.input_dim
self.MQ = self.num_inducing * self.input_dim
model.__init__(self) # @UndefinedVariable
self._set_params(self._get_params())
@ -151,7 +150,7 @@ class MRD(model):
itertools.izip(ns,
itertools.repeat(name)))
return list(itertools.chain(n1var, *(map_names(\
sparse_GP._get_param_names(g)[self.MQ:], n) \
SparseGP._get_param_names(g)[self.MQ:], n) \
for g, n in zip(self.bgplvms, self.names))))
def _get_params(self):
@ -165,14 +164,14 @@ class MRD(model):
X = self.gref.X.ravel()
X_var = self.gref.X_variance.ravel()
Z = self.gref.Z.ravel()
thetas = [sparse_GP._get_params(g)[g.Z.size:] for g in self.bgplvms]
thetas = [SparseGP._get_params(g)[g.Z.size:] for g in self.bgplvms]
params = numpy.hstack([X, X_var, Z, numpy.hstack(thetas)])
return params
# def _set_var_params(self, g, X, X_var, Z):
# g.X = X.reshape(self.N, self.input_dim)
# g.X_variance = X_var.reshape(self.N, self.input_dim)
# g.Z = Z.reshape(self.M, self.input_dim)
# g.Z = Z.reshape(self.num_inducing, self.input_dim)
#
# def _set_kern_params(self, g, p):
# g.kern._set_params(p[:g.kern.Nparam])
@ -206,7 +205,7 @@ class MRD(model):
def log_likelihood(self):
ll = -self.gref.KL_divergence()
for g in self.bgplvms:
ll += sparse_GP.log_likelihood(g)
ll += SparseGP.log_likelihood(g)
return ll
def _log_likelihood_gradients(self):
@ -215,7 +214,7 @@ class MRD(model):
dLdmu -= dKLmu
dLdS -= dKLdS
dLdmuS = numpy.hstack((dLdmu.flatten(), dLdS.flatten())).flatten()
dldzt1 = reduce(lambda a, b: a + b, (sparse_GP._log_likelihood_gradients(g)[:self.MQ] for g in self.bgplvms))
dldzt1 = reduce(lambda a, b: a + b, (SparseGP._log_likelihood_gradients(g)[:self.MQ] for g in self.bgplvms))
return numpy.hstack((dLdmuS,
dldzt1,
@ -250,9 +249,9 @@ class MRD(model):
if X is None:
X = self.X
if init in "permute":
Z = numpy.random.permutation(X.copy())[:self.M]
Z = numpy.random.permutation(X.copy())[:self.num_inducing]
elif init in "random":
Z = numpy.random.randn(self.M, self.input_dim) * X.var()
Z = numpy.random.randn(self.num_inducing, self.input_dim) * X.var()
self.Z = Z
return Z