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finally added pca package again

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This commit is contained in:
Max Zwiessele 2014-03-24 11:16:57 +00:00
parent ed2aaab4bb
commit 321a75100c
5 changed files with 134 additions and 33 deletions
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7
GPy/models/gplvm.py
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@ -5,7 +5,6 @@
import numpy as np
import pylab as pb
from .. import kern
from ..util.linalg import PCA
from ..core import GP, Param
from ..likelihoods import Gaussian
from .. import util
@ -29,9 +28,11 @@ class GPLVM(GP):
"""
if X is None:
from ..util.initialization import initialize_latent
X = initialize_latent(init, input_dim, Y)
X, fracs = initialize_latent(init, input_dim, Y)
else:
fracs = np.ones(input_dim)
if kernel is None:
kernel = kern.RBF(input_dim, ARD=input_dim > 1) + kern.Bias(input_dim, np.exp(-2))
kernel = kern.RBF(input_dim, lengthscale=fracs, ARD=input_dim > 1) + kern.Bias(input_dim, np.exp(-2))
likelihood = Gaussian()

8
GPy/models/mrd.py
View file

@ -6,12 +6,12 @@ import itertools
import pylab
from ..core import Model
from ..util.linalg import PCA
from ..kern import Kern
from ..core.parameterization.variational import NormalPosterior, NormalPrior
from ..core.parameterization import Param, Parameterized
from ..inference.latent_function_inference.var_dtc import VarDTCMissingData, VarDTC
from ..likelihoods import Gaussian
from GPy.util.initialization import initialize_latent
class MRD(Model):
"""
@ -71,7 +71,7 @@ class MRD(Model):
self.num_inducing = self.Z.shape[0] # ensure M==N if M>N
if X_variance is None:
X_variance = np.random.uniform(0, .2, X.shape)
X_variance = np.random.uniform(0, .1, X.shape)
self.variational_prior = NormalPrior()
self.X = NormalPosterior(X, X_variance)
@ -147,11 +147,11 @@ class MRD(Model):
if Ylist is None:
Ylist = self.Ylist
if init in "PCA_concat":
X = PCA(np.hstack(Ylist), self.input_dim)[0]
X = initialize_latent('PCA', np.hstack(Ylist), self.input_dim)
elif init in "PCA_single":
X = np.zeros((Ylist[0].shape[0], self.input_dim))
for qs, Y in itertools.izip(np.array_split(np.arange(self.input_dim), len(Ylist)), Ylist):
X[:, qs] = PCA(Y, len(qs))[0]
X[:, qs] = initialize_latent('PCA', Y, len(qs))
else: # init == 'random':
X = np.random.randn(Ylist[0].shape[0], self.input_dim)
return X