def crossvalidate_delta(self, folds):
import utils
cv_scheme = utils.crossValidationScheme(folds, self.nTrain)
ldeltas = SP.arange(-3, -1.5, .01)
Ss = []
Us = []
Uys = []
UCs = []
err = 0.0
errs = []
for ldelta in ldeltas:
for test_set in cv_scheme:
train_set = ~test_set
K_sub = self.kernel[SP.ix_(train_set, train_set)]
K_cross = self.kernel[SP.ix_(~train_set, train_set)]
# print LA.inv((K_sub + SP.eye(train_set.sum())*self.delta))
Core = SP.dot(
K_cross,
LA.inv((K_sub + SP.eye(train_set.sum()) * SP.exp(ldelta))))
diff = self.yTrain[test_set] -\
SP.dot(Core, self.yTrain[train_set])
err += (diff**2).sum() / diff.size
S, U = LA.eigh(self.kernel[SP.ix_(train_set, train_set)])
Ss.append(S)
Us.append(U)
Uys.append(SP.dot(U.T, self.yTrain[train_set]))
UCs.append(SP.dot(U.T, SP.ones_like(self.yTrain[train_set])))
errs.append(err / len(cv_scheme))
err = 0.0
nll_scores = []
for ldelta in ldeltas:
# print 'ldelta equals', ldelta
score = 0.0
for i in xrange(len(cv_scheme)):
score += lmm_fast.nLLeval(ldelta, (Uys[i])[:, 0], UCs[i],
Ss[i])
nll_scores.append(score / len(cv_scheme))
print 'best ldelta found ll', ldeltas[SP.argmin(nll_scores)]
return ldeltas[SP.argmin(errs)]
def crossvalidate_delta(self, folds):
import utils
cv_scheme = utils.crossValidationScheme(folds, self.nTrain)
ldeltas = SP.arange(-3, -1.5, .01)
Ss = []
Us = []
Uys = []
UCs = []
err = 0.0
errs = []
for ldelta in ldeltas:
for test_set in cv_scheme:
train_set = ~test_set
K_sub = self.kernel[SP.ix_(train_set, train_set)]
K_cross = self.kernel[SP.ix_(~train_set, train_set)]
# print LA.inv((K_sub + SP.eye(train_set.sum())*self.delta))
Core = SP.dot(K_cross, LA.inv((K_sub + SP.eye(train_set.sum()) *
SP.exp(ldelta))))
diff = self.yTrain[test_set] -\
SP.dot(Core, self.yTrain[train_set])
err += (diff**2).sum()/diff.size
S, U = LA.eigh(self.kernel[SP.ix_(train_set, train_set)])
Ss.append(S)
Us.append(U)
Uys.append(SP.dot(U.T, self.yTrain[train_set]))
UCs.append(SP.dot(U.T, SP.ones_like(self.yTrain[train_set])))
errs.append(err/len(cv_scheme))
err = 0.0
nll_scores = []
for ldelta in ldeltas:
# print 'ldelta equals', ldelta
score = 0.0
for i in xrange(len(cv_scheme)):
score += lmm_fast.nLLeval(ldelta, (Uys[i])[:, 0], UCs[i], Ss[i])
nll_scores.append(score/len(cv_scheme))
print 'best ldelta found ll', ldeltas[SP.argmin(nll_scores)]
return ldeltas[SP.argmin(errs)]