def build_vector_based_pm(self):
# samples: 40 samples in 20d space (40x20; samples x features)
self.samples = np.random.rand(40,20)
# initial prototypes are samples itself:
self.prototypes = self.samples.copy()
# using just two similarities for now:
self.similarities = [ExponentialKernel(), SquaredExponentialKernel()]
# set up prototype mapper with prototypes identical to samples.
self.pm = PrototypeMapper(similarities=self.similarities,
prototypes=self.prototypes)
# train Prototype
self.pm.train(self.samples)
def __init__(self, kernel=None, **kwargs):
"""Initialize a GPR regression analysis.
Parameters
----------
kernel : Kernel
a kernel object defining the covariance between instances.
(Defaults to SquaredExponentialKernel if None in arguments)
"""
# init base class first
Classifier.__init__(self, **kwargs)
# It does not make sense to calculate a confusion matrix for a GPR
# XXX it does ;) it will be a RegressionStatistics actually ;-)
# So if someone desires -- let him have it
# self.ca.enable('training_stats', False)
# set kernel:
if kernel is None:
kernel = SquaredExponentialKernel()
debug(
"GPR",
"No kernel was provided, falling back to default: %s" % kernel)
self.__kernel = kernel
# append proper clf_internal depending on the kernel
# TODO: add "__tags__" to kernels since the check
# below does not scale
if isinstance(kernel, GeneralizedLinearKernel) or \
isinstance(kernel, LinearKernel):
self.__tags__ += ['linear']
else:
self.__tags__ += ['non-linear']
if externals.exists('openopt') \
and not 'has_sensitivity' in self.__tags__:
self.__tags__ += ['has_sensitivity']
# No need to initialize conditional attributes. Unless they got set
# they would raise an exception self.predicted_variances =
# None self.log_marginal_likelihood = None
self._init_internals()
pass
clfswh += GNB(common_variance=True, descr="GNB(common_variance=True)")
clfswh += GNB(prior='uniform', descr="GNB(prior='uniform')")
clfswh += \
FeatureSelectionClassifier(
GNB(),
SensitivityBasedFeatureSelection(
OneWayAnova(),
FractionTailSelector(0.05, mode='select', tail='upper')),
descr="GNB on 5%(ANOVA)")
# GPR
if externals.exists('scipy'):
from mvpa2.clfs.gpr import GPR
regrswh += GPR(kernel=LinearKernel(), descr="GPR(kernel='linear')")
regrswh += GPR(kernel=SquaredExponentialKernel(),
descr="GPR(kernel='sqexp')")
# Add wrapped GPR as a classifier
gprcb = RegressionAsClassifier(GPR(kernel=GeneralizedLinearKernel()),
descr="GPRC(kernel='linear')")
# lets remove multiclass label from it
gprcb.__tags__.pop(gprcb.__tags__.index('multiclass'))
clfswh += gprcb
# and create a proper multiclass one
clfswh += MulticlassClassifier(RegressionAsClassifier(
GPR(kernel=GeneralizedLinearKernel())),
descr="GPRCM(kernel='linear')")
# BLR